News Content, Investor Misreaction, and Stock Return Predictability*

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1 News Content, Investor Misreaction, and Stock Return Predictability* Muris Hadzic a David Weinbaum b Nir Yehuda c This version: September 2015 * We thank Thomson Reuters for providing data and Prasun Agarwal, Joy Thaler, and seminar participants at Syracuse University for helpful comments and discussions. All errors are our responsibility. a Whitman School of Management, Syracuse University, 721 University Avenue, Syracuse, NY Phone: mhadzic@syr.edu. b Whitman School of Management, Syracuse University, 721 University Avenue, Syracuse, NY Phone: dweinbau@syr.edu. c School of Management, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX Phone: Nir.Yehuda@utdallas.edu. Electronic copy available at:

2 News Content, Investor Misreaction, and Stock Return Predictability Abstract Using a large dataset of news releases, we study instances of investors mistaken reaction, or misreaction, to news. We define misreaction as stock prices moving in the direction opposite to the news when it is released. We find that news tone predicts returns in the cross-section only upon the occurrence of misreaction. Stocks that are larger, more liquid, more visible, and more covered, by analysts or by the media, are less likely to exhibit misreaction. On the other hand, the ambiguity and complexity of news content, and variables that proxy for investor distraction, are all associated with more misreaction and greater predictability. Keywords: media, news, stock return predictability, delayed reaction, investor recognition hypothesis. JEL Classifications: G12, G14, G17. Electronic copy available at: 1

3 1. Introduction There is extensive evidence that stock prices underreact or overreact on average to certain types of news. 1 While underreaction or overreaction has been shown to lead to predictability in the crosssection of stock returns, it can be difficult to identify ex ante specific instances when stocks underreact or overreact to news. In this paper, we study investors mistaken reactions, or misreactions, to news. We define misreaction as stock prices moving in the direction opposite to the news when it is released. We characterize the determinants of misreaction and show that misreaction predicts returns in the cross-section. We employ a comprehensive dataset of all news released on the Reuters data feed to identify instances of investor misreaction to news and investigate the informational content of news releases in financial markets. Our main findings are easily summarized. First, we find that stocks with positive news content subsequently outperform those with negative news content. In poolpanel regressions that control for a battery of factors, we find that stocks that had positive news content during the prior month subsequently earn a monthly return 0.75% higher than stocks that had negative news content. This predictability applies mainly to smaller stocks: the difference in average monthly returns between high and low news tone stocks is 1.25% for stocks in the bottom 25% of stocks by market capitalization and 0.45% per month for other stocks. Second, we partition the sample into two groups depending on whether the signs of news tone and stock returns match over the portfolio formation period. We view instances of the signs not matching as prima facie evidence of mistaken reaction on the part of investors, e.g., bad news that is accompanied by positive returns or vice versa. Consistent with this view, we find subsequent predictability only when stock prices move in the direction opposite to the news when the news is released. We thus find that misreaction to the information contained in news releases drives the predictability that we uncover. Third, we characterize the frictions that are associated with misreaction and predictability. We consider several types of frictions. First, theories of limited stock market participation (e.g., Merton, 1987) suggest that the stock prices of less visible firms can experience delays in 1 For example, stock prices on average underreact to earnings announcements and overreact to the accruals component of earnings (e.g., Ball and Brown, 1968, Bernard and Thomas, 1989, and Sloan, 1996). 2

4 incorporating information. Also, theories of limited attention suggest that when the amount of attention investors direct towards a firm decreases, its stock price should exhibit more severe underreaction to news and greater predictability (Hirshleifer, Lim and Teoh, 2011). We find that proxies for investor recognition such as analyst coverage and institutional ownership are associated with less misreaction. We also find that the amount of news coverage about the firm itself (a proxy for investor attention to the firm) is associated with less misreaction, while news coverage about other firms (which directs attention away from the firm) is associated with greater misreaction. Similarly, we find more misreaction when the proportion of news stories about the firm that are released during non trading hours is greater. The second type of friction that we consider relates to the nature of the news itself. We hypothesize that more complex or more ambiguous news may be more costly for market participants to process, leading to greater incidence of misreaction. The Reuters news data that we use in this paper allow us to construct a novel measure of the ambiguity of a news event: for each news item, the dataset includes estimates of the probability that the tone of the story is positive, negative and neutral. We measure ambiguity by the dispersion of these probabilities, similar to a Herfindahl-Hirschman index, and find that more ambiguous news is accompanied by more misreaction. As another proxy for complexity, we consider the proportion of the news that contains soft rather than hard information. We define soft information as information unrelated to corporate results, analyst reports, earnings forecasts, IPOs, corporate restructurings, and other hard economic news. We find that soft news is associated with more misreaction and greater predictability. The third type of friction we consider is illiquidity. We find that traditional proxies for illiquidity such as low trading volume, high bid-ask spreads, and Amihud s illiquidity measure (Amihud, 2002) are positively associated with misreaction and predictability. A central hypothesis that underlies our empirical tests is that media content is a proxy for fundamental economic information not yet incorporated into asset prices: journalists descriptions of firms production activities reveal new information. One alternative hypothesis is that media con-tent is associated with investor sentiment. Theoretical models of noise trading in financial markets, e.g., DeLong et al. (1990), predict that investor sentiment can have a temporary impact on stock prices, e.g., a negative sentiment shock will temporarily depress stock prices, with a subsequent reversion to fundamentals. Our results are inconsistent with this hypothesis because 3

5 we do not find any evidence of return reversals. Another view of media content is that its primary purpose is to entertain readers. Under this view, media content only conveys information already known to market participants and incorporated in asset prices. Our results are also inconsistent with this hypothesis. Collectively, our findings provide evidence that media content distills new information about economic fundamentals that investors sometimes do not react to appropriately. In contrast, they are inconsistent with theories of media content as a proxy for non-informational trading and investor sentiment. This paper contributes to a growing literature that investigates whether stock returns can be predicted by quantifying the textual content of news articles. While the results are somewhat mixed, the emerging consensus from this research is that media content can predict returns over very short time periods. For example, Tetlock (2007) finds that the fraction of negative words in the Wall Steet Journal Abreast of the Market column predicts negative returns on the Dow Jones Industrial Average over the next day, with a reversal to fundamentals over the following four days. Using two columns from the New York Times, Garcia (2013) shows that this predictability is stronger during economic downturns and over the weekend. Because the return reversal offsets the initial reaction, the evidence is consistent with temporary price pressure caused by investor sentiment. These papers thus suggest that media content drives investor sentiment but does not convey new information about economic fundamentals: if the columns contained new information about fundamentals, the initial market reaction to the news would not be subsequently reversed. The newspaper columns in both of these papers are essentially summaries of the events on Wall Street over the previous trading day. In contrast, Tetlock et al. (2008) examine the content of firmspecific news items, like we do. While they find some evidence of stock return predictability in the cross-section, it is short lived, lasting only one day. Our results are different in that we find long-term predictability: we show that news content predicts returns at the monthly horizon. This paper is closely related to Dzielinski and Hasseltoft (2013) and Heston and Sinha (2013). The focus in Heston and Sinha (2013) is different from ours: they are primarily interested in comparing several alternative approaches to textual processing. They find that the manner in which news tone is quantified is important, in particular more sophisticated linguistic processing techniques perform better than simpler ones that count negative words. While they show that firm-specific news tone can predict returns in the cross-section, they do not consider misreaction or investigate what 4

6 frictions drive this cross-sectional predictability. Dzielinski and Hasseltoft (2013) are primarily interested in the way in which the aggregate dispersion of news tone affects aggregate market returns, while our focus is on the level of news tone and cross-sectional predictability. They find that cross-sectional dispersion of news tone forecasts aggregate stock returns with a negative sign in times of high short-sale constraints but they do not find predictability based on the level of news tone. This paper is also related to the literature that assesses the impact of investor attention constraints on cross-sectional return predictability. Market reactions to earnings announcements are more complete when investor attention is likely to be greater, e.g., on regular weekdays rather than on Fridays (DellaVigna and Pollet, 2009) and during trading hours rather than non-trading hours (Francis, Pagach, and Stephan, 1992). Hirshleifer, Lim, and Teoh (2009) find that the immediate response to earnings news is weaker, and post-earnings-announcement drift stronger, on days on which more earnings announcements are made by other firms, a proxy of distraction. Our paper is different in several respects. First, we introduce the concept of misreaction, which is distinct from underreaction and overreaction. Second, we consider all corporate news releases rather than just earnings announcements, thus extending the literature on post-earnings announcement drift to all news. 2 Third, while we confirm that investors inattention affects their reaction to news, we also identify other effects that are important, such as illiquidity and the complexity of the news being released. 3 Our study has several advantages over papers that use other news data sets, employ dictionarybased textual analysis, or focus on specific information sources such as earnings announcements or firms annual reports. Newswire agencies such as Reuters offer comprehensive coverage of financial markets and are widely used by investors. The Reuters news feed can be viewed as an approximation to investors public information set, both in terms of information content and timing 2 Ball and Brown (1968), Jones and Litzenberger (1970), Rendleman, Jones and Latane (1987), Bernard and Thomas (1989, 1990), and Ball (1992) are some of the early papers on post-earnings announcement drift. 3 Also related is the literature on neglected stocks (Arbel and Strebel, 1982, Arbel, Carvell, and Strebel, 1983, and Arbel, 1985). We note, however, that the firms in our sample are not neglected, since they all have news released about them on the Reuters data feed. 5

7 of availability. We use all news items that appear in the Reuters data feed and hence avoid selection bias: our analysis includes all news events rather than potentially highlighting those with attractive results (Fama, 1998). Finally, the Reuters RNSE database that we employ in this paper has been shown to offer superior textual analysis capabilities (Heston and Sinha, 2013). It goes beyond a simple count of negative or positive words to define news tone; it uses sophisticated linguistic techniques to analyze word meaning taking into account relations with other words, word negations, simple and complex parts-of-speech, and the overall context of the entire text. The rest of this article is organized as follows. Section 2 describes the data and methodology used in our empirical tests. Section 3 presents our empirical results on predicting returns in the crosssection by using the tone of news content. It also introduces our misreaction measure, which is based on whether or not the signs of news tone and the corresponding stock returns match. Section 4 investigates the variables that drive misreaction. Section 5 performs various robustness checks and Section 6 concludes. 2. Data and methodology This section describes the data that we use in this paper and explains how we construct our news tone variables. 2.1 Data sources Our news data originate from the Reuters News Scope Sentiment Engine (RNSE) and cover the period from January 2003 to January The RNSE is a comprehensive database of all news released on the Reuters data feed. For each news item, the dataset provides, among other things, a time stamp and estimates of the probability that the tone of the story is positive, negative and neutral. For each news event, the dataset also includes a relevance score, which ranges from 0 to 1 and provides an assessment of how specific the news is to a given firm (e.g., a score of 0.5 corresponds to two firms featuring equally prominently in a release), and topic codes that describe the topics of the news article and are provided by the journalists. Creating a quantitative variable from news stories and other textual documents requires that one devise a numerical representation of the text. The most common approach is to compute the proportion of positive and negative words in each article, e.g., using the Harvard-IV-4 dictionary, 6

8 as in Tetlock (2007) and many studies in the psychology literature. 4 Engelberg (2009) points out that the Harvard-IV-4 dictionary misclassifies as positive several terms that are in fact neutral in a financial context and Loughran and McDonald (2011) similarly note that it misclassifies as negative several neutral words and propose an alternative word list. The estimates of article tone provided as part of the RNSE database do not rely on word lists. Rather, they stem from a neural network based algorithm developed by Thomson Reuters to classify news articles as positive, neutral, or negative. The Reuters algorithm also provides an estimate of the number of prior articles written about the same story. We refer to Dzielinski and Hasseltoft (2013) and Heston and Sinha (2013) for a more thorough description of the linguistic analysis upon which the Reuters algorithm is based. Briefly, the text of each story is first broken into individual sentences, then the subject of each sentence is identified, then an estimate is formed of the tone of the article for each company mentioned. The algorithm utilizes name entity recognition and parts-of-speech tagging, which are both widely used in textual parsing analysis (e.g., Jurafsky and Martin, 2008). Heston and Sinha (2013) compare the RNSE tone estimates to estimates derived from word lists and find that the RNSE estimates are more informative. In order to merge the news data with stock return data, the date associated with each news item is established using 3:30pm Eastern as a cutoff, which is 30 minutes before the NYSE closing time of 4:00pm. If news is released before the market closes, we assign the current trading day to the release, otherwise we assign the next trading day. Our stock return data are from the Center for Research in Security Prices (CRSP). We include all stocks with an average price above $5 during the previous year. We obtain accounting data and earnings announcement dates from Compustat, analyst coverage data from I/B/E/S, institutional ownership data from the Thomson Reuters Institutional (13f) Holdings dataset, Fama-French factor returns and risk free rates from Ken French s data library, and news pressure data from David Strömberg s website. 4 A widely used text analysis program called General Inquirer employs the same dictionary. 7

9 2.2 Construction of variables For each news item, the RNSE database includes three tone probability estimates: the probability that the tone of the story is positive, negative and neutral. To aggregate these three probabilities into a single measure, we define net news tone as the difference between the probability that the story is positive and the probability that it is negative,, (1) for each news article. After each news article is assigned a net news tone, we calculate the monthly average net news tone for each firm j and every month t,, (2) where njt is the number of news articles about firm j in month t. We use this net news tone measure in our empirical tests. An additional variable that we exploit in our analysis that is also derived from the RNSE database is our news dispersion measure. Similar to net news tone, news dispersion also utilizes the probabilities that a news article is positive, neutral or negative. For each article i, we define news dispersion as 1, (3) This quantity is similar to a Herfindahl-Hirschman index; it measures the level of ambiguity in a news article. It can take on values between zero and one: a value of zero represents the extreme case of perfect certainty about tone and a value of one implies extreme ambiguity of tone. As with net news tone, we average news dispersion across all news stories for every firm j in month t,. (4) Our analysis also uses four groups of variables. The first group consists of traditional stock characteristics: market capitalization, book-to-market equity ratio, return volatility, market beta, and past 12 month momentum (excluding the most recent month). The book-to-market ratio is defined as the last available book equity value divided by market capitalization at the end of the month. Return volatility is the standard deviation of a firm s daily stock returns measured over previous two years. We use a two-year window of daily stock returns to estimate market betas. 8

10 Variables that proxy for investor attention or distraction make up the second group: analyst coverage, institutional ownership, media coverage, other firms media coverage, the proportion of news stories circulated after trading hours, and news pressure. We define media coverage as the number of news items published about a firm on the Reuters data feed over a month. News pressure is an aggregate daily market measure of availability of newsworthy material and is defined as the median (across broadcasts in a day) number of minutes a television news broadcast devotes to the top three news segments in a day (Eisensee and Strömberg (2007)). We average the daily news pressure over a month and use this aggregated version in our tests. The third group consists of variables that measure how uncertain the news is: news dispersion, and the proportion of soft news items. We define soft news as news items that contain no information about corporate results, analyst reports, earnings forecasts, IPOs, corporate restructuring, lawsuits and legal actions, and other hard economic data. Reuters topic codes provide a setting to easily identify the substance of every news item. Finally, the fourth group is composed of traditional liquidity measures and includes volume, bid-ask spread and Amihud s illiquidity measure. Bidask spread is the difference between daily bid and ask prices divided by the closing price, and Amihud s illiquidity measure is defined as absolute daily return divided by daily dollar trading volume (Amihud (2002)). We average daily bid-ask spreads and Amihud illiquidity measures to calculate their monthly counterparts. The choice of variables we include in our empirical tests is consistent with the previous studies that analyze media coverage, investor attention and informational uncertainty. For example, Fang and Peress (2005) define media coverage as the number of news articles published about randomly selected sample of S&P500 firms in New York Times, USA Today, Wall Street Journal, and Washington Post. Chan (2003) defines media coverage as the occurrence of news stories published in Dow Jones Newswires. 5 Hirshleifer, Lim and Teoh (2009) use number of earnings announcements in a day as a proxy for investor distraction and find that post-earnings announcement drift is significantly stronger when volume of earnings announcements is unusually high. 5 Both studies find that firms with less news coverage have higher expected returns. 9

11 Our news dispersion variable is similar to that in Dzielinski and Hasseltoft (2013), who compute aggregate news dispersion as the standard deviation of firm-specific news tone across firms. Our measure is different in that it measures dispersion of news tone at the article level, rather than across stories in the aggregate. Thus our measure captures how ambiguous a story is, which allows us to consider the effect of cross-sectional variation in informational ambiguity on stock returns. Finally, our definition of soft information in similar to those found in Demers and Vega (2008), Davis et al. (2011) and Feldman et al. (2010), among others. All three papers use textual analysis to quantify information content in annual reports beyond commonly used financial measures and find that tone of soft information is a significant predictor of future stock returns. 2.3 Data description We provide basic summary statistics for the news data in Panel A of Table 1. The total number of news items in our sample is slightly over 3.2 million, of which 55% are full news articles, 34% are alerts, and the rest are updates or overwrites. Interestingly, more news is published during non trading hours (60%) than during trading hours (40%). A relatively higher number of news events take place in January, April, July and October, consistent with the fact that the majority of firms have December fiscal year-ends and therefore have earnings announcements scheduled during these months. On average, 2600 firms have news published about them in a month. The lowest news volume is observed on Fridays and over the weekend. News volume then monotonically increases during the week, reaching a peak of 2501 news items published on average on Thursdays. Panel B reports descriptive statistics on selected variables, including monthly stock return, news tone, book-to-market ratio, market beta, return standard deviation, analyst coverage, institutional ownership, and news count distribution across trading hours. The average monthly net news tone is 0.05 with a standard deviation of 0.30, which suggests that there is significant variation in news tone across firms and over time. The average market capitalization of the firms in our sample is $6.3bn, and the median is $1.1bn. The average firm in our sample is covered by 11 analysts and is almost 70% owned by institutional investors. It has 19 news items published about it in a month with significant dispersion of news coverage across firms and over time. Positive news dominates in our sample: around 67% of all news items in our sample are classified as positive in the RNSE database. More negative news is usually published during weekends and after-trading hours. This 10

12 is consistent with evidence in Della Vigna and Pollet (2009) and Damodaran (1989) that firms have a tendency to announce negative earnings when investor attention is lower. 3. News tone and the cross-section of stock returns This section investigates whether stock-specific news tone contains information about the crosssection of stock returns. To investigate this, we sort stocks into portfolios based on their lagged stock-specific news tone and consider the subsequent returns on those portfolios, using both portfolio sorts and pool-panel cross-sectional regressions that include a battery of control variables. We first characterize the portfolios formed on the basis of stock-specific news tone and then turn to the predictability results. We begin by using pool-panel regressions to examine the predictive ability of stock-specific news tone. We then condition on whether the sign of the stock return matches that of news tone during the portfolio formation month using portfolio sorts. This allows us to examine whether the initial reaction to news affects future stock returns. In the next section, we proceed to investigate the cross-sectional determinants of the initial investor reaction. 3.1 Pre-formation portfolio characteristics Each month, we sort stocks into four portfolios based on their lagged monthly stock-specific news tone. The number of stocks mentioned in the news fluctuates over time, thus the size of the pools of securities in each group also fluctuates over time. On average, there are about 2,600 stocks in the sample each month. Stocks in the bottom 25% of lagged news tone are in the low news tone group and stocks in the top 25% of lagged news tone are in the high news tone group. Panel A of Table 2 reports average pre-formation characteristics for the four portfolios, specifically time-series averages of equally weighted cross-sectional averages in each group. Stocks with high or low stock-specific news tone tend be smaller than the stocks in the middle two groups. 6 Stocks in the low stock-specific news tone group are more volatile, which is consistent with the so-called leverage effect (Black (1976)). 6 This may reflect the fact that small firms are riskier and thus more likely to experience extreme news events. Alternatively, it may be due to the fact that larger firms are more widely covered by journalists: since we measure stock-specific news tone by averaging across all news stories published in a month, we inevitably dampen the effect of extreme news stories for firms with high coverage, which tend to be large. 11

13 Panel A of Table 2 also shows the average pre-formation performance of the stocks in each news tone group. We measure past returns as the equally-weighted average return during the month preceding portfolio formation. Not surprisingly, stocks in the low news tone group earn low returns during the month before portfolio formation (i.e., the month over which the news tone variable is observed) and stocks in the high news tone group earn high returns. This implies that, crosssectionally, the news tone strategy is a momentum strategy: it buys stocks that have recently performed well and sells short stocks that have recently done poorly. Anticipating the results that follow, we show that the predictability is robust to controlling for both the momentum factor and the past stock return. 3.2 Pool Panel Regressions To investigate whether news tone predicts stock returns, we first report the results of pool-panel regressions and then turn to portfolio sorts. Pool-panel regressions allow us to control for a battery of control variables, including factor returns, past stock returns (to directly control for stock momentum), and lagged factor returns (to control for possible thin-trading effects). We sort stocks into four groups based on their net news tone measured over the prior month and construct three dummy variables: low for the bottom 25%, high for the top 25%, and middle for stocks in the middle 50% by net news tone. We then run regressions of monthly excess returns on the dummies and controls, or on the dummies times net news tone (piecewise linear regressions) and controls. The t-statistics we report employ a robust variance estimator clustered by firm. The results in Table 2 show that net news tone predicts returns in the cross-section. Focusing on Panel B, column (1) reports results of regressions of stock returns on the net news tone dummies alone, column (2) adds the Fama-French (1993) and Carhart (1997) factors and lagged stock returns, and column (3) further adds lagged factors. Controlling for all the control variables, the coefficients in column (3) imply a difference in average monthly returns between stocks with high net news tone and stocks with low net news tone of 75.1 basis points per month, or about 9% per year. The results in Panel C show that the predictability is robust to using piecewise linear regressions. In Panel D, we consider the effect of size on the predictability. Specifically, we create a small size dummy that equals one for stocks in the bottom 25% by market capitalization and zero otherwise. We then interact the small size dummy with the net news tone dummies. Panel D shows that the 12

14 predictability is significantly stronger in smaller firms: including all the controls, the difference in average monthly returns between high and low net news tone stocks is 1.25% for stocks in the bottom 25% by market capitalization and 0.45% per month for other stocks. The results in Panel D constitute evidence that small capitalization stocks take longer to adjust to new public information, producing stronger delay after news articles are published about them. Anticipating the results that follow, the portfolio sorts below show that the predictability is significantly stronger for equally-weighted returns than for value-weighted returns. In Section 4, we investigate what drives this delayed response, in particular whether it is due to information and investor recognition or driven primarily by a lack of liquidity and trading constraints. Overall the results in Table 2 present evidence that net news tone predicts returns in the crosssection. The nature of the cross-sectional predictability that we uncover is quite different from that in Tetlock et al. (2008), who find that stock prices briefly underreact to the information in firmspecific news stories, leading to predictability that lasts about a day: the predictability that we find is neither short-lived nor subject to return reversals. 7 This suggests that news tone contains information about economic fundamentals rather than being a proxy for investor sentiment. Next, we shed further light on this issue by investigating what drives the predictability and when it is stronger. 3.3 Portfolio sorts and initial reaction to news We now investigate the extent to which net news tone predicts stock returns in the cross-section using portfolio sorts, first for all stocks then for two separate groups of stocks. The two groups of stocks are created as follows. Every month and for every firm, we compare the signs of net news tone and stock returns and create two groups depending on whether or not the signs match. We find that this simple characterization of investors initial reaction is effective in identifying the existence of delayed reaction to the information contained in news stories. Consistent with our earlier approach, every month we sort stocks into four groups based on their lagged net news tone and measure the subsequent monthly returns. We report the performance of 7 In unreported results, we analyze returns for up to six months following portfolio formation and find no signs of reversal. 13

15 the four news tone portfolios and that of the long/short hedge portfolio that buys stocks with high lagged net news tone and shorts stocks with low lagged net news tone. To ensure that our results are not driven by differences in risk across news tone portfolios, we calculate abnormal returns using a four-factor model that includes the three Fama-French (1993) factors and the momentum factor of Carhart (1997) and Jegadeesh and Titman (1993). The estimated abnormal return is the constant in the regression R t 1 MKT t 2 SMB t 3 HML t 4 UMD t t, (5) where Rt is the excess return over the risk free rate to a portfolio at time t, MKTt, SMBt, HMLt, and UMDt are, respectively, the excess return on the market portfolio and the return on three long/short portfolios that capture size, book-to-market, and momentum effects. In Panel A of Table 3, we begin by reporting the results for the full sample, without partitioning. The equal-weighted, four-factor adjusted return on the long/short hedge portfolio is 48.7 basis points per month with a t-statistic of Consistent with our earlier finding that the predictability is significantly stronger is smaller firms, the value-weighted return on the same portfolio is only 18.2 basis points and it is statistically insignificant. These results confirm that net news tone is informative about future stock returns and that the effect is concentrated in smaller stocks. In Panels B and C, we partition the sample into two groups depending on whether the signs of net news tone and stock returns match over the portfolio formation period. Panel B presents the results of portfolio sorts when returns and news tone are of the same sign and Panel C shows the corresponding results when the signs do not agree. We find that the predictability is much stronger when the initial market response is not in the direction of the news tone, i.e., when news tone and contemporaneous monthly returns are of a different sign. The equal-weighted Fama-French four factor adjusted return in this case is 73 basis points with a t-statistic of On the other hand, when the signs of news tone and contemporaneous monthly returns match, the equal-weighted FF4 alpha on the long/short hedge portfolio is only 30 basis points and is statistically insignificant. The value-weighted FF4 alphas are not statistically significant in either case, but they are consistent: when news tone and contemporaneous monthly returns are of a different sign, the value-weighted 8 We note that the results based on portfolio sorts are not exactly comparable to the earlier cross-sectional regression results, even for equally-weighted portfolios, because the sample size fluctuates over time. 14

16 FF4 alpha is 52.7 basis points per month and is borderline significant, with a t-statistic of When the signs agree, the value-weighed FF4 alpha is only 20 basis points and is statistically insignificant. The variable we use in this paper to capture investors initial misreaction to information is a simple and imperfect one. Because it is a binary variable, it cannot measure the extent to which investors incorporate news content, but our results suggest that it can identify situations where a more delayed reaction can be expected, if news stories indeed provide information about economic fundamentals. The fact that we use a simple measure of misreaction should work against us finding differences in predictability across the two groups. In the next session, we investigate the economic determinants of misreaction to news. 4. Determinants of reaction to news and return predictability In the previous sections, we show that net news tone contains information about stock returns in the cross-section, particularly for small stocks. We also introduce a simple misreaction measure that is based on whether the signs of net news tone and the corresponding stock returns match. Having established that this misreaction measure is a significant determinant of the predictability that we uncover, we now turn to analyzing what drives investor misreaction to news. 4.1 Economic variables We consider four groups of variables in investigating the determinants of misreaction to news. The first consists of basic stock characteristics and includes market capitalization, book-to-market ratio, market beta, standard deviation of returns, and past returns (excluding the most recent month). The second group includes variables that proxy for investor attention and recognition: analyst coverage, institutional ownership, the amount of news coverage about the firm itself during the month, the amount of news coverage about other firms, the proportion of news about the firm published outside of trading hours, and the Eisensee and and Strömberg (2005) measure of news pressure. The third group includes variables that proxy for information ambiguity: news dispersion, the proportion of soft information in news stories. Finally, the fourth group consists of liquidity variables, namely volume, bid-ask spread and Amihud s illiquidity measure (Amihud (2002)). 15

17 Our choice of variables largely follows Hou and Moskowitz (2005), but we add proxies for information uncertainty as an additional group of variables. We thus explore several possible determinants of misreaction in this analysis. First, misreaction can be due to lack of liquidity and other impediments to trade (Fang and Peress (2009)). Second, investor recognition or attention can play a role, e.g, if certain firms are less visible, or neglected, by investors who fail to pay close attention to new information about them, or if certain news events are at times overlooked because a large number of other news events is taking place at the same time, or because they are released at times when investors may be more distracted. Third, the nature of the news itself may be important: a news item can be ambiguous or it can provide complex information that is more difficult or costly for investors to process and act upon, resulting in delayed reaction. 4.2 Determinants of initial reaction to news We use probit regressions to investigate whether these variables help explain whether the sign of stock returns matches that of the corresponding net news tone. We first run regressions on each of the four variable groups separately, and then for all of them together. Table 4 reports the results of probit regression estimates. Panels A through D have the estimates for the four variable groups separately. The results in Panel A imply that larger firms are less likely to experience a mismatch while riskier firms (in terms of standard deviation or, to a lesser extent, beta) are more likely to do so. Panel B presents the results for the investor attention variables. Institutional ownership and analyst coverage are both associated with a higher likelihood of a match between the signs of net news tone and stock returns. The number of news stories about the firm is also associated with a higher likelihood of a match, while news stories about other firms decrease the likelihood of a match, consistent with the idea that stories about the firm focus attention to it, while stories about other firms focus attention away from it. Finally, the greater the proportion of news stories about the firm that are released during non trading hours, the higher the likelihood of misreaction, consistent with lower investor attention at such times. Panel C contains the results for the news uncertainty variables. We find that news dispersion is negatively associated with the likelihood of a match, consistent with the idea that more ambiguous 16

18 news is more likely to result in misreaction. Similarly, the proportion of news that is soft is also associated with misreaction. Panel D has the results for the liquidity variables. Trading volume is positively associated with the likelihood of a match and bid-ask spreads are negatively associated with the likelihood of a match, consistent with the view that liquidity matters in explaining how quickly information is impounded into stock prices. Finally, Panel E reports probit estimates for various combinations of the variables and for all the variables together. Looking at the specification in column (7), which features all the variables jointly, investor attention and news uncertainty emerge as the main determinants of the initial reaction to news. Large, more visible and more covered stocks, both by analysts and media, are associated with timely investor reaction to news. On the other hand, media coverage about other firms and the proportion of news stories published during non trading hours seem to be distracting to investors. Also, when the informational content of news stories is ambiguous or more difficult to interpret, there is more delayed reaction. After controlling for these effects, the market liquidity variables become insignificant. 4.3 Determinants of return predictability Having established which variables explain the initial reaction to news, we now ask whether these variables also help explain return predictability in the cross-section. We consider the same four groups of variables and generate a series of double portfolio sorts on news tone and each of the variables separately. Every month we sort stocks into four portfolios based on their average lagged monthly news tone and into four groups based on the second variable, each time generating 16 portfolios. We then consider the performance of the long/short hedge portfolio that buys stocks with high net news tone and sells stocks with low net news tone separately in each quartile of the second sorting variable. Table 5 reports four-factor-adjusted returns for the top and bottom quartiles of the second sorting variable both for the full sample and for the subset of observations for which the signs of news tone and stock returns disagree. The results in Panel A reveal that small firms, value firms, past losers, and riskier firms exhibit greater return predictability. In Panel B, we find that firms with low institutional ownership and firms with low analyst coverage exhibit greater predictability. Similarly, firms with a greater 17

19 proportion of news released when the market is closed also exhibit more predictability, although neither the firm s own news count nor that of other firms is associated with predictability in the cross-section. In Panel C, we find that stocks that experience a greater proportion of soft news exhibit more predictability, although news dispersion is not associated with predictability. Finally, the results in Panel D suggest that liquidity is an important determinant of predictability: stock with low trading volume, high illiquidity and high bid-ask spreads exhibit economically large and statistically significant predictability, while more liquid stocks do not. Overall, the results in Table 5 are consistent with our earlier results in Table 4: investor attention, recognition and the complexity of the news are important determinants of the predictability, as is stock liquidity. 5. Further interpretation and discussion The previous sections provide evidence that news distills information that is relevant for asset prices. We find that news tone forecasts future returns, particularly in small stocks, and that this predictability arises due to delayed reaction to news when investors are inattentive, distracted, when the news contains more complex soft information, and when the stock is less liquid. In this section we conduct several additional tests to investigate whether our findings are robust to alternative specifications. First, we ask whether our predictability results are driven by postearnings announcement drift. Second, because our sample includes the recent financial crisis, we exclude it to see if the predictability is affected by these extreme returns. Third, we consider weekly news tone and weekly stock returns to examine whether our main results hold at the weekly frequency Excluding earnings announcements Following the same approach as in Section 3, every month we sort stocks into four groups based on their lagged net news tone and measure the subsequent monthly returns. We measure net news tone excluding earnings news. Table 6 reports the performance of the four news tone portfolios and that of the long/short hedge portfolio that buys stocks with high lagged net news tone and shorts stocks with low lagged net news tone. We exclude earnings announcements either by 9 We also consider terciles, quintiles and decile specifications for our portfolios and find our results intact. 18

20 excluding all news items that mention quarterly earnings in Panel A, or by completely excluding all firm-month observations that contain an earnings announcement, in Panel B. The equal-weighted long/short hedge portfolio that excludes earnings news earns on average 43 basis points (t-statistic 3.13) in the month following portfolio formation, after adjusting for market, size, book-to-market, and momentum effects. A similar long/short strategy that excludes all firmmonths with earnings announcements yield an average four-factor adjusted alpha of 46 basis points per month (t-statistic 2.53). These results are almost identical to those in Table 3, where we form the portfolios based on all news, including earnings news. The value-weighted returns and alphas are also very similar. We thus conclude that our results are not driven by earnings announcements and post-earnings announcement drift. 5.2 Excluding the financial crisis Table 7 reports the results of pool-panel regressions of monthly stock returns on news tone and other control variables excluding the period of the recent financial crisis. Specifically, we exclude monthly observations starting in September 2008, when Lehman Brothers collapse had a large impact on stock markets. 10 The results in Table 7 suggest that the ability of news tone to predict returns is not an artifact of the extreme observations surrounding the financial crisis. For example, focusing on column (3) of Panel A, where we include all the control variables, the coefficients imply a difference in average monthly returns between stocks with high net news tone and stocks with low net news tone of 50 basis points per month, which is quite similar to (but somewhat weaker than) the corresponding 75 basis point difference in Table 2 for the full sample. Excluding the period of the financial crisis eliminates more than 10% of our sample, and removes a period of high volatility, but the results are robust in all the specifications. 5.3 Weekly news tone and stock returns In Table 8 we choose a shorter horizon over which to measure net news tone and stock returns and consider the same pool-panel regressions as in Table 2 but now at the weekly frequency. Each 10 Excluding months after December 2007, when the recession officially started according to the National Bureau of Economic Research, does not change the results. 19

21 week, we sort stocks into four portfolios based on their lagged weekly net news tone and hold them for one week. Panel A of Table 8 reports the results of pool-panel regressions that feature dummies for low and high lagged weekly news tone as the main variables, and contemporaneous and lagged weekly Fama-French factors, as well as lagged weekly returns, as control variables. Panel B includes the interactions between the dummies and the corresponding news tone, and the same set of control variables. Finally, in Panel C, we consider the interactions of news tone and market capitalization, where the small size group again represents the bottom quartile by market capitalization. The results in Panels A-C of Table 8 are qualitatively similar to our earlier results in Table 2, where we use monthly net news tone and monthly stock returns. For example, the difference in average weekly returns between the high net news tone portfolio and the low net news tone portfolio is 22 basis points per week after controlling for the lagged return and the lagged and contemporaneous four Fama-French factors. This difference in performance is highly significant statistically and large economically, corresponding to roughly 95 basis points per month (22 52 / 12), which is somewhat larger than the corresponding monthly alpha of 75 basis points in Table 2, although of course the weekly strategy involves significantly more trading. In Panel D, we consider weekly portfolio sorts and investigate the impact of the initial market reaction to the predictability. We report average returns and alphas for long/short hedge portfolios based on news tone, conditional on whether or not the signs of lagged weekly news tone and stock returns match. The equal-weighted four factor adjusted alpha is 31 basis points per week (t-statistic 2.89) when the signs of news tone and returns differ and an insignificant 9 basis points when they match. Similarly, the value-weighted four factor adjusted alpha is 46 basis points per week (tstatistic 3.78) when the signs of news tone and returns differ and is negative and insignificant when they match. We thus find that using weekly rather than monthly returns and news tone does not alter our main result that news tone forecasts future stock returns and that predictability is driven by initial misreaction to news content. 20

22 6. Conclusion This paper studies instances of investor misreaction to news and finds that they predict returns in the cross-section. We define misreaction as stock prices moving in the direction opposite to the news when it is released. We find that stocks with positive news content subsequently earn monthly average returns that are 0.75% higher than those with negative news content. This predictability applies mainly to smaller stocks: the difference in average monthly returns between high and low news tone stocks is 1.25% for stocks in the bottom 25% of stocks by market capitalization and 0.45% per month for other stocks. When we partition the sample into two groups depending on whether the signs of news tone and stock returns match over the portfolio formation period, we find subsequent predictability only when stock prices move in the direction opposite to the news when the news is released. We thus find that misreaction to the information contained in news releases drives the predictability that we uncover. We then investigate the factors that are associated with misreaction and predictability. We consider three types of factors: factors that proxy for investor recognition and attention, factors that proxy for the ambiguity and complexity of the news, and factors that proxy for illiquidity. We find that all three types of effects operate. Large, more visible and more covered stocks, both by analysts and the media, are associated with less misreaction. On the other hand, media coverage about other firms and the proportion of news stories published during non trading hours are associated with more misreaction. Also, when the informational content of news stories is ambiguous or more difficult to interpret, there is more misreaction. Traditional liquidity variables such as trading volume and bid-ask spreads matter also. While the predictability that we uncover is stronger in smaller firms, it is economically large, statistically significant, and robust to a battery of control variables, the exclusion of earnings announcements, the exclusion of the financial crisis months, and the choice of alternative time horizons or sorting specifications. The returns on news tone portfolios show no sign of reversal, suggesting that they is driven by information rather than sentiment. Thus our results provide evidence that media content distills important new information about economic fundamentals that investors sometimes do not react to appropriately. 21

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