Market sentiment dispersion and its effects on stock return and volatility
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RESEARCH PAPER
Market sentiment dispersion and its effects on stock return and volatility Eric. W. K. See-To 1 & Yang Yang 1
Received: 4 July 2016 / Accepted: 15 March 2017 # Institute of Applied Informatics at University of Leipzig 2017
Abstract Behavioral economics has revealed that investor sentiment can profoundly affect individual behavior and decision-making. Recently, the question is no longer whether investor sentiment affects stock market valuation, but how to directly measure investor sentiment and quantify its effects. Before the era of big data, research uses proxies as a mediator to indirectly measure investor sentiment, which has proved elusive due to insufficient data points. In addition, most of extant sentiment analysis studies focus on institutional investors instead of individual investors. This is despite the fact that United States individual investors have been holding around 50% of the stock market in direct stock investments. In order to overcome difficulties in measuring sentiment and endorse the importance of individual investors, we examine the role of individual sentiment dispersion in stock market. In particular, we investigate whether sentiment dispersion contains information about future stock returns and realized volatility. Leveraging on development of big data and recent advances in data and text mining techniques, we capture 1,170,414 data points from Twitter and used a text mining method to extract sentiment and applied both linear regression and Support Vector Regression; found that individual sentiment dispersion contains information about stock realized volatility, and can be used to increase the prediction accuracy. We expect our results contribute to extant theories of electronic market financial behavior by directly measuring the individual sentiment dispersion; raising a new perspective to assess the impact of
Responsible Editor: Eric Ngai * Eric. W. K. See-To [email protected]
1
The Hong Kong Polytechnic University, Hong Kong, Hong Kong
investor opinion on stock market; and recommending a supplementary investing approach using user-generated content. Keywords Investor sentiment . Text mining . Return and volatility predictability JEL Classification C55 . C53 . C52
Introduction The idea of investor sentiment dates back to mid-twentieth when Keynes (1936) proposed that markets are influenced by investors’ Banimal spirits^, causing prices to deviate from fundamentals. This idea is formalized by De Long et al. (1990), who theoretically demonstrated that sentiment changes can lead to noise trading and excessive volatility. Now, the question is no longer whether investor sentiment affects stock market valuation, but how to directly measure investor sentiment and quantify its effects. Extant studies identified two kinds of sentiment measures (Lee et al. 1991; Neal and Wheatley 1998; Brown and Cliff 2004). The first sentiment measures are derived from surveys while the second measures relied on objective variables that correlate with investor sentiment. Both of the
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