Journalists, Persuasion, and Asset Prices

Transcription

1 Journalists, Persuasion, and Asset Prices Casey Dougal, Joseph Engelberg, Diego García, and Christopher A. Parsons October 18, 2010 Abstract We find that a small set of financial columnists has a causal effect on short-term aggregate stock market prices. For some journalists ( bulls ) the one or two day market reaction is consistently positive, whereas for others ( bears ) it is negative. The resulting predictability suggests that the market does not filter out journalistspecific sentiment or spin of underlying facts. Moreover, the random arrivals of bulls and bears allows us to make causal inferences about the media s influence on aggregate market returns. Keywords: Media Bias, Market Efficiency, Financial Journalism JEL: H53, I38, J31, J33 We thank Stephan Siegel, Paul Tetlock, and Sheridan Titman for helpful feedback and suggestions. The paper has also benefitted from suggestions by seminar participants at Arizona, Boston College, and UNC. The usual disclaimer applies. All authors are from the Kenan-Flagler Business School, University of North Carolina. Casey Dougal, ( ) casey dougal@kenan-flagler.unc.edu, (Tel) ; Joseph Engelberg, ( ) joseph engelberg@kenan-flagler.unc.edu, (Tel) ; Diego García, ( ) diego garcia@unc.edu, (Tel) ; Christopher Parsons, ( ) chris parsons@kenan-flagler.unc.edu, (Tel)

2 1 Introduction Although the media is often modeled as a faceless institution, its main output news content is generated by specific people. This is important because unlike, say, making tires or processing paper, writing is a fiercely individualistic craft that allows the author s style, persuasion, views, or bias to be injected into the finished product. In this paper, we present direct evidence that the writing of specific journalists has a casual effect on aggregate market outcomes. This finding is surprising because, at any point in time, individual columnists are unlikely to possess information advantages relative to the market as a whole, let alone consistently over a period of several years. Thus, any persistent return predictability related to specific authors must arise from their sentiment or spin of public information. From 1970 to 2007, the short-term returns on the Dow Jones Industrial Average (DJIA) can be predicted knowing only the author of a widely read market summary article, the Wall Street Journal s column entitled Abreast of the Market (AOTM). Ordinarily we would be concerned about the endogenous nature of news coverage in a column summarizing market events. As Tetlock (2007) notes, It is unclear whether the financial news media induces, amplifies, or simply reflects investors interpretations of stock market performance (p.1139). Making a distinction between a reflective and a causal role for financial media thus requires exogenous variation in news content i.e., reporting uncorrelated with underlying events. In this regard, our setting is useful: what a journalist writes may be endogenous to market events, but which journalist writes is not. Columnists for the AOTM are assigned weeks in advance and appear to alternate based on quasi-regular schedules (e.g., every other Friday), such that their rotations bear no systematic relation to past or future market returns. These exogenous rotations imply that simple tests are sufficient for identification of a casual relationship between news content and market returns. In our main tests, the dependent variable in a linear regression is the daily excess return on the DJIA Index. The control variables include several lags of returns, day of the week 1

3 dummies, and lagged volume. Our interest is in the twenty-six vectors of journalist fixed effects, one for each financial columnist writing for the Wall Street Journal (WSJ ) during our sample period. Given their apparently random assignment, we take these indicators as exogenous with respect to DJIA market returns. In aggregate, journalist fixed effects are significant predictors of future DJIA returns. Knowing only the name of the financial columnist writing for the WSJ on a given day increases predictive power of the regression by 15 percent relative to the other control variables. In simple linear restriction tests, journalists are jointly significant at predicting DJIA returns on the day the journalist s article is published (day t) (p-value = 1.4%) and the day after (day t + 1) (p-value = 0.1%). Over the two-day interval, roughly one fourth of journalists are individually significant at the 5 percent level, and roughly one half are significant at the 10 percent level. Because we are examining signed, not absolute, market returns, we can interpret the coefficients on specific journalists as their average bullishness or bearishness. When a bullish columnist writes, the market inches upward a few basis points over the next one to two days. By contrast, the market greets a bear s column with a slight decline. To gauge the size of this predictability, we construct a trading strategy that depends on the identity of the AOTM authors. Specifically, we classify bull and bear columnists by examining the average market response to their first twenty-five columns, and then simply buy on mornings (after the estimation period) when a bull authored the AOTM column. Unconditionally, the DJIA s excess return over U.S. Treasuries is about 2.6 basis points a day over our sample period, with a standard deviation of roughly 100 bp. However, on the 38 percent of days that bull columnists write, the daily excess return jumps to over 4 bp, with nearly identical variability. On the days where bear columnists write, the daily excess return averages about 1 bp. What these return patterns do not tell us is why the aggregate market appears to grant financial columnists a voice, some more than others. There are only two possibilities. The 2

4 first is that financial columnists are endowed with information about future returns, and by writing, make this information public. The second is that journalists are not informed, but nevertheless can exert sufficient influence to move the entire market. For example, journalists may exhibit persistent optimism or pessimism, but investors may not sufficiently condition out these journalist-specific effects. Two pieces of evidence argue against the information hypothesis. First, we are linking specific individuals to the future returns of a diversified, aggregate index. An information story would thus require a few financial columnists to have persistent information advantages over the entire market, a claim that seems implausible in the short term, even more so over many years. Second, recalling that columnists are affiliated with return patterns of a particular sign, these information advantages would need to be both journalist- and signspecific. For example, columnist John Smith would need to consistently receive private, positive signals about future returns. This seems nearly impossible. On the other hand, the potential for certain journalists to inject sentiment, or spin, into their interpretation of public information seems more realistic. Indeed, when we run regressions where the dependent variable is some measure of an article s tone e.g., the percentage of positive versus negative words rather than market returns, columnist fixed effects are extremely important determinants. Because these regressions also control for recent market performance, it appears prudent to conclude that journalists have persistent, idiosyncratic views of the market that are reflected in their writings. Moreover, we find that the set of journalists who write persistently positive (negative) articles are precisely those journalists who have persistently positive (negative) effects on returns. Our final set of results provides evidence that journalists rhetoric and its effectiveness vary with market conditions. In particular, when we interact journalist fixed effects with lagged returns, we find much more explanatory power for both the content of articles and future returns. Some journalists appear to hype present market conditions when they write while others are quite contrarian. We also find that a journalist s persuasion is most 3

5 effective in bad times. A number of studies have documented the media s ability to shift public opinion, particularly with regard to voting patterns (DellaVigna and Kaplan (2007), and Gerber, Karlan, and Bergan (2009)). What makes the present results so surprising is the strong theoretical assumption that the media should not, apart from information effects, be able to influence prices, certainly at the aggregate level. Whereas there are models to explain why consumers might be susceptible to biased reporting and by extension, why media outlets may then have an incentive to misreport to them (Gentzkow and Shapiro (2006)) prices of financial securities should not reflect bias due to informed traders. That they do is consistent with market frictions limiting such arbitrage. However, the impact of journalists on returns is strongest at the very end of our sample (2000 to 2007), when transaction costs were at historic lows and opportunistic hedge funds were most active. Our study contributes to a growing literature exploring the connection between the media and the stock market. Tetlock (2007) is seminal in this regard, showing that the percentage of negative words in the AOTM predicts next-day market returns. One of our key aims is to shed light on the underlying mechanism i.e., to distinguish whether the media reflects existing investor sentiment or causes it to change. Because journalist arrival is random, our experimental design allows us to claim a cause-and-effect relationship between the print media and financial market outcomes. While a number of similarly motivated studies present evidence that the media can influence the prices and trading of individual securities (Huberman and Regev (2001), Reuter and Zitzewitz (2006), Engelberg and Parsons (2010)), causal evidence at the market level is absent. This distinction is important, because although journalists could conceivably be informed about a particular stock, this is nearly impossible at the aggregate level. Finally, our paper adds to a growing literature that focuses on the effects of individuals on economic outcomes. For example, Bertrand and Schoar (2003) demonstrate that specific managers influence firms investment and capital structure decisions, while Chevalier and 4

6 Ellison (1999) show that personal fund manager characteristics are related to investment choices and fund performance. Like these studies, we show that individualism matters - not only for the expression of media content, but also for aggregate economic outcomes based on it. The paper is organized as follows. In Section 2, we describe the data and define our key variables. Section 3 presents evidence that specific journalists influence the aggregate markets. Within this section, we also conduct a number of specification and falsification tests and apply the predictability to an out-of-sample trading strategy. In Section 4, we attempt to be more specific about why journalistic contributions matter for returns. We show that journalist fixed effects explain several article characteristics (such as the choice of positive and negative words) and that journalists predictability for future returns is highly dependent upon the economic climate in which they write. Section 5 concludes. 2 Data Two main data sources are used in this paper: the Abreast of the Market (AOTM) column from the Wall Street Journal, and the Dow Jones Industrial Average (DJIA) price and dividend series. Our sample period spans January 1, 1970 to December 31, Data further back is available for both sources, but it was not until approximately 1970 that the AOTM column was consistently published with the accompanying author s name. AOTM is one of the most widely read market summary columns in the United States. It provides analysis of prior market activity, describes some notable company-specific events, and sometimes offers predictions for the future. 1 Electronic text copies of AOTM columns dated after 1984 are available. Data prior to 1984 is obtained from Proquest s historical Wall Street Journal archive, which stores the articles as scanned images. To convert these images to text files, we use ABBYY OCR software. 2 Typically, this process yields a high 1 See Tetlock (2007) for more discussion. 2 OCR, or optical character recognition, is the electronic translation of scanned images of handwritten, 5

7 quality of transcription, but there are occasional errors. Because this measurement error is idiosyncratic, we expect this to bias coefficients of interest towards zero. During our sample period, the AOTM column was published Monday through Friday with a few exceptions on national holidays. Overall, our sample includes 9,194 AOTM articles. We restrict our attention to journalists which wrote at least fifty AOTM columns. For a small number of articles (349), we are either unable to identify the author, or the author wrote fewer than fifty articles. A similarly small number (383) were co-written, in which case authorship is credited to both journalists. Overall, this results in a set of twenty-six authors. Table 1 presents statistics for each journalist. Moving across the table, we list the journalist s last name, the years he or she was active, the total number of articles written, and the most frequent year and month in which he or she was published. The median number of articles authored is approximately 120. More detailed columnist-level content analysis is presented in Table 2. In this table, we tabulate a number of additional measures intended to capture differences in writing style. Using the finance-specific dictionaries of Loughran and McDonald (2009), we count the number of positive and negative words, as well as the total number of words used by each author daily. 3 Pessimism, shown in the first column of Table 2, is equal to the difference between the percentage of negative and positive words, but scaled across all authors to have zero mean and unit variance. By this measure, the most optimistic journalists are Steiner and Marcial (Pessimism = 0.25), and the most pessimistic is Pettit (Pessimism = 0.36). The remaining columns characterize each journalist s writing in a number of other ways, including the average number of syllables per word (Syllables), sentences per AOTM column typewritten, or printed text into machine-encoded text. The ABBYY OCR software we use performs OCR using intelligent character recognition (ICR). This type of OCR works by searching the scanned image for common elements such as open spaces, closed forms, lines, diagonals intersecting and so on to identify letters. Typically, the accuracy rates using ICR are very high. However, much of the success of the OCR program depends on the clarity of the scanned image. 3 See mcdonald/word Lists.html. We use these dictionaries in particular because they account for a number of the nuances related to financial language. 6

8 (Sentences), and words per sentence (WPS). Complex words (Complex) are defined as having three or more syllables and are tabulated as a fraction of total words. The final column, Fog, reports the Fog readability score which indicates the number of years of formal education a reader of average intelligence would need to read and understand the article in one sitting. For example, a Fog score of 18 would be considered unreadable, a score of 12 appropriate for a high school graduate, and so on. It is calculated as F og = (W P S + Complex) 0.4. What is most interesting, for our purposes, is the degree of heterogeneity among journalists (e.g., Ip (12.25) versus Talley (9.59)). For now, we delay discussion of these differences, but return to it in our subsequent analysis. Our main dependent variable is the excess daily return on the DJIA Index. 4 From Yahoo! Finance, we extract a daily series of opening and closing prices for the DJIA and then we add the price-weighted dividend yield for each of the index s components because the DJIA is a price-weighted index. 5 Defining r t as the DJIA excess return and p t as the level of the DJIA index at the close of day t, we have r t+1 = p t+1 p t p t r f,t+1 + dp t+1 (1) where r f,t+1 is the one-month Treasury bill rate obtained from the Center for Research in Securities Prices (CRSP), and dp t+1 is the price-weighted average dividend yield for the stocks in the DJIA index defined as dp t+1 = i DJIA d i,t+1. (2) i DJIA p i,t Note that one-day lagged prices are used in the calculation of the DJIA aggregate dividend yield. This is to avoid the price adjustments that occur following a dividend issue or stock 4 We use DJIA returns as our dependent variable following Tetlock (2007) who argues that the AOTM column tends to cover the blue-chip stocks of the DJIA. However, our main results are nearly identical when we use S&P 500 Index or the CRSP Value-Weighted Index (Appendix, Tables 10 and 11). 5 Changes in the level of the DJIA ignore distributions to shareholders. See Sialm and Shoven (2000). 7

9 split. Over our sample period, the total excess return of the DJIA averaged 2.6 basis points, equating to an annualized excess return of 6.5 percent. Daily excess return volatility is approximately 101 basis points, implying an annualized Sharpe ratio of 0.41, nearly identical to that found in other diversified return indices 6. Additionally, in the regressions that follow other variables are included to control for known sources of return predictability such as day-of-the-week or liquidity effects and microstructure noise such as bid-ask bounce or non-synchronous trading. To this end, we construct the Controls vector which includes five lags of detrended daily log volume from the New York Stock Exchange (NYSE) obtained from CRSP, five lags of detrended squared DIJA residuals which proxy for volatility, day-of-the-week dummies, and a dummy variable for the month of January. Both log volume and squared DIJA residuals are detrended by subtracting their past 60-day moving average. DIJA residuals are demeaned DIJA returns. Furthermore, to control for heterskedasticity or auto-correlation in regression residuals all regression standard errors are calculated using Newey-West standard errors with five lags. Using these controls makes our regression specifications comparable to those in Tetlock (2007). 3 Journalists and market returns 3.1 Univariate statistics and exogenous rotations Table 3 displays the main results by making a series of simple univariate comparisons. Over each journalist s tenure, Table 3 shows the average DJIA return the day his or her article is published in the WSJ versus the average return on all other days. These are denoted in the table as r wrote and r!wrote respectively. As an example, Table 1 indicates that O Brien authored 1,289 AOTM articles from 1995 to The average DJIA return on the days 6 For example, over this same time period the CRSP Value-Weighted Index annualized Sharpe ratio was also

10 these articles were published was 11 bp. By contrast, the average DJIA return from 1995 to 2002 on the complement set of days when another journalist wrote the AOTM column was 1 bp. This difference is statistically significant at roughly the 4 percent level. Progressing down the table, we see that of the twenty-six WSJ columnists, roughly onethird are associated with significant abnormal returns the day their articles are published. These are split fairly evenly between positive and negative abnormal returns, with perhaps a slight advantage to columnists with higher-than-average next-day returns. In an absolute sense, three journalists (Pasha, Dorfman, and McGee) are associated with particularly striking abnormal returns, ranging from 38 bp per day to +38 bp per day. However, the more representative case, based on number of articles written, implies magnitudes roughly half as large (e.g., O Brien 11 bp, Pettit 11 bp, Raghavan +19 bp). In light of these results, the question is whether we can say anything causal about the return patterns seen in Table 3. That is, does the act of a columnist writing actually generate an abnormal market response? Or are certain columnists simply chosen at certain times that correspond to higher or lower than average returns? One could imagine, for example, that some journalists excel at assessing or describing market crashes, and thus, might be politic choices for these times. The same might be said for booms, either of which might appear capable of generating journalist-based return predictability, but not for causal reasons. However, evidence argues against such a possibility. Note that because r wrote and r!wrote are both constructed over the same time interval the years during which the relevant journalist was active the differences observed in Table 2 cannot be ascribed to broad differences in sample periods. Thus, those who schedule columnists would need not only motivation to select journalists to coincide with times of high or low returns, but also the ability to conduct such market timing over very short horizons. Since short-term returns are virtually unpredictable (Fama (1970)) successfully implementing this would be limited at best. This of course presumes that such a timing incentive exists in the first place. Unfor- 9

11 tunately, the plausibility of this hypothesis is difficult to judge, given that we know little about the process behind how the editors of the WSJ schedule their columnists. However, an examination of journalists schedules reveals that columnists generally adhere to a semiregular rotation schedule. This rotation is not easy to depict in a single figure given the large number of journalists and potential days they could have written, but Figure 1 gives two typical cases. For example, the top plot in Figure 1 compares the schedules of Victor Hillery, the mode author in our sample, and Joseph Rosenberg, both over a three month period during the Fall of The regularity is striking, and in this specific case, corresponds to Rosenberg substituting for Hillery primarily on Fridays, as shown in black bars. The bottom plot in Figure 1 shows a slightly different pattern where Robert Rose and Beatrice Garcia tradeoff writing over longer periods of time during the Fall of At least in these cases, the rotation of columnists appears almost completely deterministic, and consequently, can be taken as exogenous with respect to future returns. 7 Examination of other cases reveals similar, but not identical patterns. For a more formal characterization, we attempt to predict whether a journalist s article runs on a given day based on the recent stock returns. Specifically, we run twenty-six separate regressions of the form: ι(journalist i,t ) = 5 α j r t j + γ [Time Effects] t + ϵ t, (3) j=1 where ι is an indicator variable for whether journalist i wrote an article published on day t. We include in this regression the last five days of market returns, so as to capture any propensity for some journalists to be more or less likely to be selected based on these variables. The vector Time Effects includes indicators for day of the week (e.g., Monday, Tuesday) as well as those for specific month-years (e.g., April 1986, May 1986). We estimate linear 7 Our regressions will control explicitly for day of the week effects (French (1980)), so any differences between these will be irrelevant for our main analysis. 10

12 probability models to accommodate the large number of date fixed effects, although probit estimates (without month-year fixed effects) paint a similar picture. The p-values shown in the final column of Table 1 correspond to those for the joint linear restriction test that all five return lags are simultaneously zero. A low p-value indicates that recent stock returns (days t 5 through t 1), in tandem, are significant predictors of whether a given columnist is selected to publish on day t. As seen however, for no journalist is this the case. In only one case (McGee) do past returns even approach statistical significance, and this is due to a strong effect of day t 4 returns, almost certainly spurious. This evidence, of course, does not entirely preclude columnists from being selected based on determinants of future returns. However, it does imply that such a selection process would have to be based on unobservable determinants, despite no evidence of observable factors playing a role. Given these findings, we proceed under the assumption that columnist selection is not influenced by past returns, and for remaining analysis, can thus be taken as exogenous with respect to future returns. 3.2 Regression To formally characterize the univariate patterns seen in Table 3, we estimate the following linear regression: 26 5 r t = β i Journalist i,t 1 + α j r t j + γ Controls t + ϵ t, (4) i=1 j=1 where r t is the excess return of the DJIA index on day t, as defined in Equation (1). All returns are nominal and are reported in basis points. The variable of interest, Journalist, corresponds to the each of the twenty-six WSJ columnists, i, active from If columnist i is the author of the AOTM written the night of t 1 (for publication the morning of day t), then Journalist i,t 1 takes a value of one, and zero otherwise. As mentioned previously, the Controls vector includes five lags of detrended daily log NYSE volume, five 11

13 lags of detrended squared DIJA residuals, day-of-the-week dummies, and a dummy variable for the month of January. Newey-West standard errors are calculated using five lags. In the third column of Table 4, we present the results of estimating equation (4). Perhaps the most immediate observation is the improvement in R-squared with the addition of journalist fixed effects. Lagged returns and other controls explain roughly 2.7 percent of the variance in daily returns, but explanatory power increases 15 percent to 3.1 percent once journalists for the AOTM market are added. As indicated by the F-statistic and corresponding p-value, this improvement is easily sufficient to justify the inclusion of the twenty-six journalist fixed effects. The joint linear restriction test (β 1 = β 2 =... = β 26 = 0) is rejected at the 1.4 percent level. 8 The similarity between Table 3 and Table 4, column 3 is immediately apparent. Every journalist with a statistically significant point estimate in Table 4 also has a significant difference in raw means shown in Table 3. Moreover, of the fifteen journalists with positive estimated return effects in the univariate comparisons, fourteen are positive in the regression. The same ratio for negatively estimated coefficients is ten to six. The few cases where coefficients flip sign are for journalists with point estimates very close to zero. However, this similarity should not be surprising, given our earlier finding that the timing of journalists is almost completely exogenous. The fourth column presents results from the identical regression, except that the dependent variable, DJIA returns, is advanced one day. Here, we are attempting to explain returns on day t + 1, given that journalist i publishes his AOTM article on day t. Given that readers view the AOTM column at different times of day depending on time zone and personal preference, one might expect journalists effects on returns to show up with a one day lag. Indeed, the results indicate significant effects for returns on day t+1 as a function of articles written (published) on day t 1 (t). The increase in the explanatory power (from to 0.031) and p-value (0.001) are both similar to column 3, as is the number of journalists that 8 The F-statistics reported in Table 4 are, like the standard errors, corrected for heteroskedasticity and auto-correlation (see Wooldridge (2009)). 12

14 are individually significant (four). Note also that with the exception of McGee, the set of journalists that affects day t returns (column 3) is different than the set that influences day t + 1 returns (column 4). Although not our main objective, an interesting observation is that journalists with an immediate impact appear to write more digestible articles. The two journalists with only a significant impact on day t returns Raghavan and Power write articles averaging slightly more than 77 sentences with 12.8 words per sentence and 16.6 percent complex words. For their counterparts with only significant effects on day t + 1 returns Smith, Rosenberg, and Gonzalez these same figures are 91 sentences with 10.8 words per sentence and 17.4 percent complex words. We stop short of making a causal inference from the differences here, noting only that it is consistent with investors having finite cognitive resources, and therefore needing processing time. 9 In the fifth and final column, we see that by the second day after an article is published, any evidence of journalist-specific return effects has vanished. Only a single journalist (Ip) shows a statistically significant effect which, given twenty-six possible candidates, is what chance would predict at the five percent significance level. Analysis of returns past day t + 2 (unreported) yield similar non-results. 3.3 Falsification and discussion Returning to the middle column but proceeding leftward shows the results of two simple falsification tests. For columns 1 and 2 respectively, the dependent variable is now the market s return two and one days before an article by journalist i is published. Finding any statistical significance in such a specification would shut down the causal channel by construction, leaving only model mis-specification to generate correlation between market returns and subsequent media activity. However, examination of the p-values from both columns 1 and 2, indicates quite clearly 9 See for example, Plumlee (2003), Peterson (2004), Engelberg (2008), DellaVigna and Pollet (2009) and Hirshleifer, Lim, and Teoh (2009). 13

15 that such specification concerns are moot. The AOTM s author on day t is completely irrelevant for predicting prior returns, as evidenced by p-values of 0.73 and 0.95 for days t 1 and t 2 respectively. 10 These findings, along with the exogenous timing of authorship, effectively rule out alternatives to a causal interpretation based on statistical problems. Moreover, the finding has nothing to do with outliers or the choice of market index. When we winsorize the return data (Appendix, Table 8), construct open-to-close returns (Appendix, Table 9), or use the S&P 500 (Appendix, Table 10) or the CRSP Value-Weighted Index (Appendix, Table 11), the results are unchanged. It is worth reiterating the implications of Table 4. Beginning on precisely the day of publication, AOTM authors as a group explain approximately 15 percent more variation in daily returns than lagged volume, volatility, and return series combined. This predictability lasts for only two days, and then vanishes as quickly as it came. This timing is exactly what we would expect if specific journalists are responsible for the subsequent abnormal returns we observe. If we were examining the responses of individual stocks rather than a diversified index, an information-based explanation for the price changes might be more tenable. However, even then it would not be particularly plausible, recalling that journalists are associated with specific signs, i.e., either positive or negative. Over the course of a journalist s career, he or she would have to be persistently endowed with either positive or negative information, a claim that seems implausible if not impossible. Perhaps a more natural interpretation is that even when journalists do not have special 10 More formally, we have also considered the possibility of finite-sample biases in the reported p-values of our linear restriction tests in Table 4. Although we have over 9,000 observations, our test is based on the difference between the writing of 26 journalists, many of whom only appear between times in our dataset. In order to address this issue, we simulate the data 10,000 times, creating random journalist fixed effects with the frequencies observed in the data. For example, in each simulation Hillery would have a 2374/9194 = 25.8% chance of writing on any particular day. Our simulations suggest our linear restriction tests may have a small finite-sample bias. The p-value of 1.4% we observe in column 3 of Table 4 we observe in the simulations less than 2% of the time, and a p-value less than the 0.1 percent observed in column 4 is never observed. 14

16 information they are nevertheless able to influence the beliefs or attitudes of investors. Certainly, the idea that journalists can sway opinion is not novel, going back to at least John Milton s Areopagitica, a Speech for the Liberty of Unlicensed Printing in Noting the potential for biased writing to influence opinions, he advocated freedom of the press from explicit governmental influence. Yet even in modern times with a free and competitive media environment, the effects of biased reporting appear alive and well, at least with respect to political outcomes (DellaVigna and Kaplan (2007), and Gerber, Karlan, and Bergan (2009)). Given this evidence, it is perhaps natural to think that dynamics evident in the public arena would apply to financial markets. However, this is not supposed to be the case. Strong and timely market forces work to eradicate the impact of erroneous expectations in securities prices. If prices are too high relative to prevailing information and risk, profit motives will push the price lower, and vice versa. It makes little difference why the bias exists, whether it is for concerns over reputation (Gentzkow and Shapiro (2006)) or to cater to an audience s taste for distorted views (Mullainathan and Shleifer (2005)). 11 In either case, the ability to benefit from mis-pricing would appear to generate a strong theoretical foundation against biased reporting manifesting as return predictability. However, the return patterns seen in Tables 3 and 4 indicate quite clearly that these foundations are insufficient to erase the impact of journalist-specific rhetoric from prices. 3.4 Trading The results in Table 4 would seem to imply an easily implemented trading strategy. Here, we present the results of a simple exercise that demonstrates the out-of-sample predictability coming from journalist identity. First, note that the columnist coefficients in Table 4 have been estimated using the full sample, so that at any point in time, using these would make 11 In Gentzkow and Shapiro (2006), bias arises endogenously due to a media outlet s concern over its reputation for accuracy. Interestingly, although the media may have information it strongly believes to be true, it has an incentive to bias toward the priors of its readership, even when its readers care only about the truth. The reason is that on average, information that wildly contradicts the audience s prior beliefs is more likely to be wrong, which exposes the media outlet to serious reputation impairments. In Mullainathan and Shleifer (2005), a preference for biased reporting is taken as given. 15

17 use of future information. Instead, we classify bulls and bears using only reaction of the DJIA to each journalist s first twenty-five articles. Specifically, we designate a journalist as a bull if the average excess return for the first 25 days he or she has an article published is positive. Likewise, we define bears as those where this average is negative. The second row of Table 5 shows the average excess returns from buying the DJIA the morning when a bull has written the AOTM article and selling at the close. 12 As seen, compared to the average excess return averaged across all days (row 1), the average return on the 38 percent of days bulls are published is 4.3 bp, with slightly smaller variability (97 versus 101 bp). 13 Annualized, the Sharpe ratio for a bull-only strategy is 0.71, approaching twice that for the unconditional DJIA Index with dividends. By contrast, the third row shows that the returns to being long a bear strategy are (expectedly) much worse, averaging only 1.1 bp a day excess return, with 91 bp daily volatility. This corresponds to a much inferior Sharpe ratio of The strategies in rows 2 and 3 are conditional on bulls and bears writing, respectively, and thus do not deploy capital every possible trading day. In the fourth and fifth rows, we modify these respective strategies as follows: 1) the bull strategy invests in the DJIA when bulls write, and in the risk-free asset on other days, and 2) the bear strategy invests in the risk-free asset after bears write, and in the DJIA when bears do not write. As seen, the Sharpe ratios of these strategies despite investing nearly half the time in the risk-free asset still slightly exceed that of the full-time DJIA strategy. Table 5 presents results for only the simplest of trading strategies almost certainly, more sophisticated approaches would generate still more impressive performance. For example, varying the holding periods or using content to further refine the strategy may lead to even more predictability. But the results from the simple strategies shown in Table 5 present clear 12 The results are nearly identical if one calculates returns using close-to-close prices, but traders might not know the identify of the AOTM columnist at this time. 13 The classification of bulls and bears closely matches the findings in Table 4, but does not use the full sample of returns. Bears are Hillery, Talley, Pettit, McLean, Levingston, Steiner, Granahan, Dorfman, Rose, and Bauman. Bulls are O Brien, Marcial, Garcia, Wilson, Smith, Browning, Torres, Sease, Raghavan, Rosenberg, Kansas, Ip, Gonzales, Power, Pasha, and McGee. 16

18 out-of-sample evidence that investors fail to condition out journalist-specific effects. Even when presented with a period of only 25 articles to estimate these effects, journalist-specific rhetoric appears to be effective. 4 Content analysis To this point, we have focused primarily on the exogeneity of journalist timing, emphasizing causality over the underlying mechanism. Here, we attempt to be more specific about the guts behind the return coefficients seen in Tables 3 and 4. Assuming that the return predictability stems from individual columnists, can we say more about the specific means that culminate in their influence on asset prices? Because writers by definition communicate with their audience through the written word, the answer to this question ultimately comes down to differences in word selection, ordering, or other stylistic elements. However, we are faced with two challenges when attempting to move beyond this rather obvious statement. First, because we do not read every article (and even if we did, our reactions would not be the same as those of the marginal investor), our ability to explain the coefficients of individual journalists depends on how well an automated algorithm can approximate a human s response to the written word. Hence, our computational limits impact our success or failure in this step. Second, we are not guided by theory as to what content metrics should matter. The return predictability in Tables 3, 4, and 5 clearly shows that we have identified the behavior of investors susceptible to journalist persuasion. Predicting how specific content measures such as words per sentence and number of syllables per word should affect stock returns seems premature absent a model for how such an irrational class of investors processes information and makes decisions. For these reasons, we have only modest ambitions in this section. We first aim to demonstrate that for the content metrics we can quantify, journalist fixed effects are very important 17

19 determinants. At a minimum, this finding suggests the presence of persistent stylistic differences among columnists, which we know must be at the root of any return predictability. In at least one of these dimensions, Pessimism, our intuition is confirmed columnists with a persistently dour outlook appear to generate abnormally low returns, and vice versa. But our final tests show that even this result does not fully explain the reasons why particular journalists wield more influence than others. There appears to be a columnist-specific residual component, an expertise or nuance of the written word, that cannot easily be summarized other than by specifying a particular journalist s identity. 4.1 Journalists and writing style Tables 3, 4, and 5 show that the authors of the AOTM column influence asset prices. In this section we analyze whether these differing persuasive abilities can be explained by observable differences in the journalists writing styles. In our context, these observable differences might be AOTM article characteristics of the type we have already tabulated in Table 2. However, instead of tabulating the averages of these characteristics for each journalist, Table 6 formally shows statistical differences between them. Beginning at the last column titled Pessimism, we see that journalist fixed effects explain roughly 3 percent of the variation in the AOTM s Pessimism, and in aggregate are highly significant (p < 0.001). The most persistently optimistic columnists are Marcial ( 0.29), Steiner ( 0.25), Raghavan ( 0.22), and Rose ( 0.22), whereas the most pessimistic are Pettitt (+0.34), O Brien (+0.26), and Pasha (+0.24). In interpreting these differences, it is important to note that these represent marginal effects, after controlling for past returns, volume, and volatility. Consequently, the coefficients in Table 6 capture the incremental impact of a given columnist writing, which as we have already seen, is exogenous to past returns. This implies that on average, the arrival of a given columnist coincides with the exogenous dissemination of article sentiment to the market. Although we do not expect impersonal algorithms to perfectly measure an investor s perception of a journalist s bearishness or 18

20 bullishness, it would be surprising if Pessimism were positively related to subsequent returns. Indeed, we find that of the eight columnists with returns statistically significant at the 10 percent level in Table 3, seven of them have point estimates for Pessimism of opposite sign. This implies that columnists who persistently select more negative words (higher than average Pessimism) generate low short-term returns, and vice versa. To illustrate this relationship, Figure 2 plots the journalist fixed effect coefficient estimates from the regression of Pessimism on journalists recorded in column 6 of Table 6 versus the journalist coefficient estimates from the regression of returns on journalists recorded in column 3 of Table 4. For example, from the return regression we see that Raghavan has a coefficient of with a t-statistic of 2.7, and that his point estimate from the pessimism regression is 0.22 with a t-statistic of 3.2. A circle for each journalist is plotted at their corresponding coordinates, with the size of the circle being proportional to the square-root of the number of articles written. Additionally, this plot shows the weighted-least squares line between the two sets of coefficients where weights are proportional to the squared-root of the number of articles written by each journalist. As expected, we see that journalists that correspond to lower (higher) returns on average typically also have higher (lower) levels of pessimistic writing. But while intuitive, even this correlation is limited in its application. It does not explain the columnist fixed effects in the return regressions any more than peering into the shopping cart of a master chef would explain why dinner tastes so good. Presumably, writers for the Wall Street Journal are sufficiently adept at their craft that a computerized summary of an article s tone would capture only the tip of the iceberg. For the sake of completeness, Table 6 presents the results for a number of other content characteristics. Moving across the table, we find that columnist fixed effects are even more important for the number of sentences, words per sentence, and percentage of complex words. Stylistic differences among journalists are extremely important for each of these, with the fixed effects significantly increasing the fit of regression. Likewise, we find that the three 19

21 measures of literary complexity or readability presented in Table 2 are also highly columnistspecific. As an example, Table 6 presents results using the Fog readability score as the dependent variable. In this case, roughly 18 percent of the total variability can be ascribed to average differences between columnists. Unlike Pessimism, intuition provides little guidance for these variables e.g., should authors with more complex sentences be associated with negative abnormal returns? But whether or not these specific content characteristics relate to returns is not important per se. Other than perhaps the prediction that more complex writings appear to take longer for the market to digest (see Table 4 and surrounding discussion), we present these results only to illustrate the considerable variation among columnists. Given the enormous cross-journalist differences we observe for measurable content attributes, it is not unreasonable to expect similar variation in dimensions that existing algorithms currently ignore. In either case, identification still runs through specific journalists, which, as we have already established, can be taken as exogenous. 4.2 Journalists and initial conditions Tables 2 and 6 demonstrate that AOTM articles bear their authors fingerprints in terms of pessimism, length, complexity, and other content attributes. Here, we explore to what extent such stylistic differences are related to the prevailing market conditions. The idea is that some journalists might sound the alarm in bad times, while others will focus on positive aspects. If so, then the impact of particular columnists will change when interacted with recent returns. Table 7 shows the results of this exercise. The specification is the same as in the last column of Table 6, except now we add interactions for each journalist r t 1. The coefficients on these interactions capture how recent returns alter each journalist s marginal contribution to measured Pessimism, or alternatively, how the presence of a specific journalist alters the mapping of past returns to AOTM Pessimism. Because this unconditional relationship is al- 20

22 ways negative (negative returns lead to more Pessimism), a positive coefficient indicates that a journalist s contribution to article Pessimism is moderating, while a negative coefficient indicates an amplifying effect. As a specific example, consider David McLean who, as shown in Table 1, authored 102 AOTM articles. The coefficient on his fixed effect ( 0.14) indicates that on flat market days, McLean writes slightly more optimistic content than a typical columnist. 14 However, the coefficient on the interaction (point estimate 0.41, t-statistic 4.5) suggests that McLean s content seems to amplify the marginal effect of returns on sentiment. In fact, this magnitude is nearly identical to the coefficient on r t 1 (point estimate 0.41), indicating that after a 100 bp decline, the presence of McLean roughly doubles the Pessimism, relative to a generic journalist. Continuing with the example, there were exactly 10 days that McLean wrote following a market decline of 1 percent or more. On those days, McLean s articles had a Pessimism score of 1.15, up sharply from 0.08 on days when the market was flat. By comparison, the other 25 journalists had an average Pessimism score of 0.98 on days when the market fell by more than 1 percent and a Pessimism score of 0.00 otherwise. Thus, while a 1 percent decline might roughly increase Pessimism by one standard deviation for a typical journalist, this effect is 26 percent stronger for McLean. Examining the interaction coefficients for the other journalists, we see that roughly half are significant at the 10 percent level and nearly a forth at the 1 percent level, both far more than what we saw in Table 4. Overall, journalist r t 1 interactions are jointly significant in a linear restriction test (p-value < 0.001) and increase the R-squared from 29 percent to 31 percent. Moreover, the increase in statistical significance indicates that the crossjournalist effects we observed earlier are best appreciated when conditioned on past returns. As formalized in the first column of Table 7, some journalists appear to hype the previous days returns, while others seem to play them down. 14 Recall that 343 of our observations have an unnamed journalist write the AOTM column. Therefore the coefficient on r t 1 is the marginal effect on r t 1 on sentiment for these observations. 21

23 In the final three columns of Table 7 we take the analysis a step further, and consider that journalists might have a differential influence on future returns, as a function of past returns. Now the dependent variable is the DJIA return on day t rather than Pessimism on day t. As in column 1, we see that many of the journalist interactions are significant, and jointly, they are significant at better than the 1 percent level. Continuing the example of journalist McLean and examining column 2, the coefficient on his indicator alone suggests that on days he writes, the market experiences almost no abnormal return (+2 bp). However, the coefficient on the interaction term is 0.33 (t-statistic of 3.6), suggesting that on average, the effect of McLean writing is to perpetuate recent return patterns. Given that his content amplifies recent return patterns (the negative coefficient in column 1), this is the expected result. Perhaps a less obvious, but no less important, point is the marked increases in explanatory power conferred by the interaction terms. Compared to the properly specified columns in Table 4, the R-squared values seen in Table 7 are in the neighborhood of four to seven percent. It is noteworthy that the improvement in R-squared comes almost exclusively from the interactions not from the lagged returns themselves. The final columns of Table 7 treat good times and bad times separately by interacting the journalist fixed effects with M t 1 = max{0, r t 1 } and M t 1 = min{0, r t 1 } in columns 3 and 4 respectively. This allows, for example, a contrarian author to be so only in bad times. Here we find that the journalist M t 1 interactions appear much more important in bad times (column 4). This evidence is consistent with García (2010) who finds the predictive power of textual content much greater in recessionary periods. Taken together, the results in this section paint a clearer picture of the power of individual rhetoric in financial journalism. If we think of journalists as actors with the goal of persuading an audience, the results in Section 3 suggested that an actor s identity alone tells us something about the performance he will give. However, Table 7 indicates that an actor s performance also depends upon the stage he is given. When the stage is set for good 22