Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction

Andrew Sun, Michael Lachanski () and Frank Fabozzi ()

International Review of Financial Analysis, 2016, vol. 48, issue C, 272-281

Abstract: We investigate the potential use of textual information from user-generated microblogs to predict the stock market. Utilizing the latent space model proposed by Wong et al. (2014), we correlate the movements of both stock prices and social media content. This study differs from models in prior studies in two significant ways: (1) it leverages market information contained in high-volume social media data rather than news articles and (2) it does not evaluate sentiment. We test this model on data spanning from 2011 to 2015 on a majority of stocks listed in the S&P 500 Index and find that our model outperforms a baseline regression. We conclude by providing a trading strategy that produces an attractive annual return and Sharpe ratio.

Keywords: Tweets; Social media text mining; Sparse matrix factorization; Stock market prediction (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (27)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:48:y:2016:i:c:p:272-281

DOI: 10.1016/j.irfa.2016.10.009

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