Stock Return and Financial Media Coverage Bias1
Shengle Lin
shengle_lin@haas.berkeley.edu
Haas School of Business, University of California, Berkeley
545 Student Service Building, #1900
Berkeley, CA 94720
October 2011
I thank Dow Jones News Service for providing the news data, Paul Tetlock for offering consolidated
media coverage data, Terrance Odean for excellent critiques and Brad Barber for insightful comments.
The author thank International Foundation for Experimental Economics, Searle Foundation Trust and
Interdisciplinary Center for Economic Science for financial support. Correspondences to:
shengle_lin@haas.berkeley.edu
Stock Return and Financial Media Coverage Bias
Abstract: Using news publications from Dow Jones News Services and the Wall Street Journal from 1979
to 2007, I test the relationship between stock returns and changes in media coverage. When stocks are
sorted on past 12-month cumulative abnormal returns, past winners receive higher abnormal coverage in
the current month than past losers. When the return sorting window is shortened to four weeks, stocks in
the both extreme quintiles receive higher abnormal coverage within the four weeks. However, in the
following weeks, abnormal coverage for the top winners remains above average, while that of the bottom
losers drops to levels below average stocks. Media coverage exhibits a long-run bias toward past winners
and a short-run bias toward high contemporaneous absolute returns. I examine three types of potential
intermediating factors: firm’s information supply, analyst coverage and media selection. I find that: (1)
press releases from firms only have weak impact on media coverage, suggesting that firms’ investor
relation activities can hardly control media coverage; (2) past winners have increased numbers of analysts
following the stocks and stories reported on high-analyst-coverage stocks are longer, indicating that
analyst coverage is an important mediating factor; (3) around earnings announcement, the media give
stocks with extreme post announcement return both more stories and higher percentage of stories with
“hot” news label, suggesting that the media prefer to write stories on extraordinary events. Our
investigation suggests that the long-run coverage bias is likely to result from accessibility of firm-specific
information and the short-run coverage bias is likely to draw from the media selection.
Keyword: Media Coverage, Stock Return, Press Release, Earnings, Coverage Bias, Stale News
“It’s always a buying time, never a selling time.” – Gerald Celente, August 2011, criticized the financial
media in persuading investors to buy stocks in all economic conditions.
1. Introduction
Recent research on the relationship between financial news and stock returns suggests that media
coverage can be manipulated by firms or the investor relationship (IR) vendors that are hired by firms.
Gurun (2010) finds that firms, with the help of a media expert on the board of directors, can have their
bad news reported with 18% fewer negative words, and have their good news receive 20% more media
coverage. Similarly, Solomon (2011) finds that IR firms help their clients generate more media coverage
of positive press releases and less of negative releases. Others have documented a local media bias toward
local firms (Gurun and Butler, 2010). These studies suggest that media coverage is influenced by firms
themselves and that firms would like to boost the coverage on their good performance. I refer to this
tendency of firms to promote their news as supply preference.
Fewer studies have addressed any distortion from the media side, that is, whether the media itself would
exhibit a preference for a certain type of news in the absence of conflicting interests noted in Gurun and
Butler (2010). Like any type of media, financial media must decide what news to be covered in order to
better compete for the attention of investors. Hamilton (2004) argues that editors must select content to
appeal to the subscribers in order to maximize subscriptions. In media coverage on celebrities, for
example, negativity bias clearly exists. It reflects that the media caters to the taste of the public rather than
that these negative stories are more readily available. This suggests that the media’s preference overrules
the supply preferences. Solomon and Soltes (2011) show that print newspaper covers more on
contemporaneous extreme earning surprises2. They argue that media caters to the sensationalism sought
by readers.
What kind of stories would financial media like to carry, holding other factors fixed? Sensationalism
cannot be the only objective of financial media. Tetlock (2010a) argues that financial media serves an
information intermediary role in resolving information asymmetry. It is reasonable to assume that readers
do not read the Wall Street Journal or tune into Mad Money just to get shocked. In most likelihood,
subscribers hope to identify investment opportunities from the media.
If the goal of readers is to seek investment information, then the media would have to steer the focus
toward the needs of investors. For example, a key characteristic of investor behavior is chasing past
returns. Barber and Odean (2008) document that retail investors tend to buy stocks with strong past
returns, a pattern that is consistent with representative bias. Grinblatt, Titman and Wermers (1995) find
that 77 percent of mutual funds are “momentum investors,” buying stocks that were past winners. If
average investors chase past winners, they would prefer the media to offer more information on these
winning stocks. The preferences of the media and the subscribers rarely have been studied. I refer to this
preference, if it exists, as demand preference.
This study serves to answer two questions: (1) What is the relationship between stock returns and followup media coverage? Fang and Peress (2010) and Tetlock (2010b) both examine the impact of media
coverage on future stock returns. This paper takes on an opposite perspective and studies how historic
return affects future media coverage. (2) If there is a media coverage slant, where might it originate?
Using comprehensive data on news publications from the Dow Jones News Service and the Wall Street
Journal from 1979 to 2007, I measure media coverage on a stock as the number of stories reported on a
stock in a period (day, week, or month). I examine how media coverage changes in response to stocks’
past and current performance. Change in media coverage is measured as the difference between stories in
2
Solomon and Soltes (2011) find that print newspapers cover more heavily on extreme negative earnings report, but
the tilt toward negativity is not found among newswire.
the current month and the moving average of stories prior to the return sorting window. The difference
also can be seen as abnormal coverage. When sorted on past 12-month cumulative abnormal returns
(CAR), stocks with the highest return receive more abnormal coverage in the current month, and the
coverage sustains at elevated levels in the following months, too; stocks with the lowest return do not
experience any increase in coverage, and their coverage subsequently stays at lower-than-average levels.
The pattern is robust after controlling for firms’ market capitalization, contemporaneous return, analyst
coverage, price-earnings ratio and industry fixed effect. The bias also exists when the historic return sorts
are limited to the past six months or three months.
When I shorten the return sorting window to four weeks, stocks with extreme returns, both positive and
negative, receive the highest abnormal coverage. This suggests that media coverage is higher for both the
best performing stocks and the worst performing stocks in the short-run. The short-run bias toward
extreme returns is consistent with the findings of Solomon and Soltes (2011). However, in the weeks
following the sorting weeks, coverage on high return stocks remains above average, while coverage on
low return stocks drops below average (even though the return differences across the two groups in postsorting weeks are similar). Again, the shift in media coverage indicates a coverage bias toward high past
performance.
To examine where the cross-section media coverage differences might be originated, I can examine three
types of potential factors: firms’ supply of information, analyst coverage and media’ reporting preference.
The first test involves controlling for the number of press releases issued by the firms both in the return
sorting period and in the current month. Dow Jones News Service provides a tag, called “press release,”
on news that is a press release from a firm. The null hypothesis is that if firms with more press releases
can influence their media coverage, stocks with the same amount of press releases should experience the
same amount of coverage. The data show that news coverage bias toward high historic return is robust
after controlling for press release. Past press releases do not have an impact on future media coverage,
though contemporaneous press releases are correlated with abnormal coverage. The impact of press
releases on media coverage is likely to be short-lived and do not contribute to long-run coverage bias.
The second test looks at the influence of analyst coverage. I find that stocks with more analysts following
receive more coverage as well as more detailed stories (longer in length).
The third test investigates a set of earnings announcement events. Following earnings announcement, the
media quickly cover the event, with the majority of reports appearing within hours of announcement. I
find that media gives more earnings-specific reports as well as high percentage of “hot news” labels to
extreme returns.
I provide strong evidences that media coverage is biased and the robust findings on media coverage bias
are important to studies on media effect and attention3.
The rest of the paper is structured as follows: Section 2 presents the data, methodology, and main results;
Section 3 evaluates the relative impact of news supply bias and media demand bias; Section 4 compares
the results with that of other studies; and Section VI concludes.
2. Data, Methodology and Main Results
2.1 Data Source
The news data source used in this study is the same as in Tetlock (2010b), which uses the Dow Jones (DJ)
news archive containing all DJ News Service and the Wall Street Journal (WSJ) stories from 1979 to
2007. DJ breaks each news story into multiple fragment messages that are sent out at the earliest time
when each becomes available. DJ newswire is the most widely circulated financial news in the United
States for institutional investors has the most comprehensive coverage. The DJ firm code identifier at the
3
Researchers have shown that media coverage is directly related to investor attention and trading (Barber and
Odean, 2008; Engelberg and Parsons, 2009).
beginning of each newswire is used to check whether a story mentions a publicly traded US firm4. When
a story mentions multiple ticker codes, I keep only the stories with three ticker codes or fewer. Each news
story is matched to a particular date using a 4 p.m. to 4 p.m. market close-to-close rule. Refer to Tetlock
(2010b) for a more comprehensive view.
I limit the subset of news to stories with at least 30 words5 in length. The analysis here is limited to stocks
with the following criteria:
(1) Traded in NYSE, AMEX or NASDAQ;
(2) With a share code 10 or 11, which refers to common stocks;
(3) Average monthly stock price must be greater than $5 in all active months;
(4) Average monthly market capitalization must be greater than $10 million in all active months;
(5) Average monthly traded shares must be greater than 50,000 in all active months;
(6) Stocks must have at least two years of active trading record during the sample period.
This leaves a sample of 8314 stocks, and a total number of 3,175,921 news stories (excluding 557,013
press releases). The average number of stories per stock per month is 2.65. Table 1 provides the basic
summaries on the news coverage. The number of stocks in the sub-period 2000-2007 decreases slightly
from the previous sub-period 1900-1999, because a lot of stocks were delisted or merged in the burst of
the Internet bubble.
I obtain daily and monthly stock returns, stock price and common shares outstanding from CRSP,
earnings information from Compustat, and analyst coverage from I/B/E/S. The stock’s market
capitalization is computed as the share price times the common shares outstanding at a specific time. P/E
4
Tetlock (2010b) indicate that prior to November 1996, 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.
5
Tetlock (2010b) cuts off at 50 words to allow for textual mining. Since this study does not address the content of
news, the cutoff is set to 30 words instead. ratio is computed as the price at the end of fiscal year-end month divided by the earnings per share
(diluted and excluding extraordinary items) for the fiscal year.
Table 1: News Stories Summary Statistics
The Dow Jones Newswire covers a date range of 1979 to 2007. The table breaks the sample
into 3 periods, 1979-1989, 1990-1999 and 2000-2007. The total number of stories, the
number of stocks covered, average number of stories per stock per month, and the average
market capitalization of covered stocks are reported
1979-1989
176,483
1,713
1990-1999
1,728,487
6,973
2000-2007
2,853,608
6,554
All Years
4,758,578
9,107
0.8×109
1.5×109
3.4×109
2.1×109
# Stories/stock
mean
min
p25
p50
p75
max
103.03
1
24
58
111
2,771
247.88
1
56
117
227
20,007
435.4
1
53
186
415
27,500
312.24
1
49
124
287
27,500
# Stories/stock/month
mean
min
p25
p50
p75
max
2.35
1
1
1
3
109
7.46
1
2
4
7
820
9.13
1
2
4
9
727
7.68
1
2
3
7
820
#Total Stories
# Stocks Covered
Average Market Cap
2.2 Monthly Abnormal News Sorted on Past 12-month Cumulative Abnormal Returns (CAR)
The major question is how the past performance of a stock would affect its future news coverage. The
investigation starts with monthly news coverage.
In each month of 1981-2007 (month 0), I sort stocks into 10 return bins based upon their past 12-month
Cumulative Abnormal Returns (CAR, from month -12 to month -1). Abnormal return is computed as the
excess return over the equally-weighted market return (NASDAQ/NYSE/AMEX combined). Monthly
abnormal returns are compounded to arrive at the 12-month CAR. Bin 1 has the lowest CAR, and bin 10
has the highest CAR. I examine how media coverage changes in response to the past 12-month return
performance and construct an abnormal coverage variable, abnews:
abnewsi ,t  j  newsi ,t  j  average newsi ,[t 24,t 13] , j  12,11, ,11
The control period is months [-24, -13] and is fixed relative to month 0. The return sorting period is
months [-12,-1]. The observation period for subsequent news coverage is months [0,11].
I use the change in coverage and abnormal coverage, instead of levels of coverage, for two reasons: (1)
level of stories is non-stationary and tends to grow over years, thus making it improper for time-series
comparisons; (2) using the historic average as a control can remove much of stock-specific fixed effects,
many of which are unobservable heterogeneities.
I use the moving average of stories in months [-24, -13] as a denominator, instead of using the average in
months [-12,-1], to avoid potential correlation with the sorting variable. This is important for isolating the
impact of CAR in months [-12, -1] because it is not known yet how the 12-month CAR would interact
with the news coverage in the same period. The moving average in months [-24,-13] gives a fixed anchor
that is not influenced by the return in month [-12,-1].
Figure 1 Panel A plots the monthly abnormal news for each of 10 return bins from month -12 to month
11, spanning two years. At month 0, stocks are grouped into 10 bins based upon their past 12-month
CAR. The abnormal news for an individual stock is obtained by subtracting the average news in months
[-24,-13] from the news in the month 0. Next, the average of abnormal news for all stocks in a bin at
month 0 is computed. Finally, I arrive at the time-series average of each of the 10 bins. The time series
starts from 1981, two years after the beginning of available date, and ends at 2007. Each bin time series
covers 324 months.
A potential concern is the survivorship bias. When stocks are included in the sorting period [-12,-1], they
may get delisted in the months [0,11]. Since I sort on returns in the past and examine news coverage in
the future, it is inevitable that I come across stocks that are delisted in the subsequent period but still
receive news coverage. In particular, stocks with extremely low past returns are more likely to be delisted
in the following months. Delisted stocks may no longer receive news coverage, since their ticker codes
will no longer be mentioned in the newswires. This would bias the coverage in the low return bin
downward. To avoid this downward bias, I include the stories in months [0, 11] only when a stock is
actively traded in this period.
Panel A shows the evolution of news coverage in both the sorting period [-12, -1] and the subsequent
period [0, 11]. In Figure 1 Panel A, the short-dash blue line in the middle delineates the average
abnormal news for all stocks. The upward trend shows that the average stock experiences more news
reports over the time (each month has the same denominator). The slope for the average trend is constant,
suggesting that abnormal news is relatively stationary over time. As can been seen, the best
outperforming bin received increased coverage during the sorting period and the coverage sustains at the
highest levels in the subsequent months. The coverage on the worst performing bin experiences slightly
above-average growth in month -11, but the growth ceases afterwards and falls to the bottom after month
-7. The distance in coverage between the worst performing bin and the average stocks widens over time.
In the post-sorting months [0,11], the abnormal coverage across bins exhibits an ordering perfectly
coinciding with the respective 12-month CARs.
Panel B reports the monthly abnormal returns for each bin. In the sorting period [-12, -1], the returns
spread across bins are large (by construct). In the subsequent period [0, 11], the spread is small. Stocks
experiencing high (low) past returns continue to outperform (underperform), but the continuation is
reverted after month 66. This suggests that the past 12-month CARs are more likely to be the main drivers
for the subsequent abnormal coverage difference, rather than the returns in months [0, 11]. It should be
noted that past winners do not have to outperform in every single month. The high abnormal returns in
each month of the sorting period reflect the average of all stocks in the bin.
6
Fang and Peress (2010) argue that current coverage can drive future returns. Our results suggest that past returns
are correlated with current coverage. Given that returns are generally correlated over time horizons, our results
would suggest a careful re-examination of their argument. Future returns could have been affected by past returns,
too.
Figure1. Stock Returns and Abnormal Monthly News: 1981-2007
Panel A:
Panel B: To be more precise, the sorting needs to be controlled for contemporaneous returns. In addition, Solomon
and Soltes (2011) pointed out that large firms tend to receive much higher media coverage. Table 2
investigates the effect of the past 12-month CAR on the current month media coverage whilewith the
controlling for market capitalization and contemporaneous return in the current month.
In Table 2, I test whether past winners receive higher abnormal news in the current month than past losers
do, conditioned on market capitalization and contemporaneous returns. Stocks first are split into five
market cap bins based upon their market cap in lagged 13 months. The reason to use a lagged 13-month
cap is to avoid the interference between present market cap and past 12-month CAR. If I pick the market
cap at the end of month -1, it would be highly correlated with the past 12-month CAR. In this case, the
larger firms will automatically have higher past returns and the effect of past returns will be diluted.
Each size bin is further split into five contemporaneous return sub-bins based upon their abnormal returns
in the current month. In each of the 25 sub-bins, stocks are split into five past return quintiles based upon
their past 12-month CAR. This sorting scheme ensures the number of stocks in each bin are equalized to
the largest extent. Panel A reports on abnormal news in the current month. The results show that for
stocks with the same cap quintile and contemporaneous return quintile, quintile 5 of the 12-month CAR
consistently has higher abnormal news coverage than quintile 1 of the 12-month CAR. For each size-bycontemporaneous-return pair, 324 months of differences are obtained. To test the statistical difference
against the null that the difference is zero, I compute Newey-West standard errors with six lags to arrive
at t-statistics. The t-statistics are significant at 0.01 level in 23 out of 25 comparisons. In cap quintile 1-4,
the difference between the low 12-month CAR quintile and the high 12-month CAR quintile is highly
significant. In cap quintile 5, the adjusted standard errors go up, driving down the t-statistics. Yet, the
difference remains positive.
Panel B is a complementary table reporting the average past 12-month CAR for each of 125 bins.
Smaller firms on average haves higher past CAR. The past winners outperform the past losers by
economically large amounts.
An interesting fact is that the contemporaneous monthly return has a U-shaped effect on news coverage.
The lowest current month return bin does not receive the least amount of abnormal coverage. Instead, in
all of the size quintiles, stocks in the lowest current abnormal return quintile and those in the highest
quintiles have higher abnormal coverage than those in the middle quintiles. This suggests that, in the short
run, the return-coverage relationship is U-shaped. Section 2.3 will verify this specifically with a shortened
return sorting window.
0.182
-0.060
-0.041
-0.014
0.228
0.402
0.009
0.007
0.080
0.446
Low-1
2
3
4
High-5
Low-1
2
3
4
High-5
2
3
4
0.363
0.078
0.075
0.096
0.456
0.142
0.034
0.036
0.076
0.289
0.141
0.035
0.029
0.070
0.256
0.370
0.119
0.104
0.167
0.527
0.214
0.094
0.079
0.137
0.348
0.144
0.077
0.067
0.097
0.256
0.453
0.213
0.162
0.287
0.648
0.278
0.083
0.121
0.178
0.418
0.223
0.085
0.136
0.156
0.328
0.858
0.480
0.448
0.558
1.100
0.618
0.310
0.276
0.303
0.724
0.479
0.176
0.164
0.256
0.555
0.456
0.472
0.441
0.478
0.654
0.436
0.370
0.317
0.317
0.496
0.391
0.155
0.159
0.202
0.353
0.145***
0.078***
0.088***
0.090***
0.154***
0.095***
0.065***
0.046***
0.056***
0.092***
0.070***
0.040***
0.035***
0.036***
0.072***
4
3
2
Low-1
2
3
4
High-5
Low-1
2
3
4
High-5
Low-1
2
3
4
High-5
-0.516
-0.395
-0.369
-0.382
-0.473
-0.567
-0.450
-0.415
-0.426
-0.520
-0.575
-0.459
-0.426
-0.438
-0.535
-0.265
-0.171
-0.151
-0.162
-0.227
-0.316
-0.208
-0.178
-0.186
-0.257
-0.323
-0.215
-0.184
-0.194
-0.262
-0.096
-0.039
-0.030
-0.032
-0.064
-0.126
-0.053
-0.035
-0.033
-0.068
-0.119
-0.051
-0.033
-0.029
-0.052
0.093
0.106
0.100
0.107
0.120
0.112
0.121
0.122
0.138
0.156
0.144
0.144
0.142
0.160
0.204
0.582
0.480
0.450
0.479
0.604
0.739
0.599
0.555
0.617
0.762
0.937
0.719
0.656
0.737
0.956
High-5
1.515
1.111
0.974
1.097
1.498
1.098
0.875
0.820
0.860
1.077
1.306
1.049
0.970
1.043
1.282
1.512
1.178
1.082
1.175
1.490
Q5-Q1
2.078
1.558
1.399
1.539
2.034
Low-1
1.707
1.684
1.491
1.305
2.533
0.826
0.529
Low-1
-0.412
-0.201
-0.069
0.075
0.434
0.846
2
0.817
0.410
0.680
1.089
1.638
0.821
0.463*
2
-0.320
-0.136
-0.031
0.080
0.356
0.676
Large-5 3
Large-5
0.710
0.496
0.579
0.930
1.529
0.818
0.478*
3
-0.304
-0.128
-0.028
0.083
0.344
0.648
4
0.766
0.651
0.651
1.082
2.091
1.326
0.519**
4
-0.311
-0.133
-0.029
0.083
0.357
0.669
High-5
1.552
1.354
1.610
1.667
3.303
1.751
0.605***
High-5
-0.389
-0.181
-0.054
0.087
0.448
0.837
In each month from 1981 to 2007, (1) stocks are first sorted into 5 size bins based upon their market capitalization 13 months ago, right before the sorting period of lagged 12 months; (2) in each size bin,
the stocks are further split into 5 bins based upon their CAR in the observation month; (3) in each of 5 by 5 bins, stocks are split into 5 quintiles based upon their past 12-month CAR (month -12 to -1). In
each month, stocks are split into 125 bins. For each stock in a bin, obtain the abnormal news, by subtracting the average monthly stories for a stock between month -24 and -13, from the monthly stories
for the stock in a observation month. For each bin-month pair, the mean across the included stocks is computed. Thus, a time series (324 months) of means is obtained for each bin. Differences are
computed for the highest 12-month CAR quintal and the lowest 12-month CAR quintile, arriving at a time series of 324 months differences for each bin . t-statistics are provided for the differences using
Newey-West standard errors with 6 lags. The abnormal coverage is reported in Panel A. Panel B reports the average12-month CAR for each of 125 bins.
*** 0.01 **0.05 * 0.10
0.088
0.021
0.004
0.054
0.202
Low-1
2
3
4
High-5
Table 2: Abnormal News sorted on Past 12-Month CAR, Controlling for Market CAP and Contemporaneous Return: 1981-2007
Panel A: Abnormal News
Panel B: Past 12-Month CAR
Market
AR in
Quintile on past 12-Month CAR
N.W.
Market
AR in
Quintile on past 12-Month CAR
Cap
Month 0 Low-1
Cap
Month 0 Low-1
2
3
4
High-5
Q5-Q1 Std. Err.
2
3
4
Low-1
0.073
0.059
0.080
0.119
0.292
0.219
0.040***
Low-1
-0.563
-0.284
-0.051
0.280
2
-0.026
0.013
0.019
0.073
0.136
0.162
0.033***
2
-0.447
-0.194
-0.013
0.219
small-1
small-1
3
0.008
0.024
0.043
0.075
0.146
0.138
0.027***
3
-0.426
-0.172
-0.005
0.200
4
0.022
0.033
0.077
0.110
0.196
0.174
0.035***
4
-0.442
-0.181
-0.001
0.231
High-5
0.184
0.213
0.234
0.254
0.456
0.272
0.045***
High-5
-0.537
-0.239
0.000
0.322
2.3 Weekly Abnormal News sorted on 4-week CAR
Section 2.2 examines the effect of 12-month CAR on subsequent coverage. Table 2 suggests that past 12month CAR have a linear impact on current month coverage, but the contemporaneous return seems to
have a U-shaped impact on current month coverage. It seems that the short-run return effect would be
different from the long-run return effect. To examine the contemporaneous return effect closely, I now
shorten the return sorting period into about a month. To capture more details within a month, I focus on
news coverage by week.
In Figure 2, I examine media coverage response to short-run return shocks. At each week, i.e. week 0,
stocks are sorted into 10 bins based upon the past four-week CAR. The weekly abnormal news is reported
for weeks [-4, 11]. The weekly abnormal news is defined as weekly news minus the 52-week moving
average prior to the sorting period:
weekly abnewsi ,t  j  weekly newsi ,t  j  average weekly newsi ,[t 56,t 5] , j  4,3,,11
The control period is weeks [-56, -5] and is fixed relative to week 0. The return sorting period is weeks [4,-1]. The observation period for subsequent news coverage is weeks [0,11].
Figure 2 Panel A plots the weekly abnormal news from week -4 to week 11 for each bin. Panel B plots the
weekly abnormal returns for each bin. In Panel A, the blue short-dash line delineates the abnormal news
coverage for all stocks. The slope of line is constant, suggesting news growth is relatively stationary. In
the four-week sorting window, the ordering from high coverage to low coverage is bin 10 > bin 1> bin 9
>average>bin 2. The 9th and 10th high return bins and the 1st low return bin have the highest coverage
from week -4 to week -1. Bin 1 has lower coverage than bin 10, but it is above bin 9. The regularity in the
contemporaneous window is that higher return receives higher coverage, but extremely low return shocks
also receive plenty of coverage. This confirms that the contemporaneous return-coverage relationship is
U-shaped. It is consistent with the findings of Solomon and Soltes (2011), who report that surprises (as
indicated by the level of subsequent returns) drive media coverage.
In Panel A, from week 0 and on, coverage drops across the board. However, coverage for the 10th bin
remains higher than all other bins, and coverage for 1st bin drops the fastest to the bottom. Panel B draws
the average weekly abnormal returns for each bin over the 16 weeks. The return spread in the sorting
window is large (by construct), but the spread in subsequent weeks is hardly differentiable. When
combining the return information in Panel B, the coverage difference across bins from week 1 and on is
not accompanied by much difference in stock returns. The media continue to cover outperforming stocks
more intensively than underperforming stocks, even though the returns in the subsequent weeks are about
the same.
Figure 2 suggests that contemporaneously media pay extra attention to absolute return shocks but
continue to give above-average attention only to past winners. As stocks returns are usually driven by
events, the similar returns in subsequent weeks across bins suggest that each bin is equally “eventful.” It
seems that past winners get repetitive coverage on their prior events while past losers do not. I thus
hypothesize that the shock effect of absolute returns is limited; once the surprise is over, the media repeat
stories only on the past winners. In Section 2.4, I will address this in more detail.
Figure 2: Weekly Abnormal News Sorted on 4-week CAR
Panel A:
Panel B :
2.4 Panel Regression Analysis
In this section, I run panel regressions to examine the effect of long-run historic returns on abnormal
stories. The sample covers 8314 cross-sections and 348 months. I start the analysis from 1981, leaving a
remaining time length of 324 months. The dependent variable is the monthly abnormal news (abnews)
defined above. The main independent variable is past return. I examine past 12-month (car_12m), 6month (car_6m) and 3-month (car_3m) cumulative abnormal returns. The first control variable is absolute
contemporaneous abnormal return, |ar|. Given that the contemporaneous returns have a U-shaped impact
on abnormal news, it is more appropriate to use absolute values. Other control variables include:

l_log_cap, logarithm of market cap at the end of month -13. As discussed previously, I use lagged
market cap to avoid correlation with the historic return.

l_PE, the most recent price earnings ratio (P/E) before month -12. Quarterly Earnings
information is often available on Compustat.

l_log_(1+Analysts), logarithm of 1 plus the average monthly analysts following a stock between
month -24 and -13,

industry, Fama-French (1974) SIC code;

year, dummy variable controlling annual fixed effect.
The control variables are measured prior to the sorting 12-month period to avoid potential correlation
with the main explanatory variable – historic returns. PE ratio and analyst coverage are used as additional
controls.
The panel regressions use Newey-West standard errors with six lags to adjust for time series
autocorrelations.
Summary statistics are provided in Table 3A, and regression results and test statistics are provided in
Table 3B. Table 3A shows that the past 12-month, 6-month and 3-month CAR all have a positive and
significant effect on abnormal coverage in the current month. The absolute current return and market cap
also have a significant positive effect. This is consistent with Solomon and Soltes (2011).
PE ratio has a negative effect. Generally growth stocks would have higher valuations. This suggests that
growth stocks tend to receive less coverage growth over time on average. One possible explanation is that
high P/E stocks perform relatively poorer than other stocks, but the poorer performance drives down the
subsequent coverage.
Analyst coverage has a significant positive effect. The top industries that experiences higher abnormal
coverage are telecommunication, automobiles and trucks, tobacco and hardware computer.
Overall, it is confirmed that past winners receive more abnormal coverage in the current month than past
losers.
Table 3A: Summary Statistics for Regression Analysis
abnews is abnormal monthly stories, which equal to current month stories minus the average monthly
stories between month -24 and -13. car_12m is the cumulative abnormal return over the past 12 months;
car_6m is CAR over the past 6 months; car_3m is CAR over the past 3 months; |ar| is the current
month's abnormal return. l_log_cap is the logarithm of market capitalization in month -13. year is a
calendar year dummy. industry is dummy variable for the 49 Fama-French SIC codes.
Variable
N
mean
min
p10
p50
p90
max
abnews
car_12m
car_6m
car_3m
|ar|
l_log_CAP
year
industry
1087164
1117156
1157168
1177136
1197056
1106017
1197060
1180321
0.51
0.04
0.02
0.01
0.10
12.30
-
-363.08
-1.00
-1.00
-0.99
0.00
5.75
1979
1
-1.75
-0.46
-0.34
-0.24
0.01
10.00
-
0
-0.05
-0.02
-0.01
0.06
12.14
-
3
0.54
0.36
0.25
0.21
14.81
-
Table 3B: Panel Regression on Factors Determining
Change in Monthly Stories
The abnormal news, abnews, is regressed on historic returns. In
(1), (4)-(6), it is regressed on past 12-month CAR; in (2) it is
regressed on past 6-month CAR; in (3), it is regressed on past
3-month CAR. The regressions are controlled for absolute
current return, market capitalization, PE ratio, analyst
coverage, yearly fixed effect and industry fixed effect. The
panel regression adopts Newey-West adjusted standard errors
with 6 lags. *** 0.01 **0.05 * 0.10
car_12m
(1)
0.67***
(0.052)
(2)
(3)
-
-
car_6m
-
0.70***
(0.053)
-
car_3m
-
-
0.66***
(0.051)
|ar|
2.52***
(0.095)
2.51***
(0.096)
2.49***
(0.095)
l_log_CAP
0.38***
(0.027)
0.37***
(0.026)
0.36***
(0.026)
Year Fixed Effect
Yes
Yes
Yes
Industry Fixed Effect
Yes
Yes
Yes
Observation
1057508
1057508
1057508
F statistics
74.27
74.4
74.31
Date Range
1981-2007
1981-2007
1981-2007
608.83
47.34
26.69
26.43
10.22
20.22
2007
49
3. Supply Preference and Demand Preference
Having established that media have a short-run coverage bias toward extreme returns and a long-run
coverage bias toward high positive returns, I now examine whether the long bias is introduced by firms’
supply bias. Guru (2010) shows that a firm, through the help of a media expert on the board, can boost the
coverage on their good news by 20%. Solomon (2001) demonstrates that firms can “spin” their press
releases through an IR firms, generating more media coverage of positive press releases and less of
negative press releases. Those firms that are actively involved in publicity manipulation should have more
press releases as well. Therefore, I use levels of press release as a proxy for supply preferences. If the
above document long-run coverage bias ceases after controlling for the level of press releases, it would
suggest that the observed media coverage bias is introduced by supply preferences solely.
In addition, Solomon (2011) points out that earnings news is more difficult to manipulate. Therefore, I
look at a subset of news that solely addresses earnings announcements. If the coverage biases do not exist
among earnings news, it would suggest that the biases are susceptible to the influence of supply factors.
3.0 Size
3.1 Controlling for Press Release
The tests here include press releases as controls. Press releases originate from firms and their frequency
represents a willingness to share firm-specific information.
In Table 5, abnormal news coverage is conditioned on the number of press releases from a firm in the
current month and in the past 12 months. In Panel A, stocks are first split into five bins based on their
market capitalization at the end of month -13. Then each bin is further split into five bins based on the
total number of press releases in the past 12 months. Finally, each of the 25 bins are sorted into five
quintiles based on their past 12-month CAR. Panel B is similar to Panel A, except the conditioning is set
on the press release in the current month. The date range is from 2000 to 2007. Dow Jones News Service
only includes press releases after 1999. I start the analysis on 2001, and this leaves a time-series length of
84 months.
Panel A and Panel B both shows, even when controlling for press releases in the past 12 month and in the
current month, the effects of the past 12-month CAR on current abnormal coverage are not changed. The
differences between the low past return quintile and the high past return quintile remain highly
statistically significant. The standard errors are estimated with Newey-West methods with four lags. This
suggests that the long-run media coverage bias is robust.
To investigate the effect of press releases alone, Table 6 checks the effects of past 12-month press
releases and current press releases on current abnormal news. In Table 6, stocks first are sorted into five
cap bins based on market capitalization in month -13. Each cap bin is split into five past 12-month CAR
bins. After conditioning on the market cap and the past 12-month CAR, the effects of past 12-month press
releases and current press releases are examined in Panel A and Panel B specifically. Panel A indicates
that past press releases have little impact on current abnormal news, while Panel B indicates that the
effect of current month press releases is strong. This indicates that press releases have only a
contemporaneous correlation with current abnormal coverage. This does not necessarily imply that press
releases generate current news coverage. Following any events from a firm, its press release and it
experiences increased mediacurrent news coverage, but are likely to increase temporarily at the two are
not necessarily in a causal relationship. Any effect of press releases on media coverage is likely to be
contemporaneous, that is, past press releases are unlikely to influence future media coverage. This greatly
reduces the chance that press releases are the major driver behind the long-run coverage bias. Solomon
and Soltes (2011) find that the largest determinants of media coverage likely are outside managerial
control. Our results support this argument.
-0.139
0.002
-0.088
-0.224
-0.258
-0.024
0.140
-0.043
-0.073
0.602
Few-1
2
3
4
Many-5
Few-1
2
3
4
Many-5
2
3
4
0.034
0.106
0.029
0.193
0.247
0.109
-0.026
-0.049
0.083
-0.078
-0.008
0.043
0.059
-0.003
0.000
0.080
0.004
0.112
0.153
0.145
0.118
0.172
0.143
0.117
0.279
0.069
0.107
0.094
0.027
0.100
0.249
0.095
0.244
0.373
0.663
0.141
0.182
0.213
0.210
0.259
0.087
0.197
0.261
0.111
0.179
0.523
0.557
0.853
0.951
1.454
0.286
0.261
0.472
0.591
0.840
0.283
0.335
0.414
0.517
0.581
0.546
0.417
0.896
1.025
0.851
0.424
0.259
0.560
0.815
1.099
0.321
0.338
0.450
0.555
0.624
0.113***
0.139***
0.144***
0.220***
0.547
0.107***
0.117**
0.104***
0.164***
0.211***
0.064***
0.088***
0.067***
0.110***
0.133***
4
3
2
Few-1
2
3
4
Many-5
Few-1
2
3
4
Many-5
Few-1
2
3
4
Many-5
-0.467
-0.299
0.089
0.526
2.277
-0.069
-0.066
0.028
0.351
1.044
0.187
0.144
0.307
0.352
0.923
-0.083
-0.096
0.340
0.707
1.947
0.212
0.085
0.366
0.509
1.022
0.426
0.220
0.327
0.539
0.947
-0.032
-0.086
0.302
0.523
1.593
0.380
0.258
0.356
0.711
1.284
0.447
0.330
0.511
0.607
1.062
0.050
0.177
0.386
0.926
2.238
0.328
0.286
0.489
0.826
1.553
0.568
0.377
0.520
0.698
1.101
0.181
0.546
0.909
1.356
3.089
0.432
0.497
0.672
1.026
1.891
0.693
0.549
0.691
0.983
1.488
0.648
0.844
0.820
0.830
0.812
0.501
0.564
0.644
0.675
0.847
0.506
0.405
0.383
0.631
0.565
Panel B: Abnormal News conditioned on current month # Press Release
PR in
Quintile on past 12-month CAR
Month 0 Low-1
2
3
4
High-5 Q5-Q1
Few-1
0.599
0.633
0.647
0.635
0.632
0.033
2
0.423
0.409
0.405
0.479
0.507
0.085
3
0.422
0.437
0.524
0.503
0.594
0.173
4
0.545
0.563
0.564
0.506
0.756
0.211
Many-5
0.925
0.752
0.854
0.958
1.372
0.447
0.160***
0.168***
0.123***
0.220***
0.565***
0.152***
0.117***
0.192***
0.160***
0.248***
0.123***
0.090***
0.145***
0.123***
0.133***
N.W.
Std. Err.
0.045
0.056
0.054***
0.049***
0.087***
Few-1
-0.468
-0.419
-0.205
-0.040
0.571
1.039 0.251***
Few-1
-2.854
-3.388
-2.991
-2.259
-1.245
1.608 0.311***
2
0.565
-0.191
0.027
0.153
0.844
0.280 0.365
2
-2.128
-2.206
-1.806
-1.593
-0.488
1.640 0.508***
Large-5 3
Large-5 3
0.482
0.019
-0.252
-0.422
1.179
0.697 0.478***
-0.792
-1.467
-1.197
-0.058
1.029
1.821 0.551***
4
0.798
-0.233
0.038
1.081
3.077
2.279 0.791***
4
0.218
0.186
0.608
1.558
3.302
3.085 0.698***
Many-5
5.487
1.811
-0.131
-2.001
3.165
-2.322 2.134
Many-5
10.112
6.286
5.236
3.915
7.250
-2.861
2.076
In each month from 2000 to 2007, in Panel A, (1) stocks are first sorted into 5 size bins based upon their market capitalization 13 months ago, before the sorting period of lagged 1 year; (2) in each size bin, the
stocks are further split into 5 bins based upon their total numbers of press releases in the past 12 months; (3) in each of 5 by 5 bins, stocks are split into 5 quintiles based upon their past 12-month CAR. Thus, in
each month, stocks are split into 125 bins. Panel B is similar to Panel A, except that stocks are sorted on the number of press releases in the current month in step (2). For each bin, obtain abnormal news, by
subtracting the average monthly stories for a stock between month -24 and -13, from the monthly stories for the stock in the current month. For each bin-month pair, the mean across the included stocks is
computed. Thus, a time series (84 months) of means is obtained for each bin-month combination. Differences are computed for the highest 1- year CAR quintal and the lowest 1-year CAR quintile. Standard
errors for the differences are adjusted by Newey-West method with 4 lags. t-statistics are provided for the differences.
*** 0.01 **0.05 * 0.10
-0.038
-0.003
-0.036
-0.038
-0.044
Few-1
2
3
4
Many-5
Table 5: Abnormal News sorted on Past 12-month CAR, Controlling for Press Release: 2001-2007
Panel A: Abnormal News conditioned on past 12-month # Press Release
Market
Quintile on past 12-month CAR
N.W.
Market
Cap
PR 12m Low-1
Cap
2
3
4
High-5 Q5-Q1 Std. Err.
Few-1
0.019
0.013
0.050
0.053
0.151
0.132 0.037***
2
0.058
0.067
0.120
0.120
0.102
0.044 0.055
small-1
small-1
3
0.075
0.022
0.026
0.132
0.194
0.119 0.055**
4
0.056
0.044
-0.004
0.091
0.308
0.252 0.045***
Many-5
0.122
0.019
0.054
0.149
0.572
0.450 0.085***
‐0.067
‐0.026
0.185
0.141
0.396
0.049
0.095
‐0.044
0.211
0.641
‐0.096
0.064
0.082
0.200
0.281
0.077
0.001
0.025
0.254
0.595
Low-1
2
3
4
High-5
Low-1
2
3
4
High-5
2
3
4
‐0.018
‐0.014
0.051
0.210
0.940
‐0.210
‐0.075
0.101
0.199
0.485
‐0.058
0.017
0.106
0.176
0.391
‐0.122
0.147
0.214
0.254
0.836
‐0.223
0.003
0.184
0.209
0.579
‐0.078
0.027
0.047
0.209
0.507
0.752
0.066
0.318
0.716
1.407
‐0.195
0.120
0.143
0.362
0.793
‐0.040
‐0.020
0.043
0.274
0.453
0.675
0.065
0.293
0.462
0.812
-0.100
0.057
0.062
0.162
0.512
0.004
-0.068
0.026
0.117
0.168
0.736
0.323
0.331
0.284
0.450
0.214
0.178
0.141
0.177
0.219**
0.148
0.119
0.104
0.118
0.143
4
3
2
Low-1
2
3
4
High-5
Low-1
2
3
4
High-5
Low-1
2
3
4
High-5
‐0.456
‐0.072
‐0.074
0.135
0.295
0.044
0.252
0.349
0.409
0.514
0.191
0.468
0.550
0.614
0.599
‐0.238
‐0.196
‐0.066
0.134
0.604
‐0.186
0.045
0.282
0.277
0.437
0.083
0.217
0.216
0.420
0.524
0.110
0.326
0.261
0.462
0.948
0.006
0.244
0.470
0.431
0.769
0.320
0.356
0.366
0.549
0.764
0.469
0.479
0.606
0.748
1.280
0.400
0.572
0.605
0.724
1.068
0.436
0.529
0.669
0.657
0.974
2.611
1.647
1.656
2.054
3.098
1.008
1.087
1.209
1.394
1.912
1.008
0.839
0.832
1.191
1.446
3.067
1.720
1.730
1.920
2.803
0.964
0.834
0.859
0.984
1.398
0.817
0.370
0.281
0.578
0.847
0.780***
0.376***
0.346***
0.353***
0.596***
0.310***
0.270***
0.239***
0.278***
0.331***
0.214***
0.123**
3.32***
0.220***
0.216***
N.W.
Std. Err.
0.67*
0.148
0.115
0.140
0.214***
‐0.235
0.525
0.147
0.107
6.180 6.415 5.172
‐2.727
‐1.960
‐0.644
1.272
10.764 13.490 4.238***
Low-1
Low-1
‐0.367
‐0.216
0.050
‐0.626
0.980 1.347 6.097
‐3.421
‐2.334
‐1.444
0.013
6.957 10.377 4.170**
2
2
Large-5 3
Large-5 3
‐0.352
‐0.265
0.013
0.201
‐1.224 -0.872 4.480
‐2.889
‐2.120
‐1.006
0.292
3.950 6.839 3.459*
‐0.097
0.080
0.046
0.664
0.226 0.324 3.682
‐2.460
‐1.502
‐0.171
0.803
4.131 6.591 2.838**
4
4
1.058
2.669
7.320 8.440 2.452***
0.735
1.122
0.981
2.977
3.010 2.274 2.907
‐1.120
‐0.490
High-5
High-5
In Panel A, in each month from 2001 to 2007, (1) stocks are sorted into 5 size bins based upon their market capitalization 13 months ago, before the sorting period of lagged 1 year; (2) in each size bin, stocks
are split into 5 quintiles based upon their 1-year lagged CAR; (3) in each of 5 by 5 bins, the stocks are further split into 5 bins based upon their total numbers of press releases in the past 12 months. In Panel
B, the sorting steps are the the same in (1) and (2). In step (3), the stocks are further split into 5 bins based upon their numbers of press releases in the current month. In each month, stocks are split into 125
bins in both panels. For each bin, obtain the abnormal, by subtracting the average monthly stories for a stock between month -24 and -13, from the monthly stories for the stock in the current month. For each
bin-month pair, the mean across the included stocks is computed. Thus, a time series (84 months) of means is obtained for each bin-month combination. Differences are computed for the highest press
release quintile and the lowest press release quintile. t-statistics using Newey-West adjusted standard errors (4 lags) are provided for the differences.
*** 0.01 **0.05 * 0.10
0.017
0.025
0.069
0.271
0.414
‐0.044
0.047
0.017
0.157
0.284
Low-1
2
3
4
High-5
Table 6: Change in Number of Stories reported sorted on Press Releases in part 12 Months and in Current Month: 2001-2007
Panel A: Abnormal News conditioned on past 12-month # Press Release
Panel B: Abnormal News conditioned on current month # Press Release
Market 12-month
Quintile on past 12-Month #PR
N.W.
Market 12-month
Quintile on current month #PR
Few-1
2
3
4
Many-5 Q5-Q1 Std. Err.
Few-1
2
3
4
Many-5 Q5-Q1
Cap
CAR
Cap
CAR
0.043
0.073
0.040
0.092
0.075 0.033 0.102
0.623
0.391
0.498
0.560
0.929 0.306
Low-1
Low-1
0.014
0.092
0.017
0.023
‐0.010 -0.023 0.061
0.622
0.337
0.457
0.504
0.749 0.128
2
2
small-1 3
small-1 3
0.047
0.138
0.072
0.019
0.051 0.004 0.059
0.630
0.523
0.408
0.640
0.743 0.113
0.055
0.102
0.096
0.076
0.117 0.061 0.074
0.692
0.448
0.442
0.565
0.893 0.200
4
4
0.176
0.117
0.268
0.292
0.529 0.353 0.113***
0.661
0.551
0.623
0.674
1.362 0.701
High-5
High-5
3.2 Earnings News
Solomon (2011) argues that earnings information is “hard information,” which is less likely to be
influenced by firms’ IR activities. If the long term bias were introduced by a firm’s IR activities, the bias
should diminish or disappear in earnings news coverage.
DJNS provides two tags on news. One tag is “press release,” which indicates that a story is issued by a
firm itself. The other tag is “earnings,” which indicates that a story falls into the category of earning news.
Using these two tags, I can locate the firms’ press releases on earnings. A subset of 89,535 earnings press
releases is identified. The available date range is from 2000 to 2007. The earnings releases generally are
distributed in January, April, July, and October. For most firms, the releases are issued usually once a
quarter or less frequent.
Following each earnings press release, from day 0 to day 6 I count the stories that have the earnings tag
but are not press releases. The observation window is set to the week from the day of an earnings
announcement to six days from the announcement date. The media’s coverage on earnings in this time
frame is likely to be reports on the most recent earnings announcement. Follow-up earnings likely result
in media “mentions” of the recent earnings event.
In Table 7, the stocks are first conditioned on market cap in month -13 prior to the 12-month sorting
period. In Panel A, stocks in each size bin are then conditioned on a seven-day CAR following the
announcement and, finally, each of 25 bins is split into another five bins based on the past 12-month
CAR. An 84-month time series of differences between the high past return quintile and the low past
return quintile is generated for the 25 bins. I use bootstrapping methods to obtain p-values for whether the
differences are significantly different from zero. Panel A allows us to examine if the long-run bias exists
within the earnings follow-up coverage. That is, do the media give more attention to earnings releases
from stocks that performed well in the past? The answer is yes. In 23 out of 25 conditioning bins, high
past 12-month CAR quintiles receive more earnings news coverage than low past 12-month CAR
quintiles. The bootstrapping statistics are significant for most situations. While the significance drops
slightly relative to Table 2, this is not surprising because stock-day pairs in quintile 5 may not be in the
same time as quintile 1. As a result, the time-series upward trend in news coverage increases the standard
deviations of the differences. Overall, Table 7 Panel A supports Table 2 at a different level: the
documented long-run bias is unlikely introduced by firms themselves.
Panel B examines the effect of post-earnings drift on earnings news coverage, conditioning on market cap
and past 12-month CAR. The top post 1-week CAR quintiles likely are earnings announcements above
expectation and the bottom quintiles likely are announcements below expectation. Panel B shows how the
media covers events in the short run. As can be seen, the top quintiles and the bottom quintiles receive
about the same “mentions” on their earnings releases. That is, bad earnings announcements and good
earnings announcements receive about the same number of media follow-up reports. However, both
extreme quintiles receive more “mentions” than the middle quintiles. Thus, media coverage is biased
toward “surprises” in the short run. This is very consistent with Solomon and Soltes (2011). In their
study, the press (paper) coverage is more intensive on extreme daily contemporaneous returns7.
7
Solomon and Soltes (2011) find that paper press also tends to emphasize bad news. Our results only have slight
support for this and do not hold much statistical significance.
High-5
4
3
2
Low-1
High-5
4
3
2
Low-1
High-5
4
3
1.018
0.784
0.718
0.749
0.947
1.178
0.942
0.855
0.926
1.217
1.548
1.240
1.152
1.263
1.560
0.951
0.796
0.749
0.753
0.904
1.078
0.957
0.895
0.945
1.133
1.434
1.254
1.097
1.313
1.391
1.560
1.253
1.123
1.329
1.479
1.226
0.988
0.870
1.013
1.198
1.027
0.805
0.708
0.755
0.990
1.629
1.232
1.254
1.273
1.544
1.257
1.038
0.920
0.990
1.263
1.010
0.824
0.790
0.808
0.981
1.773
1.453
1.245
1.459
1.718
1.406
1.078
1.006
1.062
1.297
1.066
0.820
0.822
0.881
1.041
0.339***
0.199***
0.147**
0.146**
0.327***
0.328***
0.121**
0.111**
0.117**
0.164***
0.115***
0.024
0.073*
0.127***
0.137***
0.089**
-0.01
0.109***
-0.049
0.034
Q5-Q1
Market
Cap
4
3
2
small-1
Release, 2001-2007
High-5
4
3
2
Low-1
High-5
4
3
2
Low-1
High-5
4
3
2
Low-1
High-5
4
3
2
Low-1
past 12month
CAR
1.451
1.578
1.555
1.609
1.770
1.126
1.192
1.201
1.244
1.413
0.990
1.003
0.981
0.988
1.081
0.856
0.813
0.799
0.801
0.943
1.305
1.167
1.233
1.219
1.440
0.965
0.959
0.969
0.996
1.090
0.830
0.765
0.811
0.817
0.866
0.756
0.672
0.626
0.693
0.744
1.126
1.196
1.131
1.242
1.270
0.902
0.859
0.877
0.914
1.010
0.760
0.719
0.674
0.801
0.829
0.618
0.583
0.594
0.626
0.712
1.310
1.286
1.242
1.275
1.471
0.975
0.907
0.956
1.019
1.068
0.774
0.709
0.752
0.804
0.891
0.718
0.650
0.660
0.676
0.696
1.437
1.541
1.490
1.509
1.727
1.139
1.214
1.210
1.232
1.304
0.920
0.940
0.954
0.973
1.066
0.836
0.787
0.767
0.807
0.897
-0.014
-0.036
-0.066
-0.100
-0.042
0.013
0.022
0.009
-0.012
-0.108*
-0.069*
-0.063*
-0.027
-0.015
-0.015
-0.020
-0.026
-0.032
0.006
-0.046
0.325***
0.381***
0.424***
0.367***
0.499***
0.223***
0.333***
0.324***
0.330***
0.402***
0.230***
0.284***
0.307***
0.188***
0.253***
0.238***
0.230***
0.206***
0.174***
0.231***
Panel B: Media Follow-ups conditioned on post 1-week CAR
Quintile on Post 1-week CAR
Low-1
2
3
4
High-5 Q5-Q1
Q1-Q3
0.311***
0.345***
0.358***
0.267***
0.457***
0.236***
0.355***
0.332***
0.318***
0.294***
0.161***
0.220***
0.281***
0.173***
0.238***
0.218***
0.204***
0.174***
0.181***
0.185***
Q5-Q3
2
Low-1
Low-1
2.281
2.871
2.996
3.051
2.666 0.385***
2.320
2.490
2.572
2.647
2.564 0.244*
-0.252* -0.008***
2
2.425
2.809
2.831
2.797
2.528 0.103
2.891
2.741
2.392
2.800
2.742 -0.149
0.499*** 0.350***
Large-5 3
Large-5 3
2.508
2.436
2.609
2.446
2.374 -0.135
2.966
2.858
2.557
2.766
3.004 0.038
0.409*** 0.448***
4
4
2.707
2.783
2.840
2.861
2.770 0.063
2.992
2.838
2.483
2.850
3.084 0.092
0.508*** 0.601***
High-5
High-5
2.506
2.851
2.908
3.047
2.950 0.444***
2.633
2.602
2.346
2.755
2.962 0.329** 0.288** 0.616***
From 2000 to 2007, a subset of stock-day pairs with firm releasing earning news are obtained. Dow Jone News Services provides tags on firms' press release and earnings news. In Panel A, among the
selcted stock-days pairs, (1) stocks are sorted into 5 size bins based upon their market capitalization 13 months ago; (2) in each size bin, the stocks are further split into 5 bins based upon 1 week CAR from
day 0 to day 6; (3) in each of 5 by 5 bins, stocks are split into 5 quintiles based upon their past 12-month CAR. Thus, all the selected stock-day pairs are split into 125 bins. The stories with earnings tag are
reported and these stories are likely to repeat the stock's recent earning release. In Panel B, the sorting steps (2) and (3) are swaped and this reveals the conditional impact of post annoucement CAR. In
Panel A, Differences are computed for high-5 12-month CAR quintile and low-1 12-month CAR quintile. In Panel B, the main sorting variable is post annoucement drift. The difference between Q5 and Q1,
between Q1 and Q3, and between Q5 and Q3 are computed. The time series span 84 months. Bootstrapping test statistics are used to check the significance of the diferences.
*** 0.01 **0.05 * 0.10
4
3
2
2
Low-1
Table 7: Number of Earnings News following Firm's Earnings Press
Panel A: Media Follow-ups conditioned on past 12-month CAR
Post 1Quintile on past 12-month CAR
Low-1
2
3
4
High-5
Market
week
Cap
CAR
Low-1
0.854
0.813
0.831
0.791
0.943
2
0.729
0.729
0.624
0.699
0.718
small-1 3
0.595
0.600
0.590
0.613
0.704
4
0.709
0.658
0.653
0.672
0.660
High-5
0.830
0.807
0.799
0.845
0.864
4. Discussion
I find a long-run media coverage bias toward high returns and a short-run bias toward extreme returns.
The long-run bias occurs only for stale news, probably because the media revisits only past winners. I
show that the biases are robust against the influence of supply preferences, indicating that the main driver
comes from the demand side.
4.1 Reconciliation with other studies
Our results suggest that the media coverage can be affected by both supply bias and demand bias, but the
supply factors have only a short-lived impact. I show that contemporaneous press releases are correlated
with abnormal news coverage. This is consistent with Solomon (2011), Gurun (2010), and Gurun and
Bulter (2010). Yet past press releases have little impact on contemporaneous abnormal news coverage.
The long-run coverage bias toward high historic returns likely is driven by demand factors.
Our study examines returns in much longer time horizons. Other studies have looked only at returns for
very short windows, most at daily frequencies. I extend the return sorting window and identify the longrun coverage bias.
Our short-run bias findings agree with other studies. I show that in event weeks, the media pays higher
attention to extreme returns. This is consistent with the sensationalism argument of Solomon and Soltes
(2011). Following event weeks, the media revisits outperforming stocks at higher than average levels and
revisit underperforming stocks at lower than average levels. This suggests that media attention on
underperformance is short-lived. It can be reasonably assumed that extreme loss might shock investors
initially, but the effect of sensationalism drops fast with repetition.
These results open the question of why the media chooses to revisit outperforming stocks.
4.2 Possible explanations
Why is the media more inclined to report on past winners? I have shown above that this phenomenon is
not driven by supply bias—that is by firm press releases, though it is affected by contemporaneous
supply. I conjecture that it is, instead, driven mostly by investors' preferences for information relevant to
their investment decisions.
Below I extend a few conjectures, which needs further verification and are currently beyond the domain
of this study.
(1) The scope of interested investors. When the media tells subscribers that a certain stock is
outperforming, it is likely to arouse the interest of all investors; when the media reports on an
underperforming stock, it may only interest those who have stakes in the stock given the shortselling constraints. Watchers of Jim Cramer’s show are hoping to get tips more on stocks to buy,
less on stocks to sell.
(2) Representative Bias. Investors may look into the characteristics of the outperforming stocks with
the hope of understanding why they outperformed and how to identify a new opportunity. The
representative heuristic might lead them to believe these stocks will continue to outperform. For
the poor performing stocks, they surely have less interest.
(3) Chasing past returns. Grinblatt et al (1995) reveal that mutual funds chase past winners.
Newswire subscribers are mostly investment professionals. If they seek to chase past returns, they
prefer their media to cover more stories on the winning stocks.
(4) Brag and look smart. Suppose media covers the prospects of two stocks intensively at a given
time. A year later, one of the stocks skyrockets and the other plummets. If readers of the first
story purchased the first stock mentioned in the story and readers of the second story purchased
the other stock, the media is more likely to repeat the previous story (or even cite their foresight).
Repeating stories on the first stock would emphasize that the media provided investors with a
valuable piece of investment information. The media may choose to avoid mentioning the
underperforming stock again to avoid blame for investors’ losses.
(5) Regret and Shame. Barber, Odean and Strahilevitz (2011) find that retail investors’ purchasing
behavior exhibit patterns of emotions. They repurchase stocks whose previous purchase resulted
in positive emotions and avoid stocks whose previous purchase resulted in negative emotions. IT
seems reasonable that investors would spontaneously pay attention to past winners simply
because they just missed the opportunities. With regard to losers, those without stakes in the
stocks would not experience regret in hearing about them; but those with stakes would find the
attention yo the losers painful. 5. Conclusions
This study demonstrates that the media has a long-run coverage bias toward high returns, and a short-run
bias toward extreme returns. The long-run bias is robust after controlling for market capitalization,
contemporaneous return, P/E ratio, analyst coverage, press releases (both contemporaneous and historic),
and industry fixed effect.
I show that the long-run bias mostly is on repetitive news, not fresh news, and the short run bias occurs
for contemporaneous news events. Our conjecture is that the short-run bias is driven by a sensationalism
bias. Investors naturally tune in to fresh, shocking news. However, once the shock period is over,
investors would lose interest in the losers. As shown, post-shock coverage on both types of extreme
returns drops. This is consistent with the sensationalism bias argued by Solomon and Soltes (2011). After
the shock stage, investors have to reassess the information they would like to access. Media coverage on
positive returns sustains at high levels and drops fastest on negative returns, indicating that investors
probably are more interested in winners, possibly because the stocks they are thinking of buying or have
recently bought more often than not have strong past performance.
The long-run bias I identify is important to the literature of attention-driven investment behaviors. When
our results are compared with those of Fang and Peress (2010), a much bigger picture of the coveragereturn relationship emerges. Fang and Peress (2010) find that stocks covered intensively tend to have low
future returns, and I show that high coverage stocks tend to have high past returns. This calls into
question their explanations. Future research could contribute to an understanding long-term return
reversal in relation to media coverage.
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