How to measure the quality of financial tweets

How to measure the quality of financial tweets 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

How to measure the quality of financial tweets

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Publisher
Springer Netherlands
Copyright
Copyright © 2015 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-015-0229-6
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Quality & QuantitySpringer Journals

Published: Jul 14, 2015

References

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