دانلود رایگان مقاله انگلیسی پراکندگی تمایلات بازار و اثرات آن بر بازده سهام و نوسانات - اشپرینگر 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 investor opinion on stock market; and recommending a supplementary investing approach using user-generated content.

Discussion and conclusion

In this study, we used both linear regression and SVR to show that sentiment dispersion contains information about the stock volatility and stock returns. Specially, sentiment dispersion raises the realized volatility on the same day, and then reduce the realized volatility on the following several days. Subsequent analysis shows that sentiment dispersion can provide additional predictive power to realized volatility. Different from what theory suggests, sentiment dispersion does not contain much information about the daily stock returns. As a robustness test, we also used Median Absolute Deviation to measure sentiment dispersion, and the result confirms our findings. The direct measuring of individual sentiment remedies current research gap and advance theories of financial behavior. The findings uncover the potential predictive power of sentiment dispersion and raise a new perspective to assess the impact of investor opinion on stock market. We showed the value of our proposed approach in extracting semantic sentiment of stock related tweets and evaluate their predictive power on stock volatility and returns. As literature (Varian 1985; Qian 2014; Miller 1977; Carlin et al. 2014) suggests, sentiment dispersion could contain information about future returns and volatility, and can be used to increase the prediction accuracy. It is practically important to forecast realized volatility because some derivatives (e.g. options) are priced based on it, and realized volatility acts as input in various finance models. From the results, we can see that all the experiments agree that sentiment dispersion is related to the realized volatility. Particularly, sentiment dispersion will increase the volatility, but the effects happen on the same day, and reduce the realized volatility on the next day. The effects on the stock returns take place more than three days. In addition, the number of tweets is found to be influential to the future stock volatilities and stock returns. Finally, the sentiment dispersion is also affected by the past stock returns and volatility. The effects are showed to be bidirectional, and future research can confirm and address this phenomenon.