Does Public Financial News Resolve Asymmetric Information

Does Public Financial News Resolve Asymmetric Information?

Paul C. Tetlock*

April 2010

Abstract

I use uniquely comprehensive data on financial news events to test four predictions from an asymmetric information model of a firm’s stock price. Certain investors trade on information before it becomes public; then public news levels the playing field for other investors, increasing their willingness to accommodate a persistent liquidity shock. Empirically, I measure public information using firms’ stock returns on news days in the Dow Jones archive. I find four return predictability and trading volume patterns following news that are consistent with the asymmetric information model’s predictions. Some evidence is, moreover, inconsistent with alternative theories in which traders have different interpretations of news for rational or behavioral reasons.

* Roger F. Murray Associate Professor of Finance at Columbia Business School. I thank Wes Chan, Kent Daniel, Larry Glosten, Amit Goyal, Gur Huberman, Charles Jones, Eric Kelley, Anthony Lynch, Chris Parsons, Paolo Pasquariello, Gideon Saar, Mark Seasholes, Matthew Spiegel, Avanidhar Subrahmanyam, Heather Tookes, two anonymous referees, and seminar participants at Columbia University, Global Alpha, the HKUST Asset Pricing Symposium, the NBER Microstructure meetings, the NY Fed, NYU, Princeton, UNC, and the WFA meetings for their comments. I am grateful to Dow Jones for providing access to their news archive. Please send comments to paul.tetlock@https://www.wendangku.net/doc/f4130496.html,.

This study uses 29 years of data on all publicly traded US firms in the Dow Jones news archive to examine how firms’ information environments change during 2.2 million news events. This is one of the largest quantitative records of financial news events ever constructed, allowing for a uniquely comprehensive analysis of the role of news in stock pricing. I propose and test a model of a firm’s stock price in which a public news story eliminates an information asymmetry between two groups of traders. Before the news, one investor group has superior information, but also incurs a persistent liquidity shock. Then the news story informs the relatively uninformed investor group, making them less wary of providing liquidity to the informed traders. Even so, because they are risk averse, the relatively uninformed investors do not fully accommodate the liquidity shock on the day of the news event. This theoretical model is similar to the Kim and Verrecchia (1991), Wang (1994), Holden and Subrahmanyam (2002), and Llorente, Michaely, Saar, and Wang (2002) (hereafter LMSW) models, but differs in its explicit assumptions about the role and timing of public news.

This paper’s contribution is to test four predictions from this model using uniquely comprehensive news data, along with firms’ stock returns and trading activity. This model’s first prediction is that the firm’s return on a news day positively predicts its returns after the news. The intuition is that the gradual dissipation of the liquidity shock after the news leads to return momentum. Second, returns on high-volume news days are better positive predictors of post-news returns than returns on low-volume news days. The reason is that news that resolves more asymmetric information facilitates more absorption of the pre-news liquidity shock, resulting in both higher trading volume at the time of news and higher return momentum after news.

Third, the contemporaneous correlation between the firm’s trading volume and the magnitude of its price changes temporarily increases around news days. As news occurs, both volume and price changes are driven by the belief revisions of uninformed investors because informed investors already know the news. The uninformed investors increase (decrease) their stock holdings when they learn from the news that the stock’s expected returns are higher (lower), producing a high correlation

between volume and the magnitude of price changes on news days. Fourth, the price impact of

informed trading in the firm’s stock temporarily decreases as news reduces information asymmetry.

I measure public news events using the entire Dow Jones (DJ) archive, which includes all DJ newswire and all Wall Street Journal (WSJ) stories about publicly traded US firms from 1979 to 2007. I compare stock returns and trading activity on news days and non-news days using daily cross-sectional regressions in the spirit of Fama and MacBeth (1973). This analysis produces four main results: 1) ten-day reversals of daily returns are 38% lower on news days; 2) ten-day volume-induced momentum in daily returns exists only on news days for many stocks; 3) the cross-sectional correlation between the absolute value of firms’ abnormal returns and abnormal turnover is temporarily higher by 35% on news days; and 4) the price impact of order flow is temporarily lower by 3.3% on news days. These four findings suggest that some traders have already acted on the information released by public news, whereas other traders use news to learn about expected returns. The second and third empirical findings are novel, whereas the first and fourth findings significantly extend previous results.1

Although these four qualitative results are robust over time and across stocks with different characteristics, the magnitudes of the effects vary substantially. News is a better predictor of reduced return reversal in small firms, which suggests that each news story conveys more information for these firms. The link between news and reduced return reversal is also stronger for stories that consist of many newswire messages and earnings-related words, which are plausible proxies for the information content of news.

For small stocks and illiquid stocks, volume-induced return momentum occurs only on news days, whereas volume-induced reversal occurs on other days. This could indicate that public news

resolves more asymmetric information in these firms. The correlation between absolute returns and

1 Karpoff (1987) and others find a robust positive correlation between volume and absolute price changes. Smirlock and Starks (1988) show that this relationship is particularly strong around earnings announcements for 300 firms spanning 49 trading days, but they do not investigate other news events.

volume declines by a larger amount following news stories that consist of many newswire messages and earnings-related words, and for small stocks and illiquid stocks. This suggests that the role of public information in resolving privately held differences in opinion is stronger for small stocks and illiquid stocks. Conversely, I find no clear evidence that news predicts return reversals, which are typically associated with the arrival of liquidity shocks. One interpretation is that the release of news coincides with information more often than it coincides with liquidity shocks.

Several empirical design choices minimize the likelihood that the results are spurious. First, I focus on weekly time horizons for return reversals because the evidence in Jegadeesh (1990) and Lehmann (1990) shows that weekly return reversals dominate one-day autocorrelations. In these tests, I skip day one to avoid microstructure biases, such as bid-ask bounce, that affect return correlations in consecutive periods.2

Second, I present the four main results for firms in the top and bottom size and liquidity quintiles separately based on the LMSW (2002) findings that these stocks’ information environments differ. Although the effects are often stronger for small and illiquid stocks, all four results hold in both groups. This demonstrates that the results are statistically robust and economically important. At the same time, the consistently stronger findings for small stocks and illiquid stocks hint at a role for information asymmetry. By contrast, the main results are never stronger and are sometimes actually weaker for stocks with high analyst forecast dispersion and low institutional ownership. This variation is inconsistent with several alternative theories in which investors interpret news differently for rational or irrational reasons, such as Kim and Verrecchia (1994) or Harris and Raviv (1993).

Third, I use daily cross-sectional regressions—in the spirit of Fama and MacBeth (1973)—to control for a wide range of influences on firms’ stock returns. The regressions simultaneously test the

2 Another benefit is that the holding period return excludes the positive one-day autocorrelation that Sias and Starks (1997) link to institutional ownership. It is possible that institutional order splitting across days causes two-day price pressure that reverses at longer horizons. Indeed, recent evidence in Kaniel, Saar, and Titman (2007) and Barber, Odean, and Zhu (2009) demonstrates that price pressure from trading clienteles develops and subsides over multi-week horizons. Accordingly, I explicitly analyze whether institutional ownership affects the results.

model’s first two predictions for expected returns, while controlling for other known well-known predictors of returns such as size, book-to-market, return momentum, return volatility, abnormal turnover, and several other variables. I present these regressions separately for firms that differ in alternative measures of the information environment such as analyst coverage to ensure that news is distinct from other proxies for information asymmetry.

This paper contributes to three literatures. One is the volume-induced return reversal literature, which includes a complex set of results. Whereas Conrad, Hameed, and Niden (1994) show that return reversals for relatively small Nasdaq stocks decrease with trading volume, Cooper (1999) shows that return reversals for larger NYSE stocks increase with trading volume. Avramov, Chordia, and Goyal (2006) find that volume-induced return reversal increases with stock illiquidity. I confirm that large stocks and liquid stocks exhibit volume-induced momentum, whereas small stocks and illiquid stocks exhibit unconditional volume-induced reversals. I find, however, that small stocks and illiquid stocks actually exhibit volume-induced return momentum on public news days, just as large stocks and liquid stocks do on all days. The findings here complement the volume-induced reversal findings in LMSW (2002). Whereas LMSW (2002) do not directly measure firms’ information environments, I analyze the impact of public news releases on volume-induced and unconditional return reversals. I also investigate how the correlation between absolute returns and volume changes and how price impact changes around public news events. The upshot is that I provide new evidence on how investors obtain information that is relevant for firm valuation and which public signals resolve information asymmetries across investors.

This paper also contributes to a growing literature on the impact of public news releases, which includes Stickel and Verrecchia (1994), Pritamani and Singal (2001), Chan (2003), Chae (2005), Vega (2006), Chava and Tookes (2007), Gutierrez and Kelley (2008), and Tetlock, Saar-Tsechansky, and Macskassy (2008). Of these papers, Chan (2003) and Gutierrez and Kelley (2008) are most closely related to this study. The first result in this paper extends the monthly and weekly

findings in Chan (2003) and Gutierrez and Kelley (2008) to daily return reversals around public news. This is not trivial because the correlations between daily returns on news days and weekly and monthly returns surrounding public news are only 0.560 and 0.299, respectively. Interestingly, these correlations are 0.618 and 0.350 on news days with positive abnormal turnover, but just 0.321 and 0.120 on other news days. Neither Chan (2003) nor Gutierrez and Kelley (2008) explores this link between trading activity and returns on news days. By contrast, I analyze whether news predicts changes in volume-induced return momentum, the correlation between absolute returns and volume, and price impact.

This study differs from Stickel and Verrecchia (1994), Pritamani and Singal (2001), Vega (2006), Tetlock et al. (2008), and Tetlock (2009) because it compares high-frequency return reversals on news and non-news days. All of these earlier studies analyze reversals and momentum solely on news days, and the first three look at only earnings news.3 This study’s evidence on return predictability complements the evidence in Chae (2005) and Chava and Tookes (2007), which both mainly analyze trading volume around news events. This study differs from Tetlock et al. (2008) and Tetlock (2009) in its comparison of news and non-news events and its use of news data on the entire cross-section of publicly traded firms.

A third related literature examines intra-day responses to public information—e.g., Lee, Mucklow, and Ready (1993), Fleming and Remolona (1999), Green (2004), and Pasquariello and Vega (2007). The empirical focus of this paper on daily expected returns, correlations between returns and volume, and price impacts differs from the microstructure emphasis on intra-day spreads and depths. A key reason is that, although the news events in this sample have precise time stamps, these time stamps often do not correspond to the intra-day timing of the release of the underlying

3 Stickel and Verrecchia (1994) show that post-earnings announcement drift (PEAD) increases with announcement-day trading volume. Vega (2006) shows that PEAD is higher for firms with low measures of PIN, differences of opinion on public news days, and low media coverage. Tetlock et al. (2008) shows that the words in public news releases predict firms’ future cash flows and their stock returns, albeit briefly. Tetlock (2009) finds that news-day return reversal is higher when prior news, high return volatility, or high liquidity precedes the news day.

information event. Thus, I focus on daily market reactions to news because news usually occurs on the same day as the information event. A benefit of testing the daily expected return predictions of microstructure theory is that these predictions receive less attention in the recent empirical literature on public information events.

Although this paper adopts an identification strategy based on a rational market microstructure model, one could frame many of the empirical results as tests of behavioral asset pricing theories. The two classes of models are not mutually exclusive because specific behavioral biases could motivate the “liquidity” trading in microstructure models. For example, one could relate the results here on return reversals, volume-induced momentum, and the correlation between absolute returns and volume to predictions in the Barberis, Shleifer, and Vishny (1998), Daniel, Hirshleifer, and Subrahmanyam (1998), or Hong and Stein (1999) over- and underreaction models. This behavioral interpretation, however, does not seem necessary because the rational paradigm appears to explain the data and because none of the four news effects is significantly stronger in stocks with low institutional ownership.

I now provide a brief overview of the paper. In Section I, I introduce a simple model of how news resolves information asymmetries that makes four empirical predictions. In Section II, I describe the key empirical measures and present several summary statistics. In Section III, I use regressions to estimate return reversal and volume-induced reversal on news days and non-news days. I present the correlations between absolute returns and volume and the price impact results in Section IV. I provide a concluding discussion of the results in Section V.

I. A Stylized Model in Which Public News Resolves Information Asymmetry

The model here inherits its key economic features from Wang (1994) and LMSW (2002), but makes additional assumptions about the role and timing of public news stories. The model features

three trading periods, two groups of investors, one risky asset, and one risk-free asset. As in Wang

(1994), one investor group (i ) has a temporary informational advantage, but also incurs a privately

observed liquidity or endowment shock. This group combines the traditional roles of informed

traders and liquidity traders, who introduce noise. The other investor group (u ) is relatively

uninformed, but is also rational. Each group is comprised of many investors who behave

competitively as price takers. All investors have constant absolute risk aversion (CARA) utility

functions defined over consumption in period 3, after the risky asset’s liquidating dividend occurs.

The CARA assumption implies that investors’ asset demands do not depend on their wealth.

In periods 0, 1, and 2, both investor groups myopically choose how much to invest in the

risky and risk-free assets. In period 3, they consume their wealth, which depends on the risky asset’s

liquidating dividend. The myopia assumption increases tractability, but does not affect the model’s

qualitative predictions. For simplicity, the risk-free asset pays a zero rate of return and there is no

time discounting. The risky asset supply is normalized to one unit; the supply of the risk-free asset is

perfectly elastic; and both investors’ have risk aversion equal to one.

The informed investors receive a signal in period 1 (s 1) about the firm’s liquidating dividend

() that occurs in time 3, where d is a constant. The signal is normally

distributed according to . Each of the three random components of the dividend is

independently normally distributed according to 32113e e e s d d ++++=1s ),0(s V N ～),0(e t V N e ～and is revealed publicly in period t ,

where t = 1, 2, or 3. In period 2, a public news announcement reveals the signal (s 1) to the

uninformed investors.

In period 1, the informed investors incur a persistent liquidity shock to their endowments of

stock holdings equal to per investor, which is normally distributed according to .

Importantly, this shock persists until after period 2, when the news occurs. Although the uninformed

investors do not observe this liquidity shock, they make rational inferences about its value based on

1n ),0(1n V N n ～

the observed market price in period (p 1) and the initially private signal (s 1) that becomes public in

period 2. A natural interpretation of the model is that the uninformed investors are risk-averse market

makers, who act competitively and rationally.

The timeline below summarizes the key events in the model:

One can compute the equilibrium using standard backward induction techniques. Denote

investors’ demand functions in period t by x it and x ut . From the myopic CARA assumption, informed

demand per investor, excluding the liquidity shock, is:

)

()(11++?=t it t t it it p Var p p E x where the subscripts denote investor group i ’s conditional expectations based on information

available at time t . Demand for each investor in the uninformed group is the same, except the i

subscripts are u subscripts. To obtain the two group demands, one multiplies the individual investor

demands by the total group sizes, which are m for the informed and (1 – m ) for the uninformed.

it By setting demand equal to supply in period 2, I solve for the equilibrium market price:

11212n mV s e e V d p e e ++++?=

The news release allows investors to perfectly infer the value of the private liquidity shock (n 1),

making it effectively publicly observable. As in the Campbell, Grossman, and Wang (1993) model, a

publicly observable liquidity shock has a temporary impact on the stock price.

I look for an equilibrium in period 1 in which p 1 is linear in s 1 and n 1:

1111n b s b e a p n s +++=

where the equilibrium values of the signal and liquidity shock coefficients (b s amd b n ) will be

determined below. Anticipating the linear form of the pricing function, the uninformed investors use

the observed price to learn about s 1 and n 1. Applying the market clearing condition in period 1,

solving for the equilibrium price, and matching the three pricing coefficients on the constant, signal,

and liquidity shock terms yields the solutions:

()()1)1(1)1(21),,,(0222<++++++=

e e n e s n e e n e s e n s s V V m V V mV V V V m m V V mV V V V V m b ()()e n

e e n e s n e e n e s e e n s n e mV V V m V V mV V V V m m V V mV V V m V V V m b mV 2)1(1)1(21)1(),,,(222<+++++++=< ()n

e e n e s s n e e V V m V V mV V V V V m V d a 222)1(1)1(2+++???= Applying the market clearing condition in period 0, when there is symmetric information and no

liquidity shocks have occurred, the initial price is:

)

(3220n n s s e V b V b V d p +??=One can use the pricing and demand equations above to compute abnormal returns and volume in

periods 1 and 2. The period 2 abnormal return (r 2) is the unexpected difference in prices:

21112122)),,,(()),,,(1())((e n V V V m b mV s V V V m b p p E p p E r e n s n e e n s s +?+?=???=

Similarly, the period 3 abnormal return (r 3) is:

1323233))((n mV e p p E p p E r e ?=???=

To compare firms in the news to those out of the news, I analyze the model’s comparative

statics with respect to V s in the parameter region where V s approaches zero. This region is appropriate

for analyzing the impact of a single daily news story that represents a small fraction of a firm’s total

return variance. I also verify in simulations that the theoretical results below hold for larger changes

in V s . Because increasing V s holding the other parameters fixed increases the firm’s total return

variance, I conduct further simulations in which reductions in V e exactly offset the effect of

increasing V s on return variance. None of the comparative statics below change in this specification.

The model’s first two empirical predictions focus on expected post-news returns (r 3). Using

the return equations for r 2 and r 3 above, we compute the return predictability coefficient: 0)),,,(()),,,(1()),,,(()()

(222,3>?++??=n e e n s n e s e n s s n e e n s n e V mV V V V m b V V V V V m b V mV V V V m b mV r Var r r Cov (1)

The derivative of this expression with respect to V s is positive at V s = 0, as Proposition 1 states:

Proposition 1: The regression coefficient of post-news (r 3) returns on news-event returns (r 2)

increases with the informativeness of news (V s ).

The reason is that news, by resolving information asymmetry in period 2, induces the uninformed

investors to partially accommodate the (period-1) liquidity shock from informed investors. In period

3, the remainder of the liquidity shock dissipates. The gradual accommodation of the same (period-1)

liquidity shock in periods 2 and 3 is what causes the positive covariance in returns in periods 2 and 3.

Empirically, I compare return autocorrelations for firms with and without news on the same day.

The model’s second and third empirical predictions are based on the fact that trading volume

in the news period increases with the informativeness of news (V s ). I compute the abnormal trading

volume in period 2 (T 2) as the absolute value of the unexpected change in the informed investor

group’s holdings between periods 1 and 2: 1112122)],,,(2[)],,,(1[))((n V V V m b mV s V V V m b V m x x E x x m T e n s n e e n s s e i i i i ?+?=???=(2)

This equation shows that trading volume follows a folded normal distribution, where the variance of

the underlying normal is proportional to .

n e n s n e s e n s s V V V V m b mV V V V V m b 22)],,,(2[)],,,(1[?+?

Using the standard expression for the expectation of a folded normal variable, one can show that the

derivative of expected abnormal trading volume with respect to news informativeness (V s ) evaluated

at V s = 0 is strictly positive, as Proposition 2 states.

Proposition 2: Expected trading volume (T 2) increases with the informativeness of news (V s ).

Empirically, news is often a binary (0 or 1) variable that is only positively correlated with the

underlying informativeness of news (V s ). Proposition 2 suggests that the extent of trading volume is a

complementary proxy for V s , identifying news days that coincide with more resolution of asymmetric

information and more absorption of the liquidity shock. Thus, the model’s second prediction is that

the return predictability coefficient in equation (1) and Proposition 1 increases with the amount

trading volume on a news day.

A second implication of the trading volume equation is that the correlation between volume

and the absolute value of returns in the news-event period increases with the informativeness of

news. The correlation between absolute returns and volume is given by:

)2/()2)arccos(2))(sin(arccos 2(|)|,(22????+?=πρπρρρr T Corr

where ))),(((21212r x x E x x m Corr i i i i ???=ρ

The correlation unambiguously increases with news informativeness at 0=s V : 02)arccos(2|)|,(22>???=??s s dV d ρV r T Corr ππρ (3)

Both terms in equation (3) are negative at V s = 0, implying that their product is positive.

Proposition 3: The correlation between trading volume and absolute returns in the news-event period

increases with news informativeness (V s ).

The intuition is that news simultaneously reveals informed traders’ signal (s 1) and the value

of the persistent liquidity shock (n 1) to previously uninformed investors. If they learn that the shock

is negative (positive), then future expected returns will be higher (lower) than expected, motivating

uninformed investors to partially accommodate the shock. That is, the revelation of the signal

through news facilitates the absorption of the liquidity shock. More unexpected news leads to both

larger price changes and increased accommodation of the liquidity shock at the time of the news.

Lastly, I examine the price impact of informed trading, which is defined as the regression

coefficient of returns on informed order flows. In the news period, price impact is: n n e s s n e n n e s s e i i i i i i i i V b mV V b V mV b b mV V b m V x x E x x Var x x E x x r Cov 222121212122)2()1())(2()1())(())(,(?+???+????????=?????? (4)

The first term in the expression above shows that the impact of increasing the variance of the signal

(V s ) is to reduce price impact in period 2, as stated in Proposition 4:

Proposition 4: Price impact in the news-event period decreases with news informativeness (V s ).

To see the intuition, suppose that there will be important news released in period 2. Before

this news is released, in period 1, informed traders face a high cost of meeting their liquidity needs,

leading them to trade more on information and less on liquidity. When the news arrives, however,

informed traders will unwind their information-based trades and trade solely for liquidity reasons,

making their price impact appear low. Thus, the fourth prediction of the model is that the price

impact of informed trading decreases as news resolves asymmetric information. Empirically, I use

the Lee and Ready (1991) algorithm for signing order flow to identify informed trades. This

identification approach is valid if the informed group trades more aggressively than the uninformed

group, who effectively act as market makers in this model.

II. Data Description

The primary data source is the Dow Jones (DJ) news archive, which contains all DJ News Service and all Wall Street Journal (WSJ) stories from 1979 to 2007. For each news story in the archive, there are often multiple newswire messages corresponding to separate paragraphs that DJ releases individually. I use the DJ firm code identifier at the beginning of each newswire to assess whether a story mentions a publicly traded US firm. Unfortunately, my manual review of the news stories prior to November 1996 reveals that stories without any firm codes sometimes mention US firms—i.e., the DJ firm codes contain measurement error. More seriously, DJ may back-fill firm codes prior to November 1996 in a systematic fashion that introduces survivorship bias in the data. This survivorship bias does not seem to affect stories after November 1996. Between 95% and 99% of sample firms have news coverage in each year after 1996.

Subperiod analyses—most of which appear in the tables that follow—show that all the main results hold before and after 1996. Using other subperiod cutoffs does not affect these findings. In general, the results are either similar or somewhat stronger in the 1997 to 2007 period, which is not subject to survivorship bias. The fact that survivorship bias does not strengthen the results may be attributable to this paper’s focus on high-frequency return, volume, volatility, and news measures, which do not depend heavily on accurate estimates of stocks’ long-run expected returns.

Even so, I explicitly examine the relationship between stocks’ long-run returns and media coverage to gauge the potential impact of the survivorship bias. I am able to replicate the key Fang and Peress (2009) finding that one-month expected returns are lower for stocks with some media coverage. If anything, this effect is slightly larger in the current data set, which suggests that survivorship bias does not materially affect expected returns. This result also mitigates broader

concerns about survivorship bias because one-month returns are more likely than daily or weekly returns to show evidence of survivorship bias.

The main regression tests use data on news, returns, volume, and firm characteristics. The measure of firm-specific news coverage is an indicator variable (News it = 0 or 1) that is equal to one if firm i’s DJ code appears in any stories in the archive between the close of trading day t – 1 and the close of trading day t. I match the DJ firm codes to US ticker symbols in CRSP by trading date. I match each firm’s news and returns data to accounting (CompuStat), analyst forecast (IBES), institutional holdings (Thomson 13f), and stock transaction data (TAQ).

The analysis below focuses on economically important firms with reliably measured trading returns. The sample includes only stocks with positive trading volume on all days from t – 60 to t – 1, and stocks with prices that exceed $5 on day t – 1. These requirements eliminate many small and illiquid firms, most of which have very few news stories anyway. The sample includes only US firms with common equity (share codes 10 or 11 in CRSP) listed on the NYSE, NASDAQ, or Amex exchange. After imposing these requirements, 13,842 unique firms appear at some point in the 29-year sample. Of these firms, 9,452 have news stories on at least one trading day. This 68% coverage percentage is considerably higher than coverage in Fang and Peress (2009), but somewhat lower than coverage in Chan (2003). The missing firm codes in the pre-1997 DJ archive appear to account for the discrepancy with Chan (2003).

[Insert Figure 1 here.]

Figure 1 depicts the monthly average of the daily percentage of eligible firms covered in the DJ archive. Between two and five percent of firms appear in the archive on most days in the 1980s, whereas 20% to 35% of firms are mentioned on most days in the post-2000 period. I also compute three long-horizon coverage measures for trading days that meet the sample inclusion criteria: the percentage of firms with at least one news story in the current month; the percentage with news in the most recent 12 months; and the percentage of trading days in the most recent 12 months that a firm

appears in the news for the firm at the 90th percentile. This last measure shows how news coverage evolves for the most widely followed firms. All four coverage measures increase over time, and the yearly coverage measure jumps to over 95% shortly after November of 1996. In 1980, news stories occur on 10% of trading days for the firm in the 90th percentile of coverage, but they occur on 60% of trading days in 2007.

III. The Impact of News on Return Reversals

A. Regression Estimates

In the model in Section I and in several related models, liquidity shocks predict larger return reversals and the release of information predicts smaller return reversals. To evaluate whether public news coincides with liquidity or informational shocks, I examine whether news on day t predicts a larger or smaller reversal of firm i’s day-t excess stock return (Ret it). For simplicity, I define Ret it as the firm’s raw day-t return minus the value-weighted market return. The dependent variable is the firm’s ten-day raw return from trading day t+2 through day t+10 (Ret i,t+2,t+10), where I omit day t+1 to mitigate bid-ask bounce. The ten-day horizon matches earlier papers, such as Tetlock et al. (2008), that explore return momentum around news. The results are very similar with a five-day horizon. I define Ret i,t+2,t+10 using raw returns for ease of interpretation. The results below are not sensitive to the specific risk benchmarks chosen because the regressions include controls for several firm characteristics and because short-horizon return predictability is often robust to benchmark selection (Fama (1998)).

The controls for firm characteristics that predict expected returns include monthly measures of firm size (Size it), book-to-market (BM it) ratio, yearly return momentum excluding the most recent calendar month (Mom it), and average daily return volatility during the previous calendar month (TVol it) using standard techniques. I define the size and book-to-market variables as in Fama and

French (1992), the momentum variable as in Jegadeesh and Titman (1993), and the total volatility variable as in Ang, Hodrick, Xing, and Zhang (2006).4 Most regression specifications include abnormal turnover (Turn it) to control for the high volume return premium of Gervais, Kaniel, and Mingelgrin (2001). For consistency, I use the same turnover variable in the interaction terms below that measure volume-induced reversal. Thus, I use the abnormal turnover definition from Campbell, Grossman, and Wang (1993): the log of daily turnover (share volume over shares outstanding), detrended using a rolling 60-day average of log turnover.

In all regressions, the set of independent variables includes the news indicator (News it) and an interaction between news and day-t excess returns (news it*Ret it). Because news coverage is strongly related to firm size (e.g., Chan (2003), Vega (2006), Engelberg (2008), and Fang and Peress (2009)), I include an additional variable (size it*Ret it) to control for possible interactions between size and reversals. To reduce multicollinearity with the size interaction (Size it*Ret it), I demean News it by size quintile on each day t before computing the news interaction term (news it*Ret it).5 I also demean Size it by the mean size for all firms in the sample on each day t before computing the size interaction term (size it*Ret it). Lowercase letters denote the demeaned news and size variables. Throughout this paper, I demean all independent variables before computing interaction terms. The only exceptions are abnormal turnover and excess returns, which both already have means approximately equal to zero by construction.

The regression includes an interaction term to control for volume-induced momentum (Turn it*Ret it) because news and volume are correlated. It also includes an interaction term between news and turnover (news it*Turn it) as a control, in case the high volume return premium depends on the occurrence of news. I also include a triple interaction term (news it*Turn it*Ret it) to assess whether

4 To reduce positive skewness, I compute the logarithms of the size, book-to-market, momentum, and volatility variables. I add constants (k) before computing the log of each variable (x) so that the slope of ln(k+x) is equal to one when x is evaluated at the variable’s unconditional sample mean. This does not affect the results.

5 The regression results based on the raw news variable are qualitatively similar.

volume-induced return reversal depends on news. This coefficient estimate is the basis for testing two auxiliary predictions of the theory that news resolves asymmetric information: first, volume-induced return reversals (momentum) will be lower (higher) on days with news; and second, the impact of news on volume-induced return reversals will be larger for stocks with higher information asymmetry. The complete regression specification is:

Ret i,t+2,t+10 = a + b1 * Ret it + b2 * news it*Ret it+ b3 * Turn it*Ret it+ b4 * news it*Turn it*Ret it

+ c * Controls it + e it for all i on each day t(5) where Controls it = [News it size it*Ret it news it*Turn it Turn it Size it BM it Mom it TVol it]T is an 8 by 1 column vector and c is a 1 by 8 row vector of coefficients. The news-related reversal and news-related volume-induced reversal coefficients (b2 and b4) are the focus of this section.

In the spirit of the Fama and MacBeth (1973) method for estimating expected returns, I estimate equation (5) daily using the cross-section of all firms on each day. Using data from all days increases the efficiency of the regression estimates relative to throwing away data (e.g., Hansen and Hodrick (1980)), which would be necessary if I used weekly or biweekly regressions. I compute the full sample coefficient estimate as the time series average of the daily cross-sectional regression coefficients.6 Using an unweighted average disregards the standard error of each daily coefficient estimate, which is generally inefficient. Instead, I weight each daily coefficient estimate using the inverse of the variance of the daily coefficient as suggested in Ferson and Harvey (1999).7 Because consecutive daily estimates are based on return observations with overlapping nine-day time horizons, the daily estimates of the cross-sectional regression coefficients are positively autocorrelated. Thus, I compute Newey-West (1987) standard errors that are robust to autocorrelation

6 I ignore monthly estimates from months with fewer than 100 firm-days with news stories. This criterion binds only when I divide the sample by firm size, liquidity, analyst coverage, and other characteristics.

7 Even though the standard error of each daily coefficient is biased downward, using the standard errors as weights does not induce a bias in the weighted average if the downward bias is proportional. The reason is that the average weighting cancels in the numerator and denominator of the weighted average.

up to 10 daily lags and heteroskedasticity in the daily coefficient estimates. Using additional lags has no material impact on the inferences.

[Insert Table 1 here.]

Table 1 reports coefficient estimates for all variables in equation (5). The first key result is that the coefficient on the news interaction term (news it*Ret it) is positive, statistically significant, and economically significant. Reversals on days [2,10] of returns on day 0 are 4.2% lower when news occurs on day 0. By contrast, the size of the average reversal—represented by the coefficient on

Ret it—is 9.8% of the day 0 return. Using the coefficients on Ret it, news it*Ret it, news it*Turn it*Ret it , and Turn it*Ret it, along with the average values of news it , news it*Turn it , and Turn it on news days and non-news days, the reversal on news and non-news days are equal to -6.4% and -10.2% of the daily return, respectively. This implies that the reversal of day 0 returns is 38% lower if news occurs on day 0. One can also compare the reversal sizes in basis points rather than percentages of daily returns. The standard deviation of returns on news days is 3.85%, whereas the standard deviation on non-news days is 2.75%. Multiplying these standard deviations by the percentage reversals above, one sees that the news-day return reversal of 39 bps is over 31% lower than the non-news-day reversal of 56 bps. The observed difference of 17 bps in the news and non-news return reversal understates the importance of public information arrival if the news indicator variable is a noisy proxy for public information. The results in subsequent tests that allow reversal to depend on public news characteristics support this view.

The second main result in Table 1 is that the regression coefficient on the news it*Turn it*Ret it variable is consistently positive, statistically significant, and economically significant. The fourth row in Table 1 shows five regression specifications that differ in whether they exclude earnings or non-earnings news and in which period they cover. The robustness in the news it*Turn it*Ret it coefficients indicates that neither earnings news nor survivorship bias drives the results. To gauge the economic impact of news on volume-induced momentum, consider an increase in turnover from the

10th to the 90th percentile of its distribution conditional on news. This increase in turnover leads to a 3.2% increase in momentum of daily returns on news days, but only a 0.5% increase in momentum of daily returns on non-news days. These percentages correspond to volume-induced momentum magnitudes of 19 bps and 3 bps over days [2,10] for news and non-news days, respectively. Together, the first and second key results imply that the average news story reduces return reversal, and that high-volume news stories reduce return reversal by an even larger amount.

[Insert Figure 2 here.]

To summarize these first two results, Figure 2 shows stylized calculations of the predicted percentage of a stock’s daily return that is reversed in four situations: when news occurs (news it = 0.702) or does not occur (news it = -0.099), and when turnover conditional on news is high (90th percentile) or low (10th percentile). The four sets of bars in Figure 2 represent the predicted return reversal for all four combinations of news and non-news, and high and low turnover. The dark gray, light gray, and black bars show how these reversals change when the sample includes all news, excludes earnings news, and excludes non-earnings news.

Figure 2 provides a simple graphic interpretation of the first two empirical results. The fact that the first two sets of bars in Figure 2 are statistically and economically significantly lower than the second two sets of bars implies that news reduces return reversal on average. The second main result is that the difference between the third and fourth set of bars in Figure 2 is much larger than the difference between the first two sets of bars. This means that volume reduces return reversal, but only when it accompanies news. Equivalently, one could say that public news reduces return reversal by more when it accompanies high volume.

The numerous subsample results in Table 1 and Figure 2 demonstrate that the impacts of news on reversal and volume-induced momentum are both quite robust. For example, columns four and five in Table 1 show that the impact of news on reversals (news it*Ret it coefficient) and volume-induced momentum (news it*Turn it*Ret it coefficient) remains similar regardless of the period. This is

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新闻专题策划书模板

新闻专题策划模板 例1。“海大火灾门”新闻专题策划书 一、选题分析： 1、题材分析：海大学生宿舍发生火灾属于重大突发性事件，报道具有时效性、接近性。对其报道动作要迅速，以最快的速度把现场的情况传达给受众。为受众传达最新最全的消息。 2、角度分析：在报道这次火灾事件时，媒体工作者要客观、全面，不要成为某一方的代言人，其中包括学生、受害者、相关领导干部、官方代表。在报道中，要体现人文关怀。通过对事件的跟踪报道，反思高校学生用电与学校限电的矛盾，捉住事件新动态来突破揭示事件的本质。 3、深度分析：这一类的高校火灾事件并不少见，为什么会屡屡见于报道。背后透漏的是什么是高校管理工作的不善与失败高校学生的安全隐患到底有多大……我们设置本专题的目的不是报道海大的这次火灾本身，希望透过这次报道，引起相关部门的重视。各高校应该引以为鉴，对存在于校园中的各种安全隐患要有一套完整的解决方案。更重要的是要在事件发生之前做好防备工作。

二、内容分析：本专题暂分为6个部分。 最新报道：采用追踪连续报道，以时间为维度将事件的发生、发展过程、当前状态、未来趋向以短消息或通讯形式展示。在首页，每条消息以标题的形式展示。 事件细节：以文本的形式把事件发生的时间、地点、人物、原因、发生了什么等新闻要素显示在专题首页，方便读者对事件的了解。新闻图集：把与此事件有关的具有新闻性的图片以幻灯片的形式展示在专题首页。 视频报道：放置海大媒体对这个事件的视频报道。 类似事件：以高校为维度，将类似的新闻突发事件有选择性的放在专题首页。以图片加核心提示的形式链接下一级的新闻。 高校领导态度：收集一些针对发生火灾这一事件，一些学校领导做出的回应，发表的意见和做出的决策。还可以包括相关教育部门的态度和应对方案。 讨论区：讨论区分两部分，一个是‘评论’专栏，一个是‘灌水区’。‘评论’可以链接到与此事件相关的博客、评论文章等，‘灌水区’是浏览此专题的人留下的言论。讨论区为受众提供的一个发表言论的平台，以意见态度为维度，受众可以对当事人的态度、相关领导部门的意见、社会反映等提出自己的看法、建议等。

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觞开思维 精诚制作

分类 项目名称 项目核心
内容 确定系类报道项目名称； 将本次系列报道核心观点 明确；
备注 明确主题 围绕核心展开报道
数据支持
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旁白撰写
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