Media Coverage and Stock Returns on the London Stock Exchange, 1825–70

Media Coverage and Stock Returns on the London Stock Exchange, 1825–70 Abstract News media plays an important role in modern financial markets. In this article, we analyze the role played by the news media in an historical financial market. Using The Times’s coverage of companies listed on the London stock market between 1825 and 1870, we examine the determinants of media coverage in this era and whether media coverage affected returns. Our main finding is that a media effect mainly manifests itself after the mid-1840s and that the introduction of arm’s-length ownership along with markedly increased market participation was the main reason for the emergence of this media effect. 1. Introduction The UK capital market underwent a major transformation in the nineteenth century, with large capital-intensive companies raising funds on the equity market from multiple arm’s-length investors. This revolution resulted in a major increase in the number and value of companies listed, an increase in the number of stock exchanges operating outside London, and a substantial increase in the proportion of the UK’s population investing in equities (Thomas, 1973; Michie, 1999, pp. 88–89; Grossman, 2002; Acheson et al., 2009; Rutterford et al., 2011). This expansion of the equity market was accompanied by an increase in the demand for corporate information, which was partially met by companies through annual shareholder meetings and annual reports. In addition, the financial press emerged during the nineteenth century to meet the demand from arm’s-length investors for independent corporate news (Preda, 2001; Taylor, 2012). In this article, we analyze the press coverage of companies listed on the London stock market in the nineteenth century. In particular, we examine the coverage by The Times, the leading newspaper of the day, of companies listed between 1825 and 1870. First, we look at the determinants of press coverage to understand better what characteristics were associated with a greater probability of being covered by The Times. For example, we examine whether companies which advertised in The Times in one year were more likely to be covered in it the following year. Secondly, using monthly stock data collected from the London Stock Exchange’s (LSE’s) official price list, we test whether media coverage affected stock returns. The media may affect returns because companies not covered in the press need to pay a premium in the form of higher expected returns because they lack recognition among investors (Merton, 1987; Fang and Peress, 2009) or because there are impediments-to-trade such as liquidity constraints or higher transaction costs which prevent traders from exploiting mispriced securities (Miller, 1977). This article is the first to examine the effect of the media on asset prices before and after the period when arm’s-length and diffuse ownership emerged. We hypothesize that the media had no influence in the first half of our sample period because ownership was concentrated in the hands of a small number of shareholders, who would have had access to corporate information via local networks or through involvement in corporate governance. However, in the second half of our sample period, we hypothesize that media influenced relative returns between media and no-media stocks because they provided arm’s-length investors with additional and valuable information for covered companies. Consequently, companies that were not covered by the media and which had diffuse share ownership had to offer a higher return than companies covered by the media due to a lack of information about the company. An additional motivation for this article is that it tests the relationship between media and finance in an environment where there were few other substitutes or confounding information providers such as analysts, city circulars, 24-hour television, or internet news sources.1 In addition, equity investors at this time were individuals rather than well-informed institutional investors (Anderson and Cottrell 1975; Cheffins, 2008, p. 190; Turner 2009; Campbell and Turner, 2012). This makes the nineteenth-century stock market a unique and relatively noiseless environment in which to test the media–finance hypothesis. A further feature of this era which makes this article interesting is that, unlike with modern newspapers, advertisements were exclusively text based, making it easier to gather information on whether companies which were reported on in the newspaper had previously placed advertisements with the paper. We are not testing whether companies “paid” for coverage or whether journalistic integrity was compromised—we simply wish to see if the two things are correlated, which might suggest some implicit and unspoken arrangement. Indeed, pressure for The Times to acquiesce to such arrangements may have grown during the century because of increased competition from aggressive parvenus in the newspaper market. We find that media coverage was broadly comparable to modern markets, with The Times covering 57% of our sample companies. Notably, the proportion of companies covered in a particular year increases in a nonlinear fashion over time and the average number of articles written conditional on coverage also increased nonlinearly over time. In terms of media coverage, the number of issued shares, company size, and industry are all important determinants, suggesting that larger, widely held companies were more likely to be covered by the press in the nineteenth century. Interestingly, we also find that placing ten advertisements in The Times is associated with an additional article for that company in the subsequent year. With regards to the media effect, when we examined the whole sample period and the first half of the sample period, the evidence that returns vary with media coverage is rather weak. However, in the second half of our sample period, we find that companies without coverage have statistically significant higher returns, even after adjusting for market and company-specific risk factors. This is consistent with our hypothesis that a media effect would only manifest itself after the mid-1840s, when participation in the stock market increased dramatically and when arm’s-length ownership began to emerge. In an attempt to corroborate this hypothesis, we test whether stocks with greater participation and arm’s-length ownership experience a larger media effect. Using the number of shares issued as a proxy for participation and arm’s-length ownership, we find some evidence supporting our hypothesis. We also discover that in times when the level of ownership diffusion is greater for media relative to no-media stocks, the media effect is larger. This is consistent with our hypothesis that ownership diffusion is closely associated with the media effect. In terms of the mechanism, our evidence suggests that no-media stocks have to pay a return premium because they lack investor recognition and not because of impediments to trade, which supports Merton (1987) rather than Miller (1977). This study contributes to the growing literature which examines the relationship between media and financial markets. One branch of this literature looks at whether sentiment, as measured by the optimism/pessimism of newspaper reporting, affects asset prices (Tetlock, 2007; Tetlock, Saar-Tsechansky, and Macskassy, 2008; Engelberg, Reed, and Ringgenber, 2012; García, 2013; Soo, 2013; Walker, 2014; Manela and Moreira, 2015). The other branch of this literature focuses on the informational role played by news media and therefore concentrates on the extent of coverage (Fang and Peress, 2009; Cumming et al., 2016; Ferguson et al., 2015). Our article is most closely related to this second branch of the literature and our findings are similar to the seminal contribution of Fang and Peress (2009), who identify a media effect on NYSE and NASDAQ stocks in the period 1993–2002. However, our unique contribution is that we identify that the media effect only emerges when stock ownership becomes diffuse and arm’s-length. Our article also augments the literature which looks at the informational role of media in historical contexts such as its effect on public health (Costa and Kahn, 2015), financial “bubbles” (Bhattacharya et al., 2009; Campbell, Turner, and Walker, 2012), and corporate scandals (Taylor, 2012). We augment these papers by looking at the informational role of media on the equity market for the middle two quarters of the nineteenth century. Bignon and Miscio (2010) examine the effect of payments made by French newspapers both directly through advertising and indirectly through investment banks placing laudatory articles in a newspaper’s editorial section (so-called réclames) on media coverage. They find that companies paying for coverage were more likely to be covered. We also find that companies that advertised in The Times were more likely to be covered. The next section examines the institutional and historical setting of the study and develops our hypotheses. Section 3 describes our media and stock price data. Section 4 examines the determinants of media coverage. Section 5 asks whether there was a media effect in this historical market and tests the impediments-to-trade and investor-recognition hypotheses regarding the mechanism by which media affects returns. Section 6 tests our explanation as to why we only find a media effect in the second half of our sample period. Section 7 is a brief summary and conclusion. 2. The Development of the Equity Market and the Financial Press The UK equity market grew substantially during the middle two quarters of the nineteenth century. In terms of issues, the market grew by circa 40% between 1825 and 1870, but in terms of market capitalization to GDP, it trebled in size over this same time period (Acheson et al., 2009, pp. 1115–1117). This growth was driven on the demand side by a growing number of middle-class investors looking for returns in excess of those provided by government bonds (Jefferys, 1977). On the supply side, it was stimulated by the liberalization of UK incorporation law (Shannon, 1933; Cottrell, 1980; Taylor, 2006) and the rise of capital-intensive infrastructure projects such as railways, gas-light and coke companies, waterworks, and telegraph companies (Acheson et al., 2009). There was a marked change in the ownership of public companies before and after the 1840s. Prior to the 1840s, shareholder numbers in most companies were in the low hundreds and shareholders typically lived close to the company they were investing in (Acheson et al., 2015). For example, canals, which were the largest companies in terms of market capitalization in the pre-1840 era, fitted this characterization (Ward, 1974). From the mid-1840s onwards, there was a notable change in ownership of public companies which accompanied their growth in scale and their national, rather than regional, reach. This was true for the railway industry, which experienced a substantial surge in growth in the mid-1840s. For example, a Parliamentary survey of railway shareholders in 1855 found that there were 166,125 railway shareholdings (Parliamentary Papers, 1856). Three railway companies had in excess of 10,000 shareholders, a further three had 5,000 shareholders or more, and a further ten had more than 2,000 shareholders (Parliamentary Papers, 1856). But the growth in shareholder numbers was not just limited to railways and new sectors. Banks grew in size and with it their shareholder bases. For example, the number of UK bank shareholdings grew from 23,941 in 1844 to 40,583 in 1869. Only one bank had more than 1,000 shareholders in 1844, yet by 1869, twelve banks had in excess of 1,000 shareholders.2 Consequently, from the mid-1840s onwards, the UK experienced the rise of the arm’s-length and diffuse corporate ownership, which developed further in the last quarter of the nineteenth century, with the majority of large public companies at the beginning of the twentieth century being characterized by this type of ownership (Foreman-Peck and Hannah, 2012). The rise of this type of ownership from the mid-1840s onwards resulted in a change in investors’ access to company information. When ownership was geographically concentrated and there were a low number of shareholders, information on performance was relatively easy to obtain via direct participation in governance, local knowledge, and social networks which contained company directors. However, the rise of dispersed and arm’s-length ownership implied that investors required alternative information sources on company performance. Given that institutional investors did not participate in the equity market in this era and that there was no analyst coverage of stocks, investors could not rely on information being collected, analyzed, and disseminated by these sources. In addition, UK public companies did not face formal reporting requirements until the late-1860s (Watts and Zimmerman, 1983; Baskin and Miranti, 1997, p. 185) and it was not until the early twentieth century that companies listed on the LSE were required to distribute their annual financial accounts to shareholders (Cheffins, 2008, p. 95). Into this information lacuna stepped the press. The Times had covered financial and money markets from well before the 1840s, but the 1840s marked the beginning of widespread press coverage of equity markets by the news media (Preda, 2001; Taylor, 2012). This coverage came in two forms—expanded coverage by newspapers like The Times and the rise of weekly railway periodicals, for example, Railway Times (est. 1837), Herepath’s Railway Journal (est. 1835), and the Railway Record (est. 1844). These periodicals carried share price tables, editorial commentary, company financial reports, reports of company AGMs, and advertisements from railway promoters. The credibility of the information provider is also something which matters for investors. For example, Dyck and Zingales (2003) in their study highlight that the effect of media is more pronounced the more credible is the news source. The railway press was far from impartial—it acted as a cheerleader for railway companies and talked up railway shares during the Railway Mania promotional boom of the mid-1840s, partially due to the large advertising revenue generated for it by railway companies and promoters (Kostal, 1994, p. 37; Campbell, Turner, and Walker, 2012). The Times, on the other hand, was a credible source which was perceived to be independent of the companies it was reporting on. For example, it was extremely critical of speculation in railway shares and it published a highly critical exposé a few weeks before the railway “bubble” crashed (Tuck, 1846; Simmons, 1978, p. 40; Campbell, Turner, and Walker, 2012). In terms of our hypotheses, the context described above suggests that we should expect less press coverage of the equity market before the large expansion of arm’s-length ownership in the 1840s. In addition, due to concentrated ownership, most of the investors were aware and well informed about the companies that they were investing in, there should have been no informational advantage to investors of press reporting on companies and there should have been no effect on the shares of companies which were covered by the press. In contrast, after the expansion of arm’s-length ownership, we would expect greater coverage of companies because newspaper readers who were investing in equities valued this. We also hypothesize that by increasing the information available to investors, the press helped to diminish the problem of “investor recognition” for covered stocks, resulting in lower returns for such stocks (Fang and Peress, 2009). In terms of advertising, we hypothesize that companies which advertised in The Times were more likely to be subsequently covered by the newspaper. This could have occurred for several reasons. First, companies which advertise are more likely to have greater public recognition and therefore be covered by the press. Second, the placing of adverts may have simply brought companies to the attention of The Times’s reporters. Third, as with the case of the French press discussed by Bignon and Miscio (2010), companies may have been indirectly paying for coverage. However, this is not to suggest that this revenue stream influenced the content of reports in The Times. Indeed, it demonstrated this in 1845 when it issued several highly critical reports concerning the railway sector, which was a major source of advertising revenue for the paper. 3. Stock and Media Data Our stock data was obtained from the Course of the Exchange (COE), a bi-weekly list which was regarded as the official price list for the LSE. For listed securities, the COE reported dividends, number of issued shares, nominal and paid-up values of stock, and stock prices. The stock prices reported in the official list are usually the transaction prices from the previous day (Ye and Turner, 2014). We use Ye and Turner’s (2014) hand-collected data for each common stock listed in the COE for every month between March 1825 and December 1870. Our dataset contains 102,408 observations, consisting of stocks issued by 580 companies. Panel A of Table I contains the summary statistics for our dataset, which reveals that stocks in this era had high denominations and that the mean market capitalization was £650,000. Our data comprises stocks from thirteen industries, including banks, bridges, mining, canals, docks, gas-light and coke, insurance, roads, railways, telegraphs, waterworks, and miscellaneous industrial and commercial companies. If media coverage is biased toward particular industries, this will be identified in subsequent analysis. In addition, to account for this possibility, industry controls are used when assessing the impact of media coverage on stock returns. Table I Company descriptive statistics by media coverage, LSE, 1825–70 The definition of media coverage in this table includes advertisements in The Times as well as reporting on companies. The number of observations differs slightly across variables because of missing data: number of shares, paid-up capital per share, stock price, market capitalization, paid capital and dividend yield are all based on 102,408 observations, while nominal value per share is based on 98,712 observations. No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 Table I Company descriptive statistics by media coverage, LSE, 1825–70 The definition of media coverage in this table includes advertisements in The Times as well as reporting on companies. The number of observations differs slightly across variables because of missing data: number of shares, paid-up capital per share, stock price, market capitalization, paid capital and dividend yield are all based on 102,408 observations, while nominal value per share is based on 98,712 observations. No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 From Panels B and C of Table I, we see that there were notable differences in the characteristics of stocks that did and did not appear in The Times. First, media stocks issued a far greater number of shares: the mean number of shares issued by companies covered by the media being 32,620, which was nearly twice that issued by companies not covered by the media. Second, the average size of media stock was much greater than no-media stock, with mean market capitalization of £1.42 million and £0.46 million, respectively. Both of these differences are consistent with our hypothesis that the media played an important information role for investors in companies which had arm’s-length and diffuse ownership. Figure 1 demonstrates the step change that took place in the UK equity market in the mid-1840s. Prior to the mid-1840s, there had not been much growth in market capitalization or in the number of issued shares on the market. This changed after the mid-1840s, largely due to the arrival of large companies such as the railways rather than a substantial increase in the number of companies on the market (Campbell, 2012). The average number of shares issued by public companies increased from 9,502 to 27,568 after 1845 and the issuance of a greater number of shares was coupled with a decrease in the nominal value of shares by 20.2%. These changes capture an important market development—a significant increase in the number of shares available at lower denominations suggests that shares were held by a greater numbers of investors. The mid-1840s, therefore, marks the watershed moment in the development of diffuse and arm’s-length corporate ownership in the UK (Acheson et al., 2015). Figure 1 View largeDownload slide Total number of issued shares and market capitalization, LSE, 1826–70. Based on Ye and Turner’s (2014) hand-collected data for each common stock listed in the COE for every month between March 1825 and December 1870. The dataset contains 102,408 observations, consisting of stocks issued by 580 companies. Figure 1 View largeDownload slide Total number of issued shares and market capitalization, LSE, 1826–70. Based on Ye and Turner’s (2014) hand-collected data for each common stock listed in the COE for every month between March 1825 and December 1870. The dataset contains 102,408 observations, consisting of stocks issued by 580 companies. Our media data are sourced from The Times via The Times Digital Archive (TDA). The Times was by far the most significant newspaper in terms of influence during our sample period (Brown, 1985, pp. 27–29, 50; Simmons, 1991; Campbell, Turner, and Walker, 2012). It also had by far the widest circulation of any daily UK newspaper, with three to four times the circulation of its nearest rival (Parliamentary Papers, 1852; Campbell, Turner, and Walker, 2012, p. 464). Notably, our sample period predates the specialized daily financial press by nearly two decades, with the Financial News and Financial Times first published in 1884 and 1888, respectively. Although the Financial Times sought to provide cutting insight and commentary from its foundation, the market reporting in The Times and other newspapers tended “to be fairly staid … [and] desperately dull” (Kynaston, 1988, p. 3). This is immaterial for our analysis because we are simply interested in whether the media reported on companies. Unlike modern newspaper databases, the TDA enables users to identify advertisements through search filters. This has a two-fold advantage for our study. First, we are able to see if any correlation exists between advertisements by companies and subsequent coverage in The Times. Second, we are able to remove advertisements from our definition of media coverage to ensure that our findings are robust. Articles were identified using unique search filters for each of the 580 companies. Searches for company names were carried out for the period that they were listed in the COE and the search results on the TDA were set to exclude cases where a company was simply reported in a stock price table in the newspaper. An additional industry filter was occasionally used if there was ambiguity that articles were irrelevant. The number of articles published in each section of the newspaper for each year that the company was active was then recorded. The total number of articles published over the sample period was 6,995. Table II shows that across the entire sample period, 57% of stocks were covered by The Times. However, annual coverage rates in The Times varied between 5% and 42% and of the companies that were covered (hereafter “media stock”). The mean and median number of articles published were 20.95 and 5.0, respectively, which indicates that there were some companies with substantial amounts of press coverage. Remarkably, Fang and Peress (2009) find that The New York Times covered 57% of all companies listed on the New York Stock Exchange between 1993 and 2002. However, the mean and median number of articles published for media stock in their sample were only 4.2 and 2.0, respectively, while annual coverage rates were substantially higher than in our sample, ranging from 41% to 62%. Table II Summary statistics of company coverage in The Times, 1825–70 Not all firms were active for the entire sample period. We consider the fraction of firms that were publically listed and that were covered by The Times in a given year. The figures for 1825–47, 1848–70, and 1825–70 are not averages of the various years, but consider the three periods in their entirety. Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 Table II Summary statistics of company coverage in The Times, 1825–70 Not all firms were active for the entire sample period. We consider the fraction of firms that were publically listed and that were covered by The Times in a given year. The figures for 1825–47, 1848–70, and 1825–70 are not averages of the various years, but consider the three periods in their entirety. Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 The trend in Table II is also interesting because it is consistent with our hypothesis that after the expansion of arm’s-length ownership, there is greater coverage of companies in terms of the number of articles per covered company, with the average for the 1848–70 period being 20.46, compared to 10.56 for the first half of our sample period. There is also a noticeable step change in the annual fraction of companies covered after the mid-1840s. In the period 1825–47, the average annual fraction of companies covered in The Times was 0.15, whereas in the period 1848–70, the corresponding figure was 0.26. Table II also provides details on how the relative importance of advertising changed over our sample period. From 1825 until 1847, 22% of all media coverage was advertisements, but in the period 1848 until 1870, this nearly halves to 12%. Table III shows media coverage by industry and by section of the newspaper, that is, advertisements and newspaper reporting on companies. The Times covered 57.24% of sample companies, with 32.41% of companies having advertisements and 47.07% of companies being reported on by the newspaper. Notably, 10.17% of our sample only appeared in adverts and were not reported on by The Times. Table III Summary statistics of company coverage in The Times by industry, 1825–70 This table reports the percentage of companies covered in different sections of The Times by industry. “Adverts” refers to the fact that the company has advertised in The Times and “Reporting” is where The Times has carried a news report on a company. “Any section” refers to both news reports and advertisements. N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 Table III Summary statistics of company coverage in The Times by industry, 1825–70 This table reports the percentage of companies covered in different sections of The Times by industry. “Adverts” refers to the fact that the company has advertised in The Times and “Reporting” is where The Times has carried a news report on a company. “Any section” refers to both news reports and advertisements. N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 In terms of industry coverage, Table III shows that of the large industrial sectors, railways were by some distance the most covered industry, with 87.69% of railways in our sample being reported on by The Times. This is consistent with our hypothesis that press coverage is greater for companies with more diffuse and arm’s-length ownership. Canals and British mines are two large sectors with very little press coverage. This is also consistent with our hypothesis as canals and British mines were typically owned by investors living in proximity to the canals and mines, and were not characterized by diffuse and arm’s-length ownership (Bartlett, 1850; Ward, 1974; Burke and Richardson, 1981). Investors in gas-light and coke companies typically came from the towns and cities in which they were located (Falkus, 1967), which is consistent with the relatively low coverage of this sector by The Times. Insurance companies have relatively a lot of coverage in The Times, but this is somewhat unsurprising due to the large number of advertisements placed by this sector. About half of the banks in the sample were covered by The Times. Many banks in our sample had diffuse ownership, but several were small provincial banks dominated by a local shareholder base (Turner, 2009; Newton, 2010), which explains the relatively low coverage of this sector. The miscellaneous sector contains industrial and commercial companies, many of which were established after the liberalization of incorporation law in the mid-1850s. The relatively high press coverage of this sector is consistent with recent evidence which suggests that many of these companies had diffuse and arm’s-length ownership (Acheson et al., 2015). 4. The Determinants of Media Coverage In this section, we examine the factors that determined coverage. In particular, we are interested in whether the diffusion of share ownership and advertising are covariates of media coverage. One of our main hypotheses is that diffuse ownership meant that there was a greater need for media coverage. We also hypothesize that companies which advertised in The Times were more likely to be subsequently reported on by the paper. In order to assess the relationships between advertising and media reporting and share ownership and media reporting, we aggregated media coverage, number of shares issued, and advertisements to an annual level, and used the last observed values of the various company and stock characteristics for the given year. Lagged explanatory variables are used so that advertisements predate media reporting. To ensure that our variables are not capturing any company-specific characteristics, we controlled for observed variables such as industry, return, nominal value, and market capitalization. We used a two-stage Generalized Method of Moments (GMM) Estimation (Hansen, 1982), which is conceptually equivalent to Fama–MacBeth (1973) regressions. The findings are robust to using other approaches such as Rogers (1993) or Newey–West (1987) robust standard errors. Our regression specification is as follows: Mediait=γ0t+γ1Adsit-1+γ2NSharesit-1+ γ3Xit-1+εt (1) where Mediait is the number of articles on company i reported on by The Times in year t, Adsit−1 is the number of advertisements placed by company i in The Times in year t − 1; NSharesit−1 is the number of issued shares of company i in year t − 1; and Xit−1 is a matrix of control variables, with controls for industry, number of shares, return, nominal value, and market capitalization. Table IV shows a significant and positive relationship between advertisements placed in The Times and coverage in the main body of the newspaper over the subsequent year. Results suggest that placing ten advertisements in one year is associated with an additional article for that company in the following year. This finding is robust to excluding railway companies and using different time periods, with the advertising effect increasing in the post-1850 period. Table IV Determinants of company coverage in The Times, 1825–70 We use two-stage GMM estimation (Hansen, 1982). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. The dependent variable is Media, which is the number of news articles on a company reported in The Times in year t. Advertising is the number of advertisements placed in The Times by a company in year t − 1. Number of shares is the average natural log of number (in 000,000’s) of shares outstanding for a company in year t − 1. Industry controls are a series of dummy variables to capture industry effects. Share denomination is the nominal value of shares in £s for a company. Market capitalization is the natural log of market value of a company in £millions in year t − 1. Absolute return is the previous year’s absolute return. Volatility is the previous year’s standard deviation in returns. All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 Table IV Determinants of company coverage in The Times, 1825–70 We use two-stage GMM estimation (Hansen, 1982). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. The dependent variable is Media, which is the number of news articles on a company reported in The Times in year t. Advertising is the number of advertisements placed in The Times by a company in year t − 1. Number of shares is the average natural log of number (in 000,000’s) of shares outstanding for a company in year t − 1. Industry controls are a series of dummy variables to capture industry effects. Share denomination is the nominal value of shares in £s for a company. Market capitalization is the natural log of market value of a company in £millions in year t − 1. Absolute return is the previous year’s absolute return. Volatility is the previous year’s standard deviation in returns. All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 The coefficient on the NShares variable in Table IV variable indicates that the greater the number of issued shares which a company had, the greater the likelihood that it was covered in the press. This finding is consistent with our hypothesis that companies with more diffuse and arm’s-length ownership were more likely to be covered in The Times. The results in Table IV suggest that larger companies, as proxied by the market capitalization variable, were more likely to be covered by the media, which is a similar finding to that of Bignon and Miscio (2010). Notably, a stock’s absolute return is not a covariate of media coverage, suggesting that the media were not more likely to report on stocks that were performing particularly well or poorly. The above results suggest that the media is responding to the market need for additional information on those companies with diffuse ownership. This additional information could encourage more investors to buy a stock and enable companies to further increase their share ownership. To test if there is feedback between media coverage and share ownership, we assessed the relationship between number of shares and lagged media coverage in a GMM framework, controlling for additional variables as per Equation (1). We find that there is a significant relationship between previous media coverage and present number of shares on the market. Thus, there appears to be a feedback between media coverage and market participation. To understand the factors that precede the first occurrence of media coverage for companies, we analyzed the time series of annual company data. We find that there is little time-series variation in the number of shares at the company level, with changes occurring in only 2.87% of observations. The likelihood of this preceding the start of media coverage is very small—in only 0.10% of observations was there a change in the number of shares followed by the first occurrence of media coverage within the next year. Other corporate policy decisions were similarly unlikely to be followed by initial media coverage, for example, in only 0.17% of observations was there a change in dividends followed by the first occurrence of media coverage. 5. Did Media Affect Returns? If the press is increasing the information available to investors, it reduces the investor recognition problem for covered stocks, which should in turn result in lower returns for such stocks relative to stocks which are not covered (Fang and Peress, 2009). We initially used a broad definition of media coverage which views media coverage as advertisements plus media reports on companies. We do this for a theoretical and a practical reason. The theoretical reason for doing this is that advertisements in newspapers may have aided investor recognition just as easily as press reporting on companies and the only difference was that advertisements were simply coverage which was paid for by the company. The practical reason is that prior to 1846, there are insufficient companies with media coverage that are not advertisements to facilitate a statistically robust portfolio analysis. However, for the sake of robustness, we excluded advertisements from our definition of media coverage and our conclusions do not differ. To test if media coverage affected returns at the cross-sectional level, we formed portfolios of stocks based on media coverage in the prior year. At the beginning of each year, we divided our sample into companies with media coverage and those without. The monthly performance of each portfolio is then assessed over the next 12 months.3 This process is repeated for each of the years between 1826 and 1870. We then calculated the differential returns between the two portfolios during our sample period and this is our measure for the media effect. Figure 2 shows the 24-month moving average of differential returns between the media and no-media portfolios between 1826 and 1870. Although there appears to be cyclicality in the media effect, the long-run trend demonstrates that higher returns gave way to lower returns from the late-1840s for companies covered by the media. For example, the value-weighted media portfolio outperforms the no-media portfolio by 8.4 basis points per month between 1826 and mid-1848. However, it underperforms the no-media portfolio by 29.4 basis points per month post mid-1848. Figure 2 View largeDownload slide The 24-month moving average differential returns between companies with and without media coverage, LSE, 1828–70. Based on 580 companies listed on the COE. Portfolios of stocks are formed based on media coverage in The Times in the prior year. At the beginning of each year, we divided our sample into companies with media coverage and those without. The monthly performance of each portfolio is then assessed over the next 12 months. We then calculated the differential returns between the two portfolios as media coverage minus no media coverage. Differential returns are based on the listwise method for treating missing prices. Figure 2 View largeDownload slide The 24-month moving average differential returns between companies with and without media coverage, LSE, 1828–70. Based on 580 companies listed on the COE. Portfolios of stocks are formed based on media coverage in The Times in the prior year. At the beginning of each year, we divided our sample into companies with media coverage and those without. The monthly performance of each portfolio is then assessed over the next 12 months. We then calculated the differential returns between the two portfolios as media coverage minus no media coverage. Differential returns are based on the listwise method for treating missing prices. In Table V, we report the statistical significance of the media effect for the whole sample period as well as in the two sub-periods.4 In addition, in order to examine whether the differential returns between media and no media portfolios can be attributed to different level of risks, we calculated the risk-adjusted differential returns (i.e., alphas for the portfolio that long media stocks and short no-media stocks), using three classic asset pricing models: CAPM, Fama–French three-factor model, and Carhart four-factor model. The SMB and HML factors are constructed following Fama and French (1993) and the WML factor is constructed following Carhart (1997).5 In order to control for a possible bias in the estimation of the risk loadings due to the thin-trading problem, we report the results for the risk-adjusted returns where the bias is corrected using Dimson’s (1979) method. Table V Testing the media effect: the difference between returns of companies covered and not covered by the media (%) At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. Raw differential return represents the differential monthly returns between the media and no-media stocks. We then regress this raw differential return series against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The SMB and HML factors are constructed following Fama and French (1993) except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). We use three different treatments for missing stock prices. In Panel A, missing prices were assumed to be the same as last available price. In Panel B, in observations where stock prices were missing, we filled in the total returns with the mean returns of the same stock over the sample period. In Panel C, observations with missing prices are deleted and all calculations only use the remaining observations. Panel D uses the same assumption about missing prices as Panel A, but the delisting bias was adjusted. ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Table V Testing the media effect: the difference between returns of companies covered and not covered by the media (%) At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. Raw differential return represents the differential monthly returns between the media and no-media stocks. We then regress this raw differential return series against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The SMB and HML factors are constructed following Fama and French (1993) except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). We use three different treatments for missing stock prices. In Panel A, missing prices were assumed to be the same as last available price. In Panel B, in observations where stock prices were missing, we filled in the total returns with the mean returns of the same stock over the sample period. In Panel C, observations with missing prices are deleted and all calculations only use the remaining observations. Panel D uses the same assumption about missing prices as Panel A, but the delisting bias was adjusted. ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Following Ye and Turner (2014), we used three different treatments for missing stock prices. First, we assumed missing prices were the same as the last available price. We call this the zero-return method. Second, in the listwise method, observations with missing prices are deleted and all calculations only use the remaining observations. Finally, in the mean return method, we filled in the total returns of the observations when prices were missing with the mean returns of the same stock over the sample period. When stocks were delisted, they disappear from our dataset. When delisting was the result of bankruptcy rather than name changes, mergers, or listing migrations to regional exchanges, shareholders potentially suffered large losses, which are not captured. The difficulty in identifying the cause of delisting is highlighted by Ye and Turner (2014). If the reason for delisting is unknown, we assume that the reason for delisting was bankruptcy. We assigned a −40% return to all stocks on the month following delisting, following the assumption made by Ye and Turner (2014). As the delisting adjustment does not affect our main findings, we focus our discussion on the results with no adjustment for delisting bias unless otherwise stated. Table V shows that, with the exception of the listwise method, there is no statistical difference in return differentials when we focus on the overall period. However, consistent with our hypothesis, we see that in the first half of our sample period, there is little evidence of a media effect, whereas in the second half of our sample period, the results in Table V show that companies with media coverage tended to have much lower returns. Furthermore, the differential returns in the second half of our sample period become even more negative after adjusting for the different level of risks in the media and no-media portfolios. Between 1848 and 1870, the magnitudes of the risk-adjusted differential returns are in the range 0.311–0.422. The scale of the media effect is comparable to modern markets, where Fang and Peress (2009) found that no-media stocks outperform media stocks by about 3.0% on an annual basis after adjusting for known risk factors. We find the media effect for the period 1848–70 is slightly higher at 3.79%–5.18%.6 We also analyzed the performance as well as the risk loadings of media and no-media portfolios separately. The results are reported in Table VI.7 Consistent with Fang and Peress (2009), the media effect is more likely to be driven by companies not covered by the media having abnormally high returns rather than media covered companies having abnormally low returns. For example, the alphas for the no-media portfolios are significantly positive but those for the media portfolios are not negative. In the value-weighted portfolio returns, alphas for the media portfolio are not significantly different from zero, suggesting that they can be justified by their risk structure. The risk loadings reported in Table VI suggest that, relative to the media covered stocks, the no-media stocks tend to have lower market risk, greater SMB and WML loadings, and smaller HML loadings. Apart from the loadings on the HML factor, these results are also consistent with the results in Fang and Peress (2009). Table VI Returns, alphas, and risk loadings of media and no-media stock portfolios At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. We then regress the return for each portfolio against several classic risk factors. Raw return represents the monthly returns for the media and no-media stocks. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models, including CAPM, FF three-factor and Carhart four-factor models, respectively. Market beta, SMB, HML, and WML are the coefficients on market factor, SMB, HML, and WML factors in these risk models. Media column reports the returns, alphas, and risk loadings for the portfolio with media covered stocks. No-media column reports the returns, alphas, and risk loadings for the portfolio with stocks that were not covered by media. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. The SMB and HML factors are constructed following Fama and French (1993), except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 Table VI Returns, alphas, and risk loadings of media and no-media stock portfolios At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. We then regress the return for each portfolio against several classic risk factors. Raw return represents the monthly returns for the media and no-media stocks. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models, including CAPM, FF three-factor and Carhart four-factor models, respectively. Market beta, SMB, HML, and WML are the coefficients on market factor, SMB, HML, and WML factors in these risk models. Media column reports the returns, alphas, and risk loadings for the portfolio with media covered stocks. No-media column reports the returns, alphas, and risk loadings for the portfolio with stocks that were not covered by media. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. The SMB and HML factors are constructed following Fama and French (1993), except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 There are two possible mechanisms through which the media effect may emerge and persist: Merton’s (1987) investor recognition mechanism and Miller's (1977) impediments-to-trade mechanism. In the investor recognition hypothesis, stocks with lower investor recognition need to offer higher returns to compensate their holders for being imperfectly diversified. Because media coverage can broaden investors’ recognition, it reduces the returns on covered stocks relative to noncovered stocks. To investigate this hypothesis, we double-sorted companies by media coverage and several company characteristics.8 As pointed out by Chichernea, Ferguson, and Kassa (2015), neglected stocks are, in general, smaller and have higher idiosyncratic volatility relative to more visible stocks. Therefore, for each year, we double-sorted companies based on their prior year’s media coverage and their size or idiosyncratic volatility in the year. The size of a stock was proxied by its market capitalization. The idiosyncratic volatility for each stock was constructed following Ang et al. (2006).9 In Panels A and B of Table VII, we report the monthly raw and risk-adjusted differential returns from the double-sorted portfolios. We find that the media effects are much stronger for smaller stocks and for stocks with higher idiosyncratic volatility. These results suggest that media coverage among less recognized companies has a greater effect on stock returns. This is consistent with Merton’s (1987) investor recognition hypothesis. Table VII The media effect among stocks with different characteristics At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for one of several company characteristics (e.g., size, idiosyncratic volatility, liquidity, and nominal value). The size of a stock is proxied by its market capitalization at the end of year. The idiosyncratic volatility for each stock is constructed following Ang et al. (2006). Based on Lesmond, Ogden, and Trzcinka (1999), we approximate the zero-return measure of liquidity at each year for each stock by dividing the number of months with nonzero return by the number of months in the year. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw differential return represents the differential monthly return between media and no-media portfolios among each group of stocks. We then regress this raw differential return against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) Table VII The media effect among stocks with different characteristics At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for one of several company characteristics (e.g., size, idiosyncratic volatility, liquidity, and nominal value). The size of a stock is proxied by its market capitalization at the end of year. The idiosyncratic volatility for each stock is constructed following Ang et al. (2006). Based on Lesmond, Ogden, and Trzcinka (1999), we approximate the zero-return measure of liquidity at each year for each stock by dividing the number of months with nonzero return by the number of months in the year. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw differential return represents the differential monthly return between media and no-media portfolios among each group of stocks. We then regress this raw differential return against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) If the premium for no-media stocks represents mispricing, arbitrageurs can eliminate the premium only if there are no significant impediments-to-trade (Miller, 1977). Thus, it may be that no-media stocks have greater trading impediments, which means that the mispricing cannot be exploited by traders and that the media effect does not disappear. We assessed this possibility by double sorting portfolios by media coverage and two measures of trading impediments. First, we used a stock’s nominal value to approximate the impediments to trade. Low nominal value stocks in this era had higher trading costs and thus greater impediments-to-trade (Acheson, Turner, and Ye, 2012, p. 870). Our second measure of trading impediments is stock liquidity. Based on Lesmond, Ogden, and Trzcinka (1999), we used the zero-return measure of liquidity for each year for each stock by dividing the number of months with nonzero return by the number of months in the year. Panels C and D in Table VII show that in our sample, the media effect is stronger for low-trading-impediments stocks rather than high-trading-impediments stocks using both the zero-return liquidity and nominal value proxies. These findings are inconsistent with the impediments-to-trade hypothesis.10 6. Ownership Diffusion and Media Effect The results in Table V suggest that the media effect emerged in the second half of the sample period. We argue that this media effect appears at this point in time because corporate ownership in the UK had become diffuse and arm’s-length, and therefore the role that media played in increasing investor recognition for covered stocks became more important in influencing the relative return between media and no-media stocks. In order to obtain corroborating evidence for this conjecture, we conducted two types of analysis. First, we double sorted our sample stocks based on media coverage and ownership diffusion in order to investigate whether the media effect has any cross-sectional relation with a stock’s degree of ownership diffusion. If diffuse ownership is a necessary condition for the emergence of the media effect, we should observe that the media effect only exists or is much stronger in stocks with high ownership diffusion. Second, in a time-series regression analysis, we tested whether media stocks’ relative degree of diffuse ownership can explain away the media effect. Unfortunately, systematic evidence on corporate ownership structure or number of shareholders in this era is sporadic (Acheson et al., 2015). Instead, we have to rely on a proxy for ownership structure. The proxy we used is the number of shares companies issued because this gives some idea about how many shareholders the company wished to hold their stock and the diffuseness of ownership. In order to show that shares outstanding is associated with diffuse ownership, we collected data on the number of shareholders for all English banks in 1850, 1860, and 1870 from the relevant issue of the Banking Almanac and Yearbook. Data for the number of railway shareholders is only available for 1855 from a special report commissioned by the UK Parliament (Parliamentary Papers, 1856). Companies which registered after 1856 had to produce an annual list of shareholders under UK company legislation. Fortunately, some of these lists have been preserved in the National Archives in London. We obtained pre-1870 ownership records for forty-three companies traded on the LSE. Notably, these records permit us to calculate ownership dispersion as well as the number of shareholders. In terms of English banks in 1850, 1860, and 1870, the correlation between the number of issued shares and number of shareholders is 0.72, 0.61, and 0.69, respectively. For the fifty railways in our sample in 1855, the correlation between issued shares and number of shareholders is 0.75. For the miscellaneous forty-three companies, the correlation coefficient was 0.84. In addition, the correlation between the capital ownership of the top five and ten shareholders and number of issued shares was −0.46 and −0.50, respectively, suggesting that diffuse ownership structure was correlated with a greater number of issued shares. In Table VIII, where we display the returns from the four portfolios double sorted on ownership diffusion and media coverage, we see that the media effect only exists in stocks with high ownership diffusion. This suggests that when a stock’s ownership diffusion is low, media coverage has no effect on the stock’s return. In contrast, when a stock has a diffused ownership structure, its return/alphas become much lower if the company is covered by the press. In addition, from Table VIII we can see that the media portfolios’ alphas are not significantly different from zero for the stocks with high ownership diffusion. This suggests that due to increased investor recognition, investors no longer require higher returns for the companies covered by the press. This is consistent with our conjecture that arm’s-length ownership is a pre-condition for the media effect. Table VIII Ownership diffusion and the media effect: cross-sectional analysis At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for the sample stocks’ ownership diffusion in the year. The ownership diffusion is proxied by the number of shares of a company’s stock. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw return represents the monthly returns for the media and no-media portfolios among each group of stocks. We then regress the raw returns against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. Media column reports the returns and alphas for the portfolio with media covered stocks. No-media column reports the returns and alphas for the portfolio with stocks that were not covered by media. DIFF column reports the differential returns and alphas between the media and no-media portfolios. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) Table VIII Ownership diffusion and the media effect: cross-sectional analysis At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for the sample stocks’ ownership diffusion in the year. The ownership diffusion is proxied by the number of shares of a company’s stock. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw return represents the monthly returns for the media and no-media portfolios among each group of stocks. We then regress the raw returns against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. Media column reports the returns and alphas for the portfolio with media covered stocks. No-media column reports the returns and alphas for the portfolio with stocks that were not covered by media. DIFF column reports the differential returns and alphas between the media and no-media portfolios. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) To further corroborate this finding, we form two portfolios based on media coverage to assess differences in portfolio number of shares and liquidity. Based on our prior argument, we would expect that the relative degree of ownership diffuseness for the media-covered stocks compared to the no-media stocks is negatively associated with the difference in their returns. More importantly, the media effect should disappear once the differential ownership diffusion between media-covered and no-media stocks is controlled for. For the sake of brevity, we do not report results, but our findings are consistent with our prior expectation. When controlling for both liquidity and ownership diffusion, only ownership diffusion is significantly correlated with differential returns, suggesting that liquidity is not correlated with the media effect. Consequently, ownership diffusion does not simply serve as a proxy for liquidity, suggesting that ownership diffusion goes some way to explain the existence of a media effect. 7. Conclusions The main finding of this article is that media coverage of stocks grows substantially after the emergence of arm’s-length and diffuse ownership in the UK from the mid-1840s onwards. We argue that the media were playing an important informational role for the new cadre of middle-class investors which emerged at this time and that the additional information generated by the press increased investor recognition for covered stocks. Consistent with this, after the mid-1840s, we find that companies not covered by the media had higher returns relative to media companies. In other words, as in modern developed country stock markets, there was a media effect in the nineteenth-century London market, but this only emerged after ownership became arm’s-length and diffuse. Therefore, our findings imply that arm’s-length and diffuse ownership may be a prerequisite for the media effect. Indeed, the absence of arm’s-length and diffuse ownership may explain why media appears to have little effect on developing country’s financial markets today (Griffin, Hirschey, and Kelly, 2011). Our findings suggest two avenues that could be explored by future scholars. First, our findings highlight the relationship between press reporting and advertisements. Future work could explore the nature of this relationship and whether it was insidious or benign. Second, newspaper reporting on financial markets in our period was factual, which means that an analysis of the tone or language used in newspaper reports is not possible. However, the development of the UK’s daily financial press in the 1880s and whether it influenced financial markets through its use of language is something that future work could explore. Footnotes * Thanks to the ESRC (RES-000-22-1391) for financial support. Thanks to Amit Goyal and an anonymous referee for their comments. Thanks to Graeme Acheson for his invaluable input to this project. Research assistance was provided by Lei Qu and Nadia Vanteeva. 1 Dyck and Zingales (2003) in their study highlight that the effect of media is more pronounced for companies with low analyst coverage. 2 Banking Almanac and Yearbook, 1844 and 1870. 3 To reduce the influence of outlier returns, we winsorized monthly stock returns at the 0.5 and 99.5 percentiles. 4 For the sake of brevity, we present results for the first and second half of our sample only with June/July 1848 being the mid-point. It should be noted that the hypothesized change to the media effect is not likely to be identifiable to a single date, and we have used alternative breakpoints with qualitatively similar results. 5 We construct Small and Big portfolios using the median market capitalization of stocks at December each year as the breakpoint. As book-to-market data are not available during our sample period, we construct High, Medium, and Low portfolios using the 30th and the 70th percentiles of the dividend price ratio at December as the breakpoints. The dividend price ratio is calculated as the sum of dividend paid in the year divided by the end-of-year stock price. From these, we get six intersection portfolios, namely, Small High, Small Medium, Small Low, Big High, Big Medium, and Big Low. SMB is the average return on the three small portfolios minus the average return on the three big portfolios. HML is the average return on the two high portfolios minus the average return on the two low portfolios. Zero-yielding stocks are excluded when constructing the SMB and HML factors. To construct the WML factor, at each month t, we construct Winner and Loser portfolios based on the 30th and 70th breakpoint of the 11-month returns between months t − 1 and t − 12. The difference between the equally weighted returns from the Winner portfolio and the Loser portfolio is our WML factor. 6 Because railways were the dominant sector on the equity market after the mid-1840s and because the Railway Mania of the mid-1840s may distort our findings, we checked whether our findings are robust to their exclusion. For the sake of robustness, we also looked at the difference between media and no-media portfolios using a narrower definition of media coverage, that is, one which excludes advertisements. Results are qualitatively similar when excluding railway companies or advertisements; a media effect emerges in the second half of our sample, ranging from 0.190% to 0.375% per month. 7 In the following sections, we only report results from the listwise method for space reasons. 8 The breakpoint for constructing portfolios with different level of company characteristics is always the 50th percentile. 9 The idiosyncratic risk for a stock in year t is defined as the standard deviation of the residual in the regression of this stock’s return against the factors suggested by the Fama and French three-factor model. 10 As per previous results, we make adjustments for delisting, exclude railway companies and exclude advertisements. Our findings are robust to these changes. References Acheson G. G. , Hickson C. R. , Turner J. D. , Ye Q. ( 2009 ) Rule Britannia!: British stock market returns, 1825–1870 , Journal of Economic History 69 , 1107 – 1137 . 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( 2014 ) The cross-section of stock returns in an early stock market , International Review of Financial Analysis 34 , 114 – 123 . Google Scholar CrossRef Search ADS © The Authors 2017. Published by Oxford University Press on behalf of the European Finance Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Finance Oxford University Press

Media Coverage and Stock Returns on the London Stock Exchange, 1825–70

Review of Finance , Volume Advance Article (4) – Apr 16, 2017

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Oxford University Press
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© The Authors 2017. Published by Oxford University Press on behalf of the European Finance Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Abstract

Abstract News media plays an important role in modern financial markets. In this article, we analyze the role played by the news media in an historical financial market. Using The Times’s coverage of companies listed on the London stock market between 1825 and 1870, we examine the determinants of media coverage in this era and whether media coverage affected returns. Our main finding is that a media effect mainly manifests itself after the mid-1840s and that the introduction of arm’s-length ownership along with markedly increased market participation was the main reason for the emergence of this media effect. 1. Introduction The UK capital market underwent a major transformation in the nineteenth century, with large capital-intensive companies raising funds on the equity market from multiple arm’s-length investors. This revolution resulted in a major increase in the number and value of companies listed, an increase in the number of stock exchanges operating outside London, and a substantial increase in the proportion of the UK’s population investing in equities (Thomas, 1973; Michie, 1999, pp. 88–89; Grossman, 2002; Acheson et al., 2009; Rutterford et al., 2011). This expansion of the equity market was accompanied by an increase in the demand for corporate information, which was partially met by companies through annual shareholder meetings and annual reports. In addition, the financial press emerged during the nineteenth century to meet the demand from arm’s-length investors for independent corporate news (Preda, 2001; Taylor, 2012). In this article, we analyze the press coverage of companies listed on the London stock market in the nineteenth century. In particular, we examine the coverage by The Times, the leading newspaper of the day, of companies listed between 1825 and 1870. First, we look at the determinants of press coverage to understand better what characteristics were associated with a greater probability of being covered by The Times. For example, we examine whether companies which advertised in The Times in one year were more likely to be covered in it the following year. Secondly, using monthly stock data collected from the London Stock Exchange’s (LSE’s) official price list, we test whether media coverage affected stock returns. The media may affect returns because companies not covered in the press need to pay a premium in the form of higher expected returns because they lack recognition among investors (Merton, 1987; Fang and Peress, 2009) or because there are impediments-to-trade such as liquidity constraints or higher transaction costs which prevent traders from exploiting mispriced securities (Miller, 1977). This article is the first to examine the effect of the media on asset prices before and after the period when arm’s-length and diffuse ownership emerged. We hypothesize that the media had no influence in the first half of our sample period because ownership was concentrated in the hands of a small number of shareholders, who would have had access to corporate information via local networks or through involvement in corporate governance. However, in the second half of our sample period, we hypothesize that media influenced relative returns between media and no-media stocks because they provided arm’s-length investors with additional and valuable information for covered companies. Consequently, companies that were not covered by the media and which had diffuse share ownership had to offer a higher return than companies covered by the media due to a lack of information about the company. An additional motivation for this article is that it tests the relationship between media and finance in an environment where there were few other substitutes or confounding information providers such as analysts, city circulars, 24-hour television, or internet news sources.1 In addition, equity investors at this time were individuals rather than well-informed institutional investors (Anderson and Cottrell 1975; Cheffins, 2008, p. 190; Turner 2009; Campbell and Turner, 2012). This makes the nineteenth-century stock market a unique and relatively noiseless environment in which to test the media–finance hypothesis. A further feature of this era which makes this article interesting is that, unlike with modern newspapers, advertisements were exclusively text based, making it easier to gather information on whether companies which were reported on in the newspaper had previously placed advertisements with the paper. We are not testing whether companies “paid” for coverage or whether journalistic integrity was compromised—we simply wish to see if the two things are correlated, which might suggest some implicit and unspoken arrangement. Indeed, pressure for The Times to acquiesce to such arrangements may have grown during the century because of increased competition from aggressive parvenus in the newspaper market. We find that media coverage was broadly comparable to modern markets, with The Times covering 57% of our sample companies. Notably, the proportion of companies covered in a particular year increases in a nonlinear fashion over time and the average number of articles written conditional on coverage also increased nonlinearly over time. In terms of media coverage, the number of issued shares, company size, and industry are all important determinants, suggesting that larger, widely held companies were more likely to be covered by the press in the nineteenth century. Interestingly, we also find that placing ten advertisements in The Times is associated with an additional article for that company in the subsequent year. With regards to the media effect, when we examined the whole sample period and the first half of the sample period, the evidence that returns vary with media coverage is rather weak. However, in the second half of our sample period, we find that companies without coverage have statistically significant higher returns, even after adjusting for market and company-specific risk factors. This is consistent with our hypothesis that a media effect would only manifest itself after the mid-1840s, when participation in the stock market increased dramatically and when arm’s-length ownership began to emerge. In an attempt to corroborate this hypothesis, we test whether stocks with greater participation and arm’s-length ownership experience a larger media effect. Using the number of shares issued as a proxy for participation and arm’s-length ownership, we find some evidence supporting our hypothesis. We also discover that in times when the level of ownership diffusion is greater for media relative to no-media stocks, the media effect is larger. This is consistent with our hypothesis that ownership diffusion is closely associated with the media effect. In terms of the mechanism, our evidence suggests that no-media stocks have to pay a return premium because they lack investor recognition and not because of impediments to trade, which supports Merton (1987) rather than Miller (1977). This study contributes to the growing literature which examines the relationship between media and financial markets. One branch of this literature looks at whether sentiment, as measured by the optimism/pessimism of newspaper reporting, affects asset prices (Tetlock, 2007; Tetlock, Saar-Tsechansky, and Macskassy, 2008; Engelberg, Reed, and Ringgenber, 2012; García, 2013; Soo, 2013; Walker, 2014; Manela and Moreira, 2015). The other branch of this literature focuses on the informational role played by news media and therefore concentrates on the extent of coverage (Fang and Peress, 2009; Cumming et al., 2016; Ferguson et al., 2015). Our article is most closely related to this second branch of the literature and our findings are similar to the seminal contribution of Fang and Peress (2009), who identify a media effect on NYSE and NASDAQ stocks in the period 1993–2002. However, our unique contribution is that we identify that the media effect only emerges when stock ownership becomes diffuse and arm’s-length. Our article also augments the literature which looks at the informational role of media in historical contexts such as its effect on public health (Costa and Kahn, 2015), financial “bubbles” (Bhattacharya et al., 2009; Campbell, Turner, and Walker, 2012), and corporate scandals (Taylor, 2012). We augment these papers by looking at the informational role of media on the equity market for the middle two quarters of the nineteenth century. Bignon and Miscio (2010) examine the effect of payments made by French newspapers both directly through advertising and indirectly through investment banks placing laudatory articles in a newspaper’s editorial section (so-called réclames) on media coverage. They find that companies paying for coverage were more likely to be covered. We also find that companies that advertised in The Times were more likely to be covered. The next section examines the institutional and historical setting of the study and develops our hypotheses. Section 3 describes our media and stock price data. Section 4 examines the determinants of media coverage. Section 5 asks whether there was a media effect in this historical market and tests the impediments-to-trade and investor-recognition hypotheses regarding the mechanism by which media affects returns. Section 6 tests our explanation as to why we only find a media effect in the second half of our sample period. Section 7 is a brief summary and conclusion. 2. The Development of the Equity Market and the Financial Press The UK equity market grew substantially during the middle two quarters of the nineteenth century. In terms of issues, the market grew by circa 40% between 1825 and 1870, but in terms of market capitalization to GDP, it trebled in size over this same time period (Acheson et al., 2009, pp. 1115–1117). This growth was driven on the demand side by a growing number of middle-class investors looking for returns in excess of those provided by government bonds (Jefferys, 1977). On the supply side, it was stimulated by the liberalization of UK incorporation law (Shannon, 1933; Cottrell, 1980; Taylor, 2006) and the rise of capital-intensive infrastructure projects such as railways, gas-light and coke companies, waterworks, and telegraph companies (Acheson et al., 2009). There was a marked change in the ownership of public companies before and after the 1840s. Prior to the 1840s, shareholder numbers in most companies were in the low hundreds and shareholders typically lived close to the company they were investing in (Acheson et al., 2015). For example, canals, which were the largest companies in terms of market capitalization in the pre-1840 era, fitted this characterization (Ward, 1974). From the mid-1840s onwards, there was a notable change in ownership of public companies which accompanied their growth in scale and their national, rather than regional, reach. This was true for the railway industry, which experienced a substantial surge in growth in the mid-1840s. For example, a Parliamentary survey of railway shareholders in 1855 found that there were 166,125 railway shareholdings (Parliamentary Papers, 1856). Three railway companies had in excess of 10,000 shareholders, a further three had 5,000 shareholders or more, and a further ten had more than 2,000 shareholders (Parliamentary Papers, 1856). But the growth in shareholder numbers was not just limited to railways and new sectors. Banks grew in size and with it their shareholder bases. For example, the number of UK bank shareholdings grew from 23,941 in 1844 to 40,583 in 1869. Only one bank had more than 1,000 shareholders in 1844, yet by 1869, twelve banks had in excess of 1,000 shareholders.2 Consequently, from the mid-1840s onwards, the UK experienced the rise of the arm’s-length and diffuse corporate ownership, which developed further in the last quarter of the nineteenth century, with the majority of large public companies at the beginning of the twentieth century being characterized by this type of ownership (Foreman-Peck and Hannah, 2012). The rise of this type of ownership from the mid-1840s onwards resulted in a change in investors’ access to company information. When ownership was geographically concentrated and there were a low number of shareholders, information on performance was relatively easy to obtain via direct participation in governance, local knowledge, and social networks which contained company directors. However, the rise of dispersed and arm’s-length ownership implied that investors required alternative information sources on company performance. Given that institutional investors did not participate in the equity market in this era and that there was no analyst coverage of stocks, investors could not rely on information being collected, analyzed, and disseminated by these sources. In addition, UK public companies did not face formal reporting requirements until the late-1860s (Watts and Zimmerman, 1983; Baskin and Miranti, 1997, p. 185) and it was not until the early twentieth century that companies listed on the LSE were required to distribute their annual financial accounts to shareholders (Cheffins, 2008, p. 95). Into this information lacuna stepped the press. The Times had covered financial and money markets from well before the 1840s, but the 1840s marked the beginning of widespread press coverage of equity markets by the news media (Preda, 2001; Taylor, 2012). This coverage came in two forms—expanded coverage by newspapers like The Times and the rise of weekly railway periodicals, for example, Railway Times (est. 1837), Herepath’s Railway Journal (est. 1835), and the Railway Record (est. 1844). These periodicals carried share price tables, editorial commentary, company financial reports, reports of company AGMs, and advertisements from railway promoters. The credibility of the information provider is also something which matters for investors. For example, Dyck and Zingales (2003) in their study highlight that the effect of media is more pronounced the more credible is the news source. The railway press was far from impartial—it acted as a cheerleader for railway companies and talked up railway shares during the Railway Mania promotional boom of the mid-1840s, partially due to the large advertising revenue generated for it by railway companies and promoters (Kostal, 1994, p. 37; Campbell, Turner, and Walker, 2012). The Times, on the other hand, was a credible source which was perceived to be independent of the companies it was reporting on. For example, it was extremely critical of speculation in railway shares and it published a highly critical exposé a few weeks before the railway “bubble” crashed (Tuck, 1846; Simmons, 1978, p. 40; Campbell, Turner, and Walker, 2012). In terms of our hypotheses, the context described above suggests that we should expect less press coverage of the equity market before the large expansion of arm’s-length ownership in the 1840s. In addition, due to concentrated ownership, most of the investors were aware and well informed about the companies that they were investing in, there should have been no informational advantage to investors of press reporting on companies and there should have been no effect on the shares of companies which were covered by the press. In contrast, after the expansion of arm’s-length ownership, we would expect greater coverage of companies because newspaper readers who were investing in equities valued this. We also hypothesize that by increasing the information available to investors, the press helped to diminish the problem of “investor recognition” for covered stocks, resulting in lower returns for such stocks (Fang and Peress, 2009). In terms of advertising, we hypothesize that companies which advertised in The Times were more likely to be subsequently covered by the newspaper. This could have occurred for several reasons. First, companies which advertise are more likely to have greater public recognition and therefore be covered by the press. Second, the placing of adverts may have simply brought companies to the attention of The Times’s reporters. Third, as with the case of the French press discussed by Bignon and Miscio (2010), companies may have been indirectly paying for coverage. However, this is not to suggest that this revenue stream influenced the content of reports in The Times. Indeed, it demonstrated this in 1845 when it issued several highly critical reports concerning the railway sector, which was a major source of advertising revenue for the paper. 3. Stock and Media Data Our stock data was obtained from the Course of the Exchange (COE), a bi-weekly list which was regarded as the official price list for the LSE. For listed securities, the COE reported dividends, number of issued shares, nominal and paid-up values of stock, and stock prices. The stock prices reported in the official list are usually the transaction prices from the previous day (Ye and Turner, 2014). We use Ye and Turner’s (2014) hand-collected data for each common stock listed in the COE for every month between March 1825 and December 1870. Our dataset contains 102,408 observations, consisting of stocks issued by 580 companies. Panel A of Table I contains the summary statistics for our dataset, which reveals that stocks in this era had high denominations and that the mean market capitalization was £650,000. Our data comprises stocks from thirteen industries, including banks, bridges, mining, canals, docks, gas-light and coke, insurance, roads, railways, telegraphs, waterworks, and miscellaneous industrial and commercial companies. If media coverage is biased toward particular industries, this will be identified in subsequent analysis. In addition, to account for this possibility, industry controls are used when assessing the impact of media coverage on stock returns. Table I Company descriptive statistics by media coverage, LSE, 1825–70 The definition of media coverage in this table includes advertisements in The Times as well as reporting on companies. The number of observations differs slightly across variables because of missing data: number of shares, paid-up capital per share, stock price, market capitalization, paid capital and dividend yield are all based on 102,408 observations, while nominal value per share is based on 98,712 observations. No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 Table I Company descriptive statistics by media coverage, LSE, 1825–70 The definition of media coverage in this table includes advertisements in The Times as well as reporting on companies. The number of observations differs slightly across variables because of missing data: number of shares, paid-up capital per share, stock price, market capitalization, paid capital and dividend yield are all based on 102,408 observations, while nominal value per share is based on 98,712 observations. No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 No. of shares (000s) Nominal value per share (£) Paid-up capital per share (£) Stock price (£) Market capitalization (£m) Paid capital (£m) Dividend yield (%) Panel A: All companies Mean 19.73 77.02 51.33 71.43 0.65 0.64 0.41 Median 10.00 63.60 36.50 29.00 0.21 0.25 0.39 Std. dev. 31.86 88.74 48.97 157.62 1.72 1.63 0.72 Panel B: Companies with media coverage Mean 32.62 73.07 55.29 52.23 1.42 1.56 0.45 Median 20.00 100.00 50.00 30.25 0.52 0.68 0.36 Std. dev. 43.93 77.16 52.19 59.50 3.20 3.05 1.31 Panel C: No-media coverage companies Mean 16.59 78.03 50.37 76.10 0.46 0.41 0.40 Median 8.00 61.00 33.00 29.00 0.17 0.20 0.40 Std. dev. 27.23 91.42 48.11 172.96 1.01 0.90 0.48 From Panels B and C of Table I, we see that there were notable differences in the characteristics of stocks that did and did not appear in The Times. First, media stocks issued a far greater number of shares: the mean number of shares issued by companies covered by the media being 32,620, which was nearly twice that issued by companies not covered by the media. Second, the average size of media stock was much greater than no-media stock, with mean market capitalization of £1.42 million and £0.46 million, respectively. Both of these differences are consistent with our hypothesis that the media played an important information role for investors in companies which had arm’s-length and diffuse ownership. Figure 1 demonstrates the step change that took place in the UK equity market in the mid-1840s. Prior to the mid-1840s, there had not been much growth in market capitalization or in the number of issued shares on the market. This changed after the mid-1840s, largely due to the arrival of large companies such as the railways rather than a substantial increase in the number of companies on the market (Campbell, 2012). The average number of shares issued by public companies increased from 9,502 to 27,568 after 1845 and the issuance of a greater number of shares was coupled with a decrease in the nominal value of shares by 20.2%. These changes capture an important market development—a significant increase in the number of shares available at lower denominations suggests that shares were held by a greater numbers of investors. The mid-1840s, therefore, marks the watershed moment in the development of diffuse and arm’s-length corporate ownership in the UK (Acheson et al., 2015). Figure 1 View largeDownload slide Total number of issued shares and market capitalization, LSE, 1826–70. Based on Ye and Turner’s (2014) hand-collected data for each common stock listed in the COE for every month between March 1825 and December 1870. The dataset contains 102,408 observations, consisting of stocks issued by 580 companies. Figure 1 View largeDownload slide Total number of issued shares and market capitalization, LSE, 1826–70. Based on Ye and Turner’s (2014) hand-collected data for each common stock listed in the COE for every month between March 1825 and December 1870. The dataset contains 102,408 observations, consisting of stocks issued by 580 companies. Our media data are sourced from The Times via The Times Digital Archive (TDA). The Times was by far the most significant newspaper in terms of influence during our sample period (Brown, 1985, pp. 27–29, 50; Simmons, 1991; Campbell, Turner, and Walker, 2012). It also had by far the widest circulation of any daily UK newspaper, with three to four times the circulation of its nearest rival (Parliamentary Papers, 1852; Campbell, Turner, and Walker, 2012, p. 464). Notably, our sample period predates the specialized daily financial press by nearly two decades, with the Financial News and Financial Times first published in 1884 and 1888, respectively. Although the Financial Times sought to provide cutting insight and commentary from its foundation, the market reporting in The Times and other newspapers tended “to be fairly staid … [and] desperately dull” (Kynaston, 1988, p. 3). This is immaterial for our analysis because we are simply interested in whether the media reported on companies. Unlike modern newspaper databases, the TDA enables users to identify advertisements through search filters. This has a two-fold advantage for our study. First, we are able to see if any correlation exists between advertisements by companies and subsequent coverage in The Times. Second, we are able to remove advertisements from our definition of media coverage to ensure that our findings are robust. Articles were identified using unique search filters for each of the 580 companies. Searches for company names were carried out for the period that they were listed in the COE and the search results on the TDA were set to exclude cases where a company was simply reported in a stock price table in the newspaper. An additional industry filter was occasionally used if there was ambiguity that articles were irrelevant. The number of articles published in each section of the newspaper for each year that the company was active was then recorded. The total number of articles published over the sample period was 6,995. Table II shows that across the entire sample period, 57% of stocks were covered by The Times. However, annual coverage rates in The Times varied between 5% and 42% and of the companies that were covered (hereafter “media stock”). The mean and median number of articles published were 20.95 and 5.0, respectively, which indicates that there were some companies with substantial amounts of press coverage. Remarkably, Fang and Peress (2009) find that The New York Times covered 57% of all companies listed on the New York Stock Exchange between 1993 and 2002. However, the mean and median number of articles published for media stock in their sample were only 4.2 and 2.0, respectively, while annual coverage rates were substantially higher than in our sample, ranging from 41% to 62%. Table II Summary statistics of company coverage in The Times, 1825–70 Not all firms were active for the entire sample period. We consider the fraction of firms that were publically listed and that were covered by The Times in a given year. The figures for 1825–47, 1848–70, and 1825–70 are not averages of the various years, but consider the three periods in their entirety. Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 Table II Summary statistics of company coverage in The Times, 1825–70 Not all firms were active for the entire sample period. We consider the fraction of firms that were publically listed and that were covered by The Times in a given year. The figures for 1825–47, 1848–70, and 1825–70 are not averages of the various years, but consider the three periods in their entirety. Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Year Fraction of active firms covered Covered firms average articles Fraction of adverts/ articles Mean Median Mean Median Panel A: Annual statistics 1825 0.20 1.80 1.0 0.53 1848 0.42 6.50 3.5 0.04 1826 0.20 2.17 1.0 0.35 1849 0.32 4.68 2.0 0.06 1827 0.20 2.84 2.0 0.41 1850 0.31 4.22 2.0 0.10 1828 0.08 4.15 2.0 0.15 1851 0.29 3.64 2.0 0.09 1829 0.13 1.73 1.0 0.39 1852 0.31 5.68 2.0 0.06 1830 0.10 1.75 1.0 0.50 1853 0.22 3.12 2.0 0.18 1831 0.10 1.88 1.0 0.37 1854 0.23 2.22 1.5 0.26 1832 0.12 1.84 1.0 0.31 1855 0.25 2.62 1.0 0.25 1833 0.05 9.38 1.0 0.00 1856 0.26 3.30 2.0 0.20 1834 0.13 3.23 1.0 0.15 1857 0.25 3.64 2.0 0.10 1835 0.11 3.43 1.0 0.19 1858 0.20 3.98 2.0 0.12 1836 0.14 2.40 1.0 0.22 1859 0.18 2.40 1.0 0.12 1837 0.18 2.36 1.0 0.30 1860 0.24 5.89 3.0 0.04 1838 0.17 2.58 1.0 0.29 1861 0.19 2.58 2.0 0.14 1839 0.14 3.53 2.0 0.19 1862 0.21 3.14 1.0 0.16 1840 0.19 2.72 1.0 0.22 1863 0.19 3.38 2.0 0.25 1841 0.14 3.55 2.0 0.17 1864 0.16 3.62 1.0 0.38 1842 0.16 2.88 2.0 0.09 1865 0.24 5.72 2.0 0.07 1843 0.12 2.82 1.0 0.13 1866 0.25 7.44 3.0 0.04 1844 0.17 3.42 1.0 0.08 1867 0.15 5.53 2.0 0.10 1845 0.17 4.41 2.0 0.28 1868 0.11 3.84 2.0 0.24 1846 0.23 3.78 1.0 0.18 1869 0.17 4.79 2.0 0.19 1847 0.29 2.98 2.0 0.17 1870 0.17 3.61 2.0 0.17 Panel B: Period statistics 1825–47 0.54 10.56 3.0 0.22 1848–70 0.56 20.46 5.0 0.12 1825–70 0.57 20.95 5.0 0.14 The trend in Table II is also interesting because it is consistent with our hypothesis that after the expansion of arm’s-length ownership, there is greater coverage of companies in terms of the number of articles per covered company, with the average for the 1848–70 period being 20.46, compared to 10.56 for the first half of our sample period. There is also a noticeable step change in the annual fraction of companies covered after the mid-1840s. In the period 1825–47, the average annual fraction of companies covered in The Times was 0.15, whereas in the period 1848–70, the corresponding figure was 0.26. Table II also provides details on how the relative importance of advertising changed over our sample period. From 1825 until 1847, 22% of all media coverage was advertisements, but in the period 1848 until 1870, this nearly halves to 12%. Table III shows media coverage by industry and by section of the newspaper, that is, advertisements and newspaper reporting on companies. The Times covered 57.24% of sample companies, with 32.41% of companies having advertisements and 47.07% of companies being reported on by the newspaper. Notably, 10.17% of our sample only appeared in adverts and were not reported on by The Times. Table III Summary statistics of company coverage in The Times by industry, 1825–70 This table reports the percentage of companies covered in different sections of The Times by industry. “Adverts” refers to the fact that the company has advertised in The Times and “Reporting” is where The Times has carried a news report on a company. “Any section” refers to both news reports and advertisements. N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 Table III Summary statistics of company coverage in The Times by industry, 1825–70 This table reports the percentage of companies covered in different sections of The Times by industry. “Adverts” refers to the fact that the company has advertised in The Times and “Reporting” is where The Times has carried a news report on a company. “Any section” refers to both news reports and advertisements. N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 N Any section Adverts Reporting Advert only Reporting only Banks 73 53.42 34.25 41.10 12.33 19.18 Bridges 5 80.00 60.00 80.00 0.00 20.00 British mines 57 12.28 12.28 7.02 5.26 0.00 Canals 64 26.56 12.50 20.31 6.25 14.06 Foreign and colonial mines 40 45.00 22.50 27.50 17.50 22.50 Docks 14 78.57 42.86 64.29 14.29 35.71 Gas-light and coke 42 45.24 26.19 33.33 11.90 19.05 Insurance 60 71.67 55.00 48.33 23.33 16.67 Miscellaneous 80 63.75 36.25 48.75 15.00 27.50 Waterworks 14 57.14 42.86 50.00 7.14 14.29 Roads 5 0.00 0.00 0.00 0.00 0.00 Telegraph 7 100.00 42.86 100.00 0.00 57.14 Railways 130 87.69 39.23 86.92 0.77 48.46 All companies 580 57.24 32.41 47.07 10.17 24.83 In terms of industry coverage, Table III shows that of the large industrial sectors, railways were by some distance the most covered industry, with 87.69% of railways in our sample being reported on by The Times. This is consistent with our hypothesis that press coverage is greater for companies with more diffuse and arm’s-length ownership. Canals and British mines are two large sectors with very little press coverage. This is also consistent with our hypothesis as canals and British mines were typically owned by investors living in proximity to the canals and mines, and were not characterized by diffuse and arm’s-length ownership (Bartlett, 1850; Ward, 1974; Burke and Richardson, 1981). Investors in gas-light and coke companies typically came from the towns and cities in which they were located (Falkus, 1967), which is consistent with the relatively low coverage of this sector by The Times. Insurance companies have relatively a lot of coverage in The Times, but this is somewhat unsurprising due to the large number of advertisements placed by this sector. About half of the banks in the sample were covered by The Times. Many banks in our sample had diffuse ownership, but several were small provincial banks dominated by a local shareholder base (Turner, 2009; Newton, 2010), which explains the relatively low coverage of this sector. The miscellaneous sector contains industrial and commercial companies, many of which were established after the liberalization of incorporation law in the mid-1850s. The relatively high press coverage of this sector is consistent with recent evidence which suggests that many of these companies had diffuse and arm’s-length ownership (Acheson et al., 2015). 4. The Determinants of Media Coverage In this section, we examine the factors that determined coverage. In particular, we are interested in whether the diffusion of share ownership and advertising are covariates of media coverage. One of our main hypotheses is that diffuse ownership meant that there was a greater need for media coverage. We also hypothesize that companies which advertised in The Times were more likely to be subsequently reported on by the paper. In order to assess the relationships between advertising and media reporting and share ownership and media reporting, we aggregated media coverage, number of shares issued, and advertisements to an annual level, and used the last observed values of the various company and stock characteristics for the given year. Lagged explanatory variables are used so that advertisements predate media reporting. To ensure that our variables are not capturing any company-specific characteristics, we controlled for observed variables such as industry, return, nominal value, and market capitalization. We used a two-stage Generalized Method of Moments (GMM) Estimation (Hansen, 1982), which is conceptually equivalent to Fama–MacBeth (1973) regressions. The findings are robust to using other approaches such as Rogers (1993) or Newey–West (1987) robust standard errors. Our regression specification is as follows: Mediait=γ0t+γ1Adsit-1+γ2NSharesit-1+ γ3Xit-1+εt (1) where Mediait is the number of articles on company i reported on by The Times in year t, Adsit−1 is the number of advertisements placed by company i in The Times in year t − 1; NSharesit−1 is the number of issued shares of company i in year t − 1; and Xit−1 is a matrix of control variables, with controls for industry, number of shares, return, nominal value, and market capitalization. Table IV shows a significant and positive relationship between advertisements placed in The Times and coverage in the main body of the newspaper over the subsequent year. Results suggest that placing ten advertisements in one year is associated with an additional article for that company in the following year. This finding is robust to excluding railway companies and using different time periods, with the advertising effect increasing in the post-1850 period. Table IV Determinants of company coverage in The Times, 1825–70 We use two-stage GMM estimation (Hansen, 1982). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. The dependent variable is Media, which is the number of news articles on a company reported in The Times in year t. Advertising is the number of advertisements placed in The Times by a company in year t − 1. Number of shares is the average natural log of number (in 000,000’s) of shares outstanding for a company in year t − 1. Industry controls are a series of dummy variables to capture industry effects. Share denomination is the nominal value of shares in £s for a company. Market capitalization is the natural log of market value of a company in £millions in year t − 1. Absolute return is the previous year’s absolute return. Volatility is the previous year’s standard deviation in returns. All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 Table IV Determinants of company coverage in The Times, 1825–70 We use two-stage GMM estimation (Hansen, 1982). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. The dependent variable is Media, which is the number of news articles on a company reported in The Times in year t. Advertising is the number of advertisements placed in The Times by a company in year t − 1. Number of shares is the average natural log of number (in 000,000’s) of shares outstanding for a company in year t − 1. Industry controls are a series of dummy variables to capture industry effects. Share denomination is the nominal value of shares in £s for a company. Market capitalization is the natural log of market value of a company in £millions in year t − 1. Absolute return is the previous year’s absolute return. Volatility is the previous year’s standard deviation in returns. All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 All companies Excluding railways (1) (2) (3) (4) 1825–70 1825–50 1851–70 1825–70 Advertising 0.101*** 0.312*** 0.094*** 0.101*** (0.024) (0.054) (0.022) (0.023) Number of shares 0.367*** 0.490*** 0.461*** 0.353*** (0.059) (0.080) (0.095) (0.060) Share denomination 0.002 −0.000 0.007*** 0.002* (0.001) (0.002) (0.001) (0.001) Market capitalization 0.110** 0.317*** −0.202 0.111** (0.047) (0.098) (0.067) (0.048) absolute return 0.903 4.634 −11.68 0.857 (2.740) (4.656) (5.137) (3.376) Volatility 0.130 −1.524 1.454 0.149 (1.100) (1.670) (1.493) (1.106) Constant 2.194*** 3.028*** 1.950*** 2.142*** (0.208) (0.303) (0.293) (0.215) Industry controls Yes Yes Yes Yes Observations 6,911 3,479 3,280 5,607 The coefficient on the NShares variable in Table IV variable indicates that the greater the number of issued shares which a company had, the greater the likelihood that it was covered in the press. This finding is consistent with our hypothesis that companies with more diffuse and arm’s-length ownership were more likely to be covered in The Times. The results in Table IV suggest that larger companies, as proxied by the market capitalization variable, were more likely to be covered by the media, which is a similar finding to that of Bignon and Miscio (2010). Notably, a stock’s absolute return is not a covariate of media coverage, suggesting that the media were not more likely to report on stocks that were performing particularly well or poorly. The above results suggest that the media is responding to the market need for additional information on those companies with diffuse ownership. This additional information could encourage more investors to buy a stock and enable companies to further increase their share ownership. To test if there is feedback between media coverage and share ownership, we assessed the relationship between number of shares and lagged media coverage in a GMM framework, controlling for additional variables as per Equation (1). We find that there is a significant relationship between previous media coverage and present number of shares on the market. Thus, there appears to be a feedback between media coverage and market participation. To understand the factors that precede the first occurrence of media coverage for companies, we analyzed the time series of annual company data. We find that there is little time-series variation in the number of shares at the company level, with changes occurring in only 2.87% of observations. The likelihood of this preceding the start of media coverage is very small—in only 0.10% of observations was there a change in the number of shares followed by the first occurrence of media coverage within the next year. Other corporate policy decisions were similarly unlikely to be followed by initial media coverage, for example, in only 0.17% of observations was there a change in dividends followed by the first occurrence of media coverage. 5. Did Media Affect Returns? If the press is increasing the information available to investors, it reduces the investor recognition problem for covered stocks, which should in turn result in lower returns for such stocks relative to stocks which are not covered (Fang and Peress, 2009). We initially used a broad definition of media coverage which views media coverage as advertisements plus media reports on companies. We do this for a theoretical and a practical reason. The theoretical reason for doing this is that advertisements in newspapers may have aided investor recognition just as easily as press reporting on companies and the only difference was that advertisements were simply coverage which was paid for by the company. The practical reason is that prior to 1846, there are insufficient companies with media coverage that are not advertisements to facilitate a statistically robust portfolio analysis. However, for the sake of robustness, we excluded advertisements from our definition of media coverage and our conclusions do not differ. To test if media coverage affected returns at the cross-sectional level, we formed portfolios of stocks based on media coverage in the prior year. At the beginning of each year, we divided our sample into companies with media coverage and those without. The monthly performance of each portfolio is then assessed over the next 12 months.3 This process is repeated for each of the years between 1826 and 1870. We then calculated the differential returns between the two portfolios during our sample period and this is our measure for the media effect. Figure 2 shows the 24-month moving average of differential returns between the media and no-media portfolios between 1826 and 1870. Although there appears to be cyclicality in the media effect, the long-run trend demonstrates that higher returns gave way to lower returns from the late-1840s for companies covered by the media. For example, the value-weighted media portfolio outperforms the no-media portfolio by 8.4 basis points per month between 1826 and mid-1848. However, it underperforms the no-media portfolio by 29.4 basis points per month post mid-1848. Figure 2 View largeDownload slide The 24-month moving average differential returns between companies with and without media coverage, LSE, 1828–70. Based on 580 companies listed on the COE. Portfolios of stocks are formed based on media coverage in The Times in the prior year. At the beginning of each year, we divided our sample into companies with media coverage and those without. The monthly performance of each portfolio is then assessed over the next 12 months. We then calculated the differential returns between the two portfolios as media coverage minus no media coverage. Differential returns are based on the listwise method for treating missing prices. Figure 2 View largeDownload slide The 24-month moving average differential returns between companies with and without media coverage, LSE, 1828–70. Based on 580 companies listed on the COE. Portfolios of stocks are formed based on media coverage in The Times in the prior year. At the beginning of each year, we divided our sample into companies with media coverage and those without. The monthly performance of each portfolio is then assessed over the next 12 months. We then calculated the differential returns between the two portfolios as media coverage minus no media coverage. Differential returns are based on the listwise method for treating missing prices. In Table V, we report the statistical significance of the media effect for the whole sample period as well as in the two sub-periods.4 In addition, in order to examine whether the differential returns between media and no media portfolios can be attributed to different level of risks, we calculated the risk-adjusted differential returns (i.e., alphas for the portfolio that long media stocks and short no-media stocks), using three classic asset pricing models: CAPM, Fama–French three-factor model, and Carhart four-factor model. The SMB and HML factors are constructed following Fama and French (1993) and the WML factor is constructed following Carhart (1997).5 In order to control for a possible bias in the estimation of the risk loadings due to the thin-trading problem, we report the results for the risk-adjusted returns where the bias is corrected using Dimson’s (1979) method. Table V Testing the media effect: the difference between returns of companies covered and not covered by the media (%) At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. Raw differential return represents the differential monthly returns between the media and no-media stocks. We then regress this raw differential return series against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The SMB and HML factors are constructed following Fama and French (1993) except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). We use three different treatments for missing stock prices. In Panel A, missing prices were assumed to be the same as last available price. In Panel B, in observations where stock prices were missing, we filled in the total returns with the mean returns of the same stock over the sample period. In Panel C, observations with missing prices are deleted and all calculations only use the remaining observations. Panel D uses the same assumption about missing prices as Panel A, but the delisting bias was adjusted. ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Table V Testing the media effect: the difference between returns of companies covered and not covered by the media (%) At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. Raw differential return represents the differential monthly returns between the media and no-media stocks. We then regress this raw differential return series against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The SMB and HML factors are constructed following Fama and French (1993) except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). We use three different treatments for missing stock prices. In Panel A, missing prices were assumed to be the same as last available price. In Panel B, in observations where stock prices were missing, we filled in the total returns with the mean returns of the same stock over the sample period. In Panel C, observations with missing prices are deleted and all calculations only use the remaining observations. Panel D uses the same assumption about missing prices as Panel A, but the delisting bias was adjusted. ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Equally-weighted portfolios Value-weighted portfolios Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Jan. 1826– Dec. 1870 Jan. 1826– Jun. 1848 Jul. 1848– Dec. 1870 Panel A: Zero-return method Raw differential return 0.013 0.022 0.004 −0.059 0.127 −0.246** (0.093) (0.137) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.031 0.088 −0.148 −0.099 0.176* −0.372*** (0.077) (0.118) (0.100) (0.067) (0.106) (0.079) FF three-factor alpha −0.017 0.097 −0.147 −0.084 0.152 −0.340*** (0.079) (0.121) (0.102) (0.069) (0.107) (0.078) Carhart four-factor alpha −0.011 0.135 −0.157 −0.092 0.145 −0.349*** (0.079) (0.124) (0.103) (0.068) (0.108) (0.078) Panel B: Mean return method Raw differential return 0.001 0.030 −0.028 −0.059 0.143 −0.262** (0.079) (0.114) (0.111) (0.082) (0.125) (0.106) CAPM alpha −0.040 0.085 −0.161* −0.102 0.181* −0.390*** (0.062) (0.093) (0.084) (0.063) (0.097) (0.075) FF three-factor alpha −0.023 0.087 −0.143* −0.090 0.167* −0.362*** (0.064) (0.094) (0.085) (0.064) (0.098) (0.074) Carhart four-factor alpha −0.016 0.117 −0.157* −0.098 0.160 −0.370*** (0.064) (0.096) (0.086) (0.064) (0.099) (0.074) Panel C: Listwise method Raw differential return −0.144 −0.040 −0.249* −0.105 0.084 −0.294*** (0.106) (0.153) (0.147) (0.091) (0.143) (0.111) CAPM alpha −0.191** 0.037 −0.413*** −0.138* 0.143 −0.420*** (0.088) (0.127) (0.122) (0.074) (0.115) (0.087) FF three-factor alpha −0.177** 0.032 −0.398*** −0.132* 0.116 −0.391*** (0.090) (0.129) (0.124) (0.075) (0.116) (0.086) Carhart four-factor alpha −0.164* 0.071 −0.422*** −0.137* 0.117 −0.400*** (0.090) (0.132) (0.125) (0.075) (0.117) (0.086) Panel D: Delisting-adjusted (Zero-return method) Raw differential return 0.026 0.069 −0.018 −0.030 0.157 −0.218** (0.094) (0.139) (0.126) (0.086) (0.133) (0.108) CAPM alpha −0.020 0.137 −0.174* −0.070 0.206* −0.344*** (0.078) (0.120) (0.100) (0.068) (0.106) (0.079) FF three-factor alpha −0.010 0.141 −0.179* −0.055 0.180* −0.311*** (0.080) (0.123) (0.102) (0.069) (0.108) (0.078) Carhart four-factor alpha −0.003 0.183 −0.190* −0.063 0.173 −0.321*** (0.080) (0.125) (0.103) (0.069) (0.109) (0.078) Following Ye and Turner (2014), we used three different treatments for missing stock prices. First, we assumed missing prices were the same as the last available price. We call this the zero-return method. Second, in the listwise method, observations with missing prices are deleted and all calculations only use the remaining observations. Finally, in the mean return method, we filled in the total returns of the observations when prices were missing with the mean returns of the same stock over the sample period. When stocks were delisted, they disappear from our dataset. When delisting was the result of bankruptcy rather than name changes, mergers, or listing migrations to regional exchanges, shareholders potentially suffered large losses, which are not captured. The difficulty in identifying the cause of delisting is highlighted by Ye and Turner (2014). If the reason for delisting is unknown, we assume that the reason for delisting was bankruptcy. We assigned a −40% return to all stocks on the month following delisting, following the assumption made by Ye and Turner (2014). As the delisting adjustment does not affect our main findings, we focus our discussion on the results with no adjustment for delisting bias unless otherwise stated. Table V shows that, with the exception of the listwise method, there is no statistical difference in return differentials when we focus on the overall period. However, consistent with our hypothesis, we see that in the first half of our sample period, there is little evidence of a media effect, whereas in the second half of our sample period, the results in Table V show that companies with media coverage tended to have much lower returns. Furthermore, the differential returns in the second half of our sample period become even more negative after adjusting for the different level of risks in the media and no-media portfolios. Between 1848 and 1870, the magnitudes of the risk-adjusted differential returns are in the range 0.311–0.422. The scale of the media effect is comparable to modern markets, where Fang and Peress (2009) found that no-media stocks outperform media stocks by about 3.0% on an annual basis after adjusting for known risk factors. We find the media effect for the period 1848–70 is slightly higher at 3.79%–5.18%.6 We also analyzed the performance as well as the risk loadings of media and no-media portfolios separately. The results are reported in Table VI.7 Consistent with Fang and Peress (2009), the media effect is more likely to be driven by companies not covered by the media having abnormally high returns rather than media covered companies having abnormally low returns. For example, the alphas for the no-media portfolios are significantly positive but those for the media portfolios are not negative. In the value-weighted portfolio returns, alphas for the media portfolio are not significantly different from zero, suggesting that they can be justified by their risk structure. The risk loadings reported in Table VI suggest that, relative to the media covered stocks, the no-media stocks tend to have lower market risk, greater SMB and WML loadings, and smaller HML loadings. Apart from the loadings on the HML factor, these results are also consistent with the results in Fang and Peress (2009). Table VI Returns, alphas, and risk loadings of media and no-media stock portfolios At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. We then regress the return for each portfolio against several classic risk factors. Raw return represents the monthly returns for the media and no-media stocks. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models, including CAPM, FF three-factor and Carhart four-factor models, respectively. Market beta, SMB, HML, and WML are the coefficients on market factor, SMB, HML, and WML factors in these risk models. Media column reports the returns, alphas, and risk loadings for the portfolio with media covered stocks. No-media column reports the returns, alphas, and risk loadings for the portfolio with stocks that were not covered by media. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. The SMB and HML factors are constructed following Fama and French (1993), except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 Table VI Returns, alphas, and risk loadings of media and no-media stock portfolios At the beginning of each year between 1826 and 1870, we sort stocks into two portfolios based on the media coverage in the prior year and calculate the monthly returns for each portfolio during the next 12 months. We then regress the return for each portfolio against several classic risk factors. Raw return represents the monthly returns for the media and no-media stocks. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models, including CAPM, FF three-factor and Carhart four-factor models, respectively. Market beta, SMB, HML, and WML are the coefficients on market factor, SMB, HML, and WML factors in these risk models. Media column reports the returns, alphas, and risk loadings for the portfolio with media covered stocks. No-media column reports the returns, alphas, and risk loadings for the portfolio with stocks that were not covered by media. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. The SMB and HML factors are constructed following Fama and French (1993), except that we use the dividend price ratio to proxy the book-to-market. The WML factor is constructed following Carhart (1997). Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 Equally-weighted portfolios Value-weighted portfolios No-media Media No-media Media Panel A: Raw differential returns Raw return 0.667*** 0.522*** 0.506*** 0.401*** (0.065) (0.128) (0.063) (0.118) Panel B: Alphas and risk loadings in CAPM CAPM alpha 0.357*** 0.166** 0.190*** 0.052 (0.048) (0.076) (0.033) (0.051) Market beta 0.589*** 1.405*** 0.707*** 1.258*** (0.040) (0.062) (0.027) (0.042) R-squared 0.451 0.650 0.725 0.812 Panel C: Alphas and risk loadings in FF three-factor model FF three-factor alpha 0.303*** 0.126* 0.188*** 0.057 (0.044) (0.076) (0.034) (0.052) Market beta 0.699*** 1.468*** 0.730*** 1.238*** (0.041) (0.071) (0.031) (0.048) SMB 0.434*** 0.224** 0.101** −0.121* (0.062) (0.107) (0.047) (0.073) HML 0.048 0.105* −0.061** 0.057 (0.034) (0.059) (0.026) (0.040) R-squared 0.563 0.662 0.734 0.815 Panel D: Alphas and risk loadings in Carhart four-factor model Carhart four-factor alpha 0.304*** 0.140* 0.191*** 0.055 (0.045) (0.076) (0.034) (0.052) Market beta 0.691*** 1.421*** 0.728*** 1.242*** (0.043) (0.073) (0.033) (0.050) SMB 0.439*** 0.235** 0.106** −0.130* (0.063) (0.106) (0.047) (0.073) HML 0.044 0.081 −0.048* 0.049 (0.036) (0.062) (0.027) (0.042) WML −0.024 −0.114* 0.027 −0.002 (0.037) (0.063) (0.028) (0.043) R-squared 0.561 0.667 0.733 0.820 There are two possible mechanisms through which the media effect may emerge and persist: Merton’s (1987) investor recognition mechanism and Miller's (1977) impediments-to-trade mechanism. In the investor recognition hypothesis, stocks with lower investor recognition need to offer higher returns to compensate their holders for being imperfectly diversified. Because media coverage can broaden investors’ recognition, it reduces the returns on covered stocks relative to noncovered stocks. To investigate this hypothesis, we double-sorted companies by media coverage and several company characteristics.8 As pointed out by Chichernea, Ferguson, and Kassa (2015), neglected stocks are, in general, smaller and have higher idiosyncratic volatility relative to more visible stocks. Therefore, for each year, we double-sorted companies based on their prior year’s media coverage and their size or idiosyncratic volatility in the year. The size of a stock was proxied by its market capitalization. The idiosyncratic volatility for each stock was constructed following Ang et al. (2006).9 In Panels A and B of Table VII, we report the monthly raw and risk-adjusted differential returns from the double-sorted portfolios. We find that the media effects are much stronger for smaller stocks and for stocks with higher idiosyncratic volatility. These results suggest that media coverage among less recognized companies has a greater effect on stock returns. This is consistent with Merton’s (1987) investor recognition hypothesis. Table VII The media effect among stocks with different characteristics At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for one of several company characteristics (e.g., size, idiosyncratic volatility, liquidity, and nominal value). The size of a stock is proxied by its market capitalization at the end of year. The idiosyncratic volatility for each stock is constructed following Ang et al. (2006). Based on Lesmond, Ogden, and Trzcinka (1999), we approximate the zero-return measure of liquidity at each year for each stock by dividing the number of months with nonzero return by the number of months in the year. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw differential return represents the differential monthly return between media and no-media portfolios among each group of stocks. We then regress this raw differential return against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) Table VII The media effect among stocks with different characteristics At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for one of several company characteristics (e.g., size, idiosyncratic volatility, liquidity, and nominal value). The size of a stock is proxied by its market capitalization at the end of year. The idiosyncratic volatility for each stock is constructed following Ang et al. (2006). Based on Lesmond, Ogden, and Trzcinka (1999), we approximate the zero-return measure of liquidity at each year for each stock by dividing the number of months with nonzero return by the number of months in the year. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw differential return represents the differential monthly return between media and no-media portfolios among each group of stocks. We then regress this raw differential return against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) Equally-weighted portfolios Value-weighted portfolios Raw differential return CAPM alpha FF three- factor alpha Carhart four-factor alpha Raw differential return CAPM alpha FF Three- Factor Alpha Carhart Four-Factor Alpha Panel A: By size Small −0.243 −0.300* −0.290* −0.260 −0.429** −0.497*** −0.512*** −0.491** (0.178) (0.169) (0.173) (0.174) (0.187) (0.172) (0.177) (0.177) Large −0.150 −0.184** −0.193** −0.197** −0.122 −0.151** −0.154** −0.162** (0.102) (0.088) (0.089) (0.089) (0.092) (0.077) (0.078) (0.077) Panel B: By idiosyncratic volatility Low idiosyncratic volatility 0.015 −0.039 0.004 0.022 0.003 −0.046 −0.023 0.005 (0.107) (0.091) (0.092) (0.092) (0.108) (0.093) (0.095) (0.093) High idiosyncratic volatility −0.287** −0.323** −0.295** −0.289** −0.238* −0.256** −0.243** −0.256** (0.140) (0.129) (0.132) (0.133) (0.123) (0.116) (0.119) (0.119) Panel C: By liquidity Low liquidity −0.177 −0.206* −0.211* −0.191 −0.117 −0.146 −0.174 −0.179 (0.123) (0.123) (0.126) (0.126) (0.112) (0.111) (0.113) (0.112) High liquidity −0.276** −0.301** −0.288** −0.278** −0.153* −0.175** −0.179** −0.180** (0.124) (0.118) (0.120) (0.120) (0.092) (0.084) (0.085) (0.085) Panel D: By nominal value Low nominal value −0.044 −0.084 −0.090 −0.071 −0.143 −0.170 −0.178 −0.171 (0.168) (0.161) (0.165) (0.165) (0.145) (0.137) (0.140) (0.140) High nominal value −0.151 −0.200** −0.171* −0.163* −0.080 −0.114 −0.115 −0.120 (0.116) (0.095) (0.098) (0.098) (0.099) (0.083) (0.084) (0.084) If the premium for no-media stocks represents mispricing, arbitrageurs can eliminate the premium only if there are no significant impediments-to-trade (Miller, 1977). Thus, it may be that no-media stocks have greater trading impediments, which means that the mispricing cannot be exploited by traders and that the media effect does not disappear. We assessed this possibility by double sorting portfolios by media coverage and two measures of trading impediments. First, we used a stock’s nominal value to approximate the impediments to trade. Low nominal value stocks in this era had higher trading costs and thus greater impediments-to-trade (Acheson, Turner, and Ye, 2012, p. 870). Our second measure of trading impediments is stock liquidity. Based on Lesmond, Ogden, and Trzcinka (1999), we used the zero-return measure of liquidity for each year for each stock by dividing the number of months with nonzero return by the number of months in the year. Panels C and D in Table VII show that in our sample, the media effect is stronger for low-trading-impediments stocks rather than high-trading-impediments stocks using both the zero-return liquidity and nominal value proxies. These findings are inconsistent with the impediments-to-trade hypothesis.10 6. Ownership Diffusion and Media Effect The results in Table V suggest that the media effect emerged in the second half of the sample period. We argue that this media effect appears at this point in time because corporate ownership in the UK had become diffuse and arm’s-length, and therefore the role that media played in increasing investor recognition for covered stocks became more important in influencing the relative return between media and no-media stocks. In order to obtain corroborating evidence for this conjecture, we conducted two types of analysis. First, we double sorted our sample stocks based on media coverage and ownership diffusion in order to investigate whether the media effect has any cross-sectional relation with a stock’s degree of ownership diffusion. If diffuse ownership is a necessary condition for the emergence of the media effect, we should observe that the media effect only exists or is much stronger in stocks with high ownership diffusion. Second, in a time-series regression analysis, we tested whether media stocks’ relative degree of diffuse ownership can explain away the media effect. Unfortunately, systematic evidence on corporate ownership structure or number of shareholders in this era is sporadic (Acheson et al., 2015). Instead, we have to rely on a proxy for ownership structure. The proxy we used is the number of shares companies issued because this gives some idea about how many shareholders the company wished to hold their stock and the diffuseness of ownership. In order to show that shares outstanding is associated with diffuse ownership, we collected data on the number of shareholders for all English banks in 1850, 1860, and 1870 from the relevant issue of the Banking Almanac and Yearbook. Data for the number of railway shareholders is only available for 1855 from a special report commissioned by the UK Parliament (Parliamentary Papers, 1856). Companies which registered after 1856 had to produce an annual list of shareholders under UK company legislation. Fortunately, some of these lists have been preserved in the National Archives in London. We obtained pre-1870 ownership records for forty-three companies traded on the LSE. Notably, these records permit us to calculate ownership dispersion as well as the number of shareholders. In terms of English banks in 1850, 1860, and 1870, the correlation between the number of issued shares and number of shareholders is 0.72, 0.61, and 0.69, respectively. For the fifty railways in our sample in 1855, the correlation between issued shares and number of shareholders is 0.75. For the miscellaneous forty-three companies, the correlation coefficient was 0.84. In addition, the correlation between the capital ownership of the top five and ten shareholders and number of issued shares was −0.46 and −0.50, respectively, suggesting that diffuse ownership structure was correlated with a greater number of issued shares. In Table VIII, where we display the returns from the four portfolios double sorted on ownership diffusion and media coverage, we see that the media effect only exists in stocks with high ownership diffusion. This suggests that when a stock’s ownership diffusion is low, media coverage has no effect on the stock’s return. In contrast, when a stock has a diffused ownership structure, its return/alphas become much lower if the company is covered by the press. In addition, from Table VIII we can see that the media portfolios’ alphas are not significantly different from zero for the stocks with high ownership diffusion. This suggests that due to increased investor recognition, investors no longer require higher returns for the companies covered by the press. This is consistent with our conjecture that arm’s-length ownership is a pre-condition for the media effect. Table VIII Ownership diffusion and the media effect: cross-sectional analysis At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for the sample stocks’ ownership diffusion in the year. The ownership diffusion is proxied by the number of shares of a company’s stock. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw return represents the monthly returns for the media and no-media portfolios among each group of stocks. We then regress the raw returns against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. Media column reports the returns and alphas for the portfolio with media covered stocks. No-media column reports the returns and alphas for the portfolio with stocks that were not covered by media. DIFF column reports the differential returns and alphas between the media and no-media portfolios. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) Table VIII Ownership diffusion and the media effect: cross-sectional analysis At the beginning of each year, we divide our sample stocks into two groups based on the 50% cutoff rate for the sample stocks’ ownership diffusion in the year. The ownership diffusion is proxied by the number of shares of a company’s stock. We then further divide each group of stocks into two portfolios: one with stocks covered in the prior year and the other with stocks not covered in the prior year. Raw return represents the monthly returns for the media and no-media portfolios among each group of stocks. We then regress the raw returns against several classic risk factors. CAPM alpha, Fama and French (FF) three-factor alpha, and Carhart four-factor alpha are the constants in the relevant asset pricing models including CAPM, FF three-factor and Carhart four-factor models, respectively. Media column reports the returns and alphas for the portfolio with media covered stocks. No-media column reports the returns and alphas for the portfolio with stocks that were not covered by media. DIFF column reports the differential returns and alphas between the media and no-media portfolios. The returns and alphas in this table are calculated using the listwise method of dealing with missing stock prices. In this method, observations with missing prices are deleted and all calculations only use the remaining observations. Standard errors in parentheses and ***p < 0.01, **p < 0.05, *p < 0.1. Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) Equally-weighted portfolios Value-weighted portfolios No-media Media DIFF No-media Media DIFF PANEL A: Raw returns Low ownership diffusion 0.559*** 0.593*** 0.032 0.464*** 0.458*** −0.005 (0.057) (0.120) (0.118) (0.047) (0.110) (0.107) High ownership diffusion 0.824*** 0.482*** −0.342*** 0.548*** 0.388*** −0.159* (0.098) (0.150) (0.121) (0.081) (0.125) (0.093) Panel B: CAPM alpha Low ownership diffusion 0.263*** 0.271** 0.006 0.169*** 0.148 −0.021 (0.052) (0.105) (0.114) (0.040) (0.095) (0.103) High ownership diffusion 0.495*** 0.115 −0.379*** 0.220*** 0.036 −0.184** (0.069) (0.091) (0.111) (0.043) (0.054) (0.082) Panel C: FF three-factor alpha Low ownership diffusion 0.203*** 0.259** 0.055 0.118*** 0.154 0.038 (0.050) (0.107) (0.117) (0.039) (0.096) (0.104) High ownership diffusion 0.443*** 0.074 −0.369*** 0.240*** 0.041 −0.199** (0.065) (0.092) (0.112) (0.043) (0.055) (0.083) Panel D: Carhart four-factor alpha Low ownership diffusion 0.205*** 0.265** 0.059 0.125*** 0.160 0.036 (0.050) (0.107) (0.118) (0.038) (0.097) (0.105) High ownership diffusion 0.443*** 0.089 −0.354*** 0.242*** 0.038 −0.204** (0.066) (0.092) (0.112) (0.043) (0.055) (0.083) To further corroborate this finding, we form two portfolios based on media coverage to assess differences in portfolio number of shares and liquidity. Based on our prior argument, we would expect that the relative degree of ownership diffuseness for the media-covered stocks compared to the no-media stocks is negatively associated with the difference in their returns. More importantly, the media effect should disappear once the differential ownership diffusion between media-covered and no-media stocks is controlled for. For the sake of brevity, we do not report results, but our findings are consistent with our prior expectation. When controlling for both liquidity and ownership diffusion, only ownership diffusion is significantly correlated with differential returns, suggesting that liquidity is not correlated with the media effect. Consequently, ownership diffusion does not simply serve as a proxy for liquidity, suggesting that ownership diffusion goes some way to explain the existence of a media effect. 7. Conclusions The main finding of this article is that media coverage of stocks grows substantially after the emergence of arm’s-length and diffuse ownership in the UK from the mid-1840s onwards. We argue that the media were playing an important informational role for the new cadre of middle-class investors which emerged at this time and that the additional information generated by the press increased investor recognition for covered stocks. Consistent with this, after the mid-1840s, we find that companies not covered by the media had higher returns relative to media companies. In other words, as in modern developed country stock markets, there was a media effect in the nineteenth-century London market, but this only emerged after ownership became arm’s-length and diffuse. Therefore, our findings imply that arm’s-length and diffuse ownership may be a prerequisite for the media effect. Indeed, the absence of arm’s-length and diffuse ownership may explain why media appears to have little effect on developing country’s financial markets today (Griffin, Hirschey, and Kelly, 2011). Our findings suggest two avenues that could be explored by future scholars. First, our findings highlight the relationship between press reporting and advertisements. Future work could explore the nature of this relationship and whether it was insidious or benign. Second, newspaper reporting on financial markets in our period was factual, which means that an analysis of the tone or language used in newspaper reports is not possible. However, the development of the UK’s daily financial press in the 1880s and whether it influenced financial markets through its use of language is something that future work could explore. Footnotes * Thanks to the ESRC (RES-000-22-1391) for financial support. Thanks to Amit Goyal and an anonymous referee for their comments. Thanks to Graeme Acheson for his invaluable input to this project. Research assistance was provided by Lei Qu and Nadia Vanteeva. 1 Dyck and Zingales (2003) in their study highlight that the effect of media is more pronounced for companies with low analyst coverage. 2 Banking Almanac and Yearbook, 1844 and 1870. 3 To reduce the influence of outlier returns, we winsorized monthly stock returns at the 0.5 and 99.5 percentiles. 4 For the sake of brevity, we present results for the first and second half of our sample only with June/July 1848 being the mid-point. It should be noted that the hypothesized change to the media effect is not likely to be identifiable to a single date, and we have used alternative breakpoints with qualitatively similar results. 5 We construct Small and Big portfolios using the median market capitalization of stocks at December each year as the breakpoint. As book-to-market data are not available during our sample period, we construct High, Medium, and Low portfolios using the 30th and the 70th percentiles of the dividend price ratio at December as the breakpoints. The dividend price ratio is calculated as the sum of dividend paid in the year divided by the end-of-year stock price. From these, we get six intersection portfolios, namely, Small High, Small Medium, Small Low, Big High, Big Medium, and Big Low. SMB is the average return on the three small portfolios minus the average return on the three big portfolios. HML is the average return on the two high portfolios minus the average return on the two low portfolios. Zero-yielding stocks are excluded when constructing the SMB and HML factors. To construct the WML factor, at each month t, we construct Winner and Loser portfolios based on the 30th and 70th breakpoint of the 11-month returns between months t − 1 and t − 12. The difference between the equally weighted returns from the Winner portfolio and the Loser portfolio is our WML factor. 6 Because railways were the dominant sector on the equity market after the mid-1840s and because the Railway Mania of the mid-1840s may distort our findings, we checked whether our findings are robust to their exclusion. For the sake of robustness, we also looked at the difference between media and no-media portfolios using a narrower definition of media coverage, that is, one which excludes advertisements. 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Review of FinanceOxford University Press

Published: Apr 16, 2017

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