Twitter text data may be very useful to evaluate from a different perspective financial tangibles, such as share prices, as well as intangible assets, such as company reputation. While twitter data are becoming widely available to researchers, methods aimed at selecting reliable twitter data are, to our knowledge, not yet available. To overcome this problem, and allow to employ twitter data for descriptive and predictive purposes, in this contribution we propose an effective statistical method that formalises and extends a quality index employed in the context of the evaluation of academic research, the h index, renamed T index. Our proposal will be tested on a list of twitterers described by the Financial Times as “the top financial tweeters to follow”, for the year 2013. Using our methodology we rank these twitterers and provide confidence intervals to decide whether they are significantly different. Moreover through a sentiment analysis, we employ the twitters content to estimate graphical models useful in the context of financial systemic risk. To this aim we focus on the Italian bank system and we show how listed banks are connected on the basis of tweets data.
Quality & Quantity – Springer Journals
Published: Jul 14, 2015
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