News Versus Sentiment: Predicting Stock Returns From News Stories
Steven L. Heston
University of Maryland – Department of Finance
Nitish Ranjan Sinha
Board of Governers of the Federal Reserve System
This paper uses a dataset of more than 900,000 news stories to test whether news can predict stock returns. We measure sentiment with a proprietary Thomson-Reuters neural network. We find that daily news predicts stock returns for only 1 to 2 days, confirming previous research. Weekly news, however, predicts stock returns for one quarter. Positive news stories increase stock returns quickly, but negative stories have a long delayed reaction. Much of the delayed response to news occurs around the subsequent earnings announcement.
News Versus Sentiment: Predicting Stock Returns From News Stories – Introduction
Textual information processing has become a growing part of financial practice. Duhigg (2006) and Ro (2012) write about general artificial intelligence for stock picking, while Lo (1994) reviews neural networks. Specific applications include bankruptcy prediction Atiya (2001), corporate distress diagnosis Altman, Marco, and Varetto (1994), and consumer credit risk Khandani, Kim, and Lo (2010). While industry has led the applications, academic empirical research is increasingly confirming the value of textual analysis. Tetlock’s pioneering studies ((Tetlock, Saar-Tsechansky, and Macskassy 2008) and (Tetlock 2007)) demonstrate that news stories contain information relevant to predicting both earnings and stock returns. Subsequent studies have applied similar techniques with a variety of news sources. Researchers have generally found that textual information can briefly predict returns at the aggregate market level ( (Tetlock 2007), (Dougal, Engelberg, García, and Parsons 2012), (Garcia 2013) and Dzielinski and Hasseltoft (2013)) as well at the individual stock level ( (Boudoukh, Feldman, Kogan, and Richardson 2013), (Sinha 2016) and (Chen, De, Hu, and Hwang 2014)). However, the research has been limited to a comparatively narrow event window, and has not shown significant predictability beyond two days after news release. In contrast, this paper uses a neural network to show that news stories can predict stock returns for up to 13 weeks.
The rapid growth of this empirical research has entailed the use of different datasets and methodologies. For example, Tetlock, Saar-Tsechansky, and Macskassy (2008) uses a broad sample of Wall Street Journal and Dow Jones News Service articles, whereas Loughran and McDonald (2011) use more specialized 10-K filings. Similarly, Garcia (2013) analyzes New York Times articles, whereas Jegadeesh and Wu (2013) also examine 10-K’s, Lerman and Livnat (2010) uses 8-K’s, and Chen et al (2014) use social media. These conflicting choices confound the type of source documents used for the textual analysis with the type of textual processing. In particular, it begs the question of whether textual processing can effectively predict stock returns based on a broad set of text sources.
In addition to methodological differences, empirical studies have found different types of predictability in applications at the aggregate market level or the individual stock level. Early work by Tetlock (2007) finds that short-term return predictability is quickly reversed at the market level. Loughran and McDonald (2011) find greater response for individual stocks within a multi-day event window. Garcia (2013) and Jegadeesh and Wu (2013) also find different results with market returns and individual stocks, respectively. More recently, Hillert, Jacobs, and Müller (2014) suggest that media overreaction underlies stock momentum. Hagenau, Hauser, Liebmann, and Neumann (2013) measure news momentum to predict CDAX index returns, and Uhl, Pedersen, and Malitius (2015) aggregate sentiment for tactical asset allocation. In addition to aggregate market returns versus individual stocks, differences might stem from different source of text, or different methodologies. The duration and reversal of return predictability are important because the economic interpretation of news depends on whether there is a permanent news impact or a transient impact. Permanent news impact would suggests news as information on the other hand transient news impact would suggest news as sentiment. As Tetlock (2007) summarizes, “The sentiment theory predicts short-horizon returns will be reversed in the long run, whereas the information theory predicts they will persist indefinitely.”
This paper examines stock return predictability using a sophisticated neural network.1 It applies these techniques on a large common set of Reuters news releases. We find that the neural network appears to extract permanent information that is not fully impounded into current stock prices.
The duration of return predictability depends critically on the portfolio formation procedure. Previous research by Tetlock, Saar-Tsechansky, and Macskassy (2008), Loughran and McDonald (2011), and Lerman and Livnat (2010) has established a short-term response of stock prices to news. We also find that stocks with positive (negative) news over one day have subsequent predictably high (low) returns for 1 to 2 days. But going beyond the published literature, we find that aggregating news over one week produces a dramatic increase in predictability of returns. Stocks with news over the past week have predictable returns for up to 13 weeks, which is true even for stocks with only one news event per week. The difference in return predictability depending on the aggregation horizon shows that it is important to gauge relative news sentiment by examining news over longer horizon rather than just one day of stories.
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