From Efficient Markets Theory to Behavioral Finance

Wisdom of Crowds: The Value of Stock Opinions Transmitted through Social Media

Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis

Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.

Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective

Big data analytics for financial Market volatility forecast based on support vector machine

Scholars and practitioners alike increasingly recognize the importance of stock microblogs as they capture the market discussion and have predictive value for financial markets. This paper examines the extent to which stock microblog messages are related to financial market indicators and the mechanism leading to efficient aggregation of information. In particular, this paper investigates the information content of stock microblogs with respect to individual stocks and explores the effects of social influences on an interday and intraday basis. We collected more than 1.2 million stock-related messages (i.e., tweets) related to S&P 100 companies over a period of 7 months. Using methods from computational linguistics, we went through an elaborate process of message feature reduction, spam detection, language detection, and slang removal, which has led to an increase in classification accuracy for sentiment analysis. We analyzed the data on both a daily and a 15-min basis and found that the sentiment of messages is positively affected with contemporaneous daily abnormal stock returns and that message volume predicts 15-min follow-up returns, trading volume, and volatility. Disagreement in microblog messages positively influences stock features, both in interday and intraday analysis. Notably, if we give a greater share of voice to microblog messages depending on the social influence of microbloggers, this amplifies the relationship between bullishness and abnormal returns, market volume, and volatility. Following knowledgeable investors advice results in more power in explaining changes in market features. This offers an explanation for the efficient aggregation of information on microblogging platforms. Furthermore, we simulated a set of trading strategies using microblog features and the results suggest that it is possible to exploit market inefficiencies even when transaction costs are included. To our knowledge, this is the first study to comprehensively examine the association between the information content of stock microblogs and intraday stock market features. The insights from the study permit scholars and professionals to reliably identify stock microblog features, which may serve as valuable proxies for market sentiment and permit individual investors to make better investment decisions.

Pieter Jan van Rees

Papers

More than just noise? Examining the information content of stock microblogs on financial markets