This In Practice piece gives a practitioner’s perspective on the article “News vs. Sentiment: Predicting Stock Returns from News Stories,” by Steven L. Heston and Nitish Ranjan Sinha, published in the Third Quarter 2017 issue of the Financial Analysts Journal.
What’s the Investment Issue?
Although few investors would argue that news has value, the big questions are, how much and for how long? This study explores the extent to which media articles predict stock returns and whether news moves stock prices over the long term or only in the short term. It also seeks to discover whether positive or negative news is more predictive of returns.
Previous studies are contradictory, probably because they used different methodologies. In particular, they struggled with the enormous number of news sources now available, leading them to focus on a narrow range of sources and limited time frames.
The authors of this study sought to examine a much greater volume of articles and analyse their impact over a longer period by using a so-called neural network.
How Do the Authors Tackle the Issue?
For logistical reasons, earlier studies limited themselves to analysis of a small subsection of news flow, such as a specific newspaper column. The authors of this study use a dataset of about 900,000 news stories from the Thomson Reuters news archive, sometimes with multiple stories about a particular company.
And whereas other studies typically followed stock prices for a day or two after the news, this study tracks returns for a full quarter to determine whether the effects of news flow are fleeting or long lasting. In particular, they examine whether a week’s worth of news on a single company has greater predictive power for the shares than a single day’s coverage.
A sentiment measure, which assesses investors’ reactions to news, was developed by the authors by applying a neural network to a dataset of news stories from 2003 to 2010. This neural network, called Thomson Reuters NewScope Data, breaks sentences down into their constituent parts—nouns, adjectives, verbs, adverbs, and intensifiers. This approach enables identification of parts of speech that convey tone, the intensity of tone, and positive and negative sentiment.
This process is designed to mirror a human’s assessment of an article and enables the authors to categorise a large number of articles as positive, negative, or neutral. Using these categories, the authors were able to build portfolios based on positive, negative, and neutral company news and measure the returns of each over various time frames.
What Are the Findings?
News substantially affects stock prices: On the day of a news story, average excess returns are 1.99%. (Excess returns are the difference between positive-news portfolios and negative-news portfolios.) Although the short-term return is high, this and previous studies show that price spikes are usually quickly reversed.
A more relevant finding, then, is that the neural network produced returns of 0.17% on the day after the news and 0.04% the following day. The authors believe these second- and third-day returns represent permanent value. After that, excess returns essentially disappear for the rest of the quarter, implying that the news is quickly priced in.
So, returns can be predicted for just a day or two when the news flow lasts for a day. But when news flow lasts for a week, there is a dramatic increase in how long the effect on prices lasts. In the week following the announcement, the excess return is some 3.75%, and over the subsequent 13 weeks, average excess returns are more than 2%.
The neural network is also useful in revealing the effects of positive, negative, and neutral news. Companies that are the subject of neutral news outperform those that are not written about at all. This effect would seem to negate the saying that “no news is good news.”
Positive news, on the one hand, predicts positive excess stock returns for about a week. Negative news, on the other hand, leads to a negative premium for at least a full quarter. The longer-lasting share price action in response to negative news, the authors believe, may be a result of the constraints of short selling.
What Are the Implications for Investors and Investment Professionals?
Deep textual analysis via a neural network appears to detect news that is persistently underincorporated in stock prices. Given that stock prices predominantly react to this news only at the time of the next earnings announcement, this finding suggests that the best trading opportunities exist just before corporate announcements.
For investors with the capabilities and mandate to short stocks, a premium can be captured through buying the stocks of companies in the 24–48 hours following publication of positive stories about them and shorting those with negative news flow in that length of time.
The opportunity persists beyond the initial 24–48 hours for the stocks of companies that are the subject of news flow for a number of days. Companies with good news over a one-week period outperform companies that are the subject of a week’s worth of bad news. Portfolios formed on this basis earned excess returns for up to 13 weeks in the period studied.
Investors may consider why the longer the news flow, the greater the impact on the shares. The authors note that the cumulative weight of news about a company may reinforce investors’ belief in that news. It’s also possible that a complex corporate story may take time to unravel as journalists find out more information about it. Therein lies a potential opportunity: If investment professionals can unravel the complexity of the story earlier than journalists, they can capture more of the excess returns available.