We study portfolio stock return behavior that exhibits both a positive autocorrelation over short horizons and a negative autocorrelation over long horizons. These autocorrelations are more significant in small size portfolios. Among various forms of temporary components in stock prices, an AR(2) component is the simplest model compatible with this pattern of returns, which yields an ARMA(2,2) model of stock returns. We show that the significance of this model is that it requires the presence of feedback trading, which is a form of irrational trades, and the market's slow adjustment to the market fundamentals, which is consistent with recent modelings of stock prices. We find that the variation of the temporary component becomes greater as the firm size gets smaller. This implies that the deviation from the market fundamentals is larger in small size portfolios than in large size portfolios.
Review of Quantitative Finance and Accounting – Springer Journals
Published: Oct 13, 2004
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera