School Holidays and Stock Market Seasonality1
Nov 15, 2014
Lily Fang
INSEAD and MIT Sloan School of Management
Chunmei Lin
Erasmus University
Yuping Shao
National University of Singapore
Abstract
Globally, market returns are 0.5% to 1% lower in the month after major school holidays than
other times. The same seasonality is not observed in either macroeconomic variables or the
corporate earnings news. During holidays, abnormal institutional buys, sells, and short selling
around earnings announcements are 8%, 14%, and 18% lower than other times, and institutional
trades account for a smaller percentage of total market volume. Market response to negative
news is delayed. Our evidence suggests that the low after-holiday returns are attributable to the
market being collectively less attentive during holidays, and (negative) news is slowly
incorporated into prices.
1
We thank seminar participants at INSEAD and MIT Sloan for their comments and suggestions. All errors and
omissions are our own.
1
Introduction
In this paper, we document a novel asset pricing pattern: globally, market-wide returns
are 0.5% to 1% lower in the months after major school holidays than other times, and we present
evidence that this seasonality in stock returns is attributable to investor inattention during
holidays resulting in the slow incorporation of news, especially that of negative news.
It has been widely known among Wall Street traders that September—the month after the
summer school holiday—has historically been the worst performing month. This “September
effect” is striking in magnitude: For example, an article in the Wall Street Journal titled “How to
play the September effect” (Sep 6, 2013) points out that since 1896, the Dow Jones average
return for the month of September has been -1.09%, while that of all other months has been
+0.75%. A more recent article on the same topic illustrates the persistence of the September
effect: It is the only month that has a negative average return for 20, 50, and 100 years.2 What is
also noted by these practitioner discussions is that the origin of the September effect is not well
understood: The aforementioned 2013 WSJ article is accompanies by the following subtitle
“[i]t’s well known that September has been the worst month, on average, for stocks. But no one
has come up with a plausible explanation of why that is.”
We contribute to the understanding of the “September effect”—a remarkable yet hitherto
undocumented phenomenon in the academic literature—in two ways. First, we conjecture and
present evidence that the “September effect” reflects a broader “after holiday effect” whereby
market returns after major (long duration) holidays are significantly lower than other times.
Second, we propose an explanation to the after holiday effect, and hence also the September
effect that has long puzzled traders. We hypothesize that returns after major holidays are low
because during holidays, the market is collectively less attentive to news and as a result,
2
“Some stock strategists brace for September swoon”, the Wall Street Journal, Sep 1, 2014.
2
information is incorporated into prices slowly. This effect is particularly strong for negative
information because taking advantage of negative news is more difficult and requires more
attention, a scarce cognitive resource (Kahneman (1973)), than positive news. Miller (1977) and
Diamond and Verrecchia (1987) provide theoretical foundation that constraints on short selling
would impede the impounding of negative information into stock prices. If short selling is
curtailed during holidays, and we show that this is indeed the case, then the argument in Miller
(1977) and Diamond and Verrecchia (1987) would predict lower future returns after holidays.
Our empirical analysis consists of two main parts. We first establish the empirical fact
that there is a broader after-holiday effect which encompasses the September effect. To this end,
we collected school holiday data using school calendars from 47 countries around the world. We
find a striking pattern: returns are on average 1% lower in the months after major school holidays.
Importantly, this lower return not driven by September alone, even though the September effect
is pervasive in the northern hemisphere: Even when September is excluded, there is still a return
gap of at least 0.5% between after-holiday months and other times, and the difference remains
highly significant.
To ascertain what we document is indeed an “after holiday effect” rather than a spurious
result, we conduct a number of validation tests that exploit exogenous variations in school
calendars. For example, in the US, while the school year starts in September for many states,
some states in the south have school years that begin in early August. For stocks headquartered
in these states, we find that it is August, rather than September, that exhibit particularly low
returns. 3 In France, while some major holidays have common, nationwide dates, others are
staggered by region in order to reduce tourist congestion. We expect and find that the after3
This test makes the implicit assumption that investors exhibit local bias: local stocks are heavily held by local
investors. Thus it is a joint test of local bias and the after-holiday effect. A large extant literature has demonstrated
the local bias.
3
holiday effect is stronger after nationwide holidays than regional ones. Moreover, we find that
one region’s after-holiday dates do not predict low returns for another region (a placebo test),
unless the predictor is the last region that go on holiday. We also exploit the Chinese New Year
as a third validation test. The CNY holiday is short in countries/regions such as Singapore and
Hong Kong, as such is it not in our original school holiday dataset that counts only holidays
more than 2 weeks in duration. Nevertheless the event holds paramount cultural importance to
ethnic Chinese, and we find a pronounced after-CNY effect in Hong Kong, Singapore, China,
and Taiwan: return are nearly 2% lower after the festival than other times, even after controlling
for other school holidays.
After establishing a robust “after holiday effect”, the second element in our empirical
analysis is to examine alternative explanations of the phenomenon. One potential explanation is
that the same seasonality is observed in macroeconomic variables, so the return pattern is simply
a reflection of seasonal macro fundamentals. Another possibility is that the same seasonality is
observed in corporate news, for example, a disproportionate number of negative news is released
in after-holiday periods. We examine a host of macroeconomic measures and scrutinize the
composition of the corporate news. We do not detect the same seasonality in either dataset.
To test the investor inattention hypothesis, we decompose it into two basic elements.
First, for the investor inattention hypothesis to hold, it must be that investors are collectively less
attentive during holidays. Second, it is the inattention to news that results in low after-holiday
returns.
We examine volume data to investigate the first element that investors are less attentive
during holidays. While prior literature has documented that volume goes down during holidays
(Hong and Yu 2008), we bring two new pieces of evidence. First, we are able to identify
4
institutional buys and sells on a daily basis. This allows us to not only examine the seasonal
variation of institutional trades, but also study aggregate institutional volume as a percentage of
the total market volume. Second, we are also able to observe new short positions on a daily basis
at the stock level. This data is critical to directly test theories such as Miller (1977) and Diamond
and Virrecchia on the effect of reduced short selling.
Our empirical findings strongly support the notion that investors are collectively less
attentive during holidays. Abnormal institutional buys and sells around earnings release dates
reduce by about 18% during holidays compared to other times. In fact, overall institutional
trading account for a lower percentage of total market volume during holidays. In addition, new
abnormal short positions around earnings release also reduce by about 14% during holidays
compared to other times. The similar magnitude in the reduction of buys and sells and new short
positions is remarkable given that they are from different datasets and perhaps reflect different
institutional investor pool. The results suggest that reduced activity during holidays is pervasive
among institutional traders.
To examine the second element of the investor inattention hypothesis, namely that it is
inattention to news that result in lower after-holiday returns, we compare the after-holiday
returns among stocks sorted by news type. We find that the low return is concentrated among
stocks that release news during holidays and, among these, it is concentrated among stocks that
release negative news during holidays.
To provide direct evidence that response to news, especially negative news, is delayed
during holidays, we follow the method in Dellavigna and Pollet (2009) and construct, for each
earnings announcement, a “delayed response ratio” (DRR). The higher is the DRR, the more
slowly is news being incorporated into prices. Consistent with the predictions of the investor
5
inattention hypothesis, we find that the DRR is significantly higher for negative news during
holidays, but the effect is not observed for positive news.
Finally, we document that the after-holiday effect is stronger among larger stocks and
stocks with higher institutional holdings. This cross-sectional pattern is in sharp contrast to many
anomalies documented in the literature, which tend to be concentrated among small, illiquid
stocks with low institutional holdings. Typically, impediments to trade among small, illiquid
stocks prevent arbitrage activities to eliminate the anomaly. Thus at first glance, the fact that the
after-holiday effect is stronger among large, high institutional holding stocks is puzzling. But this
finding is precisely consistent with the investor inattention hypothesis. If institutional investors
are equally less attentive during holidays—which we show is the case, then limits to arbitrage
will be at work precisely because of their reduced activities.
Our paper makes a number of unique contributions to the literature. First, we document
an asset pricing anomaly that has not been discussed in the academic literature but one that has
long puzzled Wall Street professionals. Thus our empirical evidence is relevant to the academia
and the practitioner world alike. Second, we provide novel evidence on trading volume around
holidays, including the dynamics of institutional trading and short selling. Third, our work makes
a significant contribution to the literature on limited investor attention and its impact on asset
returns. According to the seminal work of Kahneman (1973), attention is a scarce cognitive
resource. Our examination of trading and returns during and after holidays provide rich and
direct evidence that investor inattention during holidays is pervasive and affects professional
traders and retail investors alike, which in turn affects asset returns.
6
The rest of the paper is organized as follows. Section I discusses related literature.
Section II presents the after holiday return anomaly. Section III tests different hypotheses and
Section IV concludes.
I.
Related Literature
Our paper is related Hong and Yu (2009), which document that trading volume is
significantly lower during the summer months (by about 8%), and contemporaneous returns are
also lower (by about 0.9%). While our paper clearly shares a similar flavor with Hong and Yu
(2009)—as they also examine summer holidays, our work is distinct in at least two ways. First,
Hong and Yu (2009) focus on the contemporaneous relation between volume and returns; they
use summer holiday as an exogenous variation in volume. We use volume as a proxy for investor
attention and study how it relates to the slow incorporation of information and lower future
returns. Second, we examine not only overall market volume, but also institutional buys and sells,
and short selling in order to understand why news travels slowly during holidays, especially
negative news. This has not been done in prior literature.
Our paper is also related to the work on the weekend effect. French (1980) and Gobbons
and Hess (1981) find that returns are higher on Fridays and lower on Mondays. Dellavigna and
Pollet (2009) provide evidence for investor inattention around the weekend: They find that
earnings announced on Fridays experience less initial response and more drift than those
announced in other days of the week. They also find low Monday returns. There is one important
difference between the weekend effect documented earlier and the after-holiday effect we
document here, which is that during the weekend there is no trading, but market remained open
over long school holidays. Thus, reduced trading during holidays reflects investor inattention.
7
Interestingly, in unreported analysis, we find that the low Monday return is even more
pronounced during school holidays, which is consistent with investor inattention driving the
effect.
Our paper is also broadly related to Kamstra, Kramer, and Levi (2003), which examine
the link between seasonal affective disorder (SAD) and market cycles. The authors find that
SAD, a condition that affects people in winter with few hours of sunlight, is related to high stock
market returns and they attribute it to higher risk aversion during the winter season. Our paper
documents that returns are low in the early fall (September), however the effect is not limited to
September but applicable to period after major (long) holidays.
II.
The After Holiday Effect
A. The baseline
In order to examine our conjecture that returns are low after major holidays, we collect
school holidays longer than 2 weeks in duration from 47 countries around the world. We focus
on major (long) school holidays for two reasons. First, we want to examine significant lengths of
time when traders are likely to be absent. Second, we want the market to remain open during this
time, which is different from weekends or other public holidays when markets are closed. Market
remaining open is important because it means there is still trading and any reduction in trading is
thus likely attributable to investor inattention.
We focus on returns in the months after these major school holidays. For example, US
has three major school holidays: summer, winter, and spring. The summer holiday starts in June
and typically ends after in the beginning of September (after Labor Day). The Winter holiday is
around Christmas and New Year, and the Easter holiday is in the second half of April. Thus,
8
September, January, and May are coded as “after holiday” months for the US. In refinement tests,
we collected specific calendar dates for the holidays and define the 30 calendar day afterwards as
the “after holiday” periods, results are qualitatively and quantitatively similar to those reported
below.
Table 1 reports the average stock returns by calendar month for all 47 countries in our
sample, using data from MSCI Total Return Index from 1/1970 to Dec 2012. We observe that
weak returns in September are pervasive: Out of 37 north-hemisphere countries, 28 have average
negative September returns. Looking at the global average, September is also the lowest return
month with a negative return of -0.03%, while all other months have positive averages. This is
consistent with the observations made by traders and practitioners such as those discussed in the
WSJ articles.
Table 2 tests whether the average return for each month is statistically different from that
of the other months. The table again reveals a strong September effect: for many countries, the
difference between September and other months is significantly negative. In comparison, the
January effect is weak: for the US, the average difference between January and other month is
0.53%, and not statistically significant. The table also reveals that December returns tends to be
high in many countries, consistent with Kamstra, Kramer, Levi (2003).
Table 3 examines the after-holiday returns in a regression setting. The baseline model
(Model 1) shows that, controlling for year and country fixed effects, returns during the months
after major school holidays is 1.1% lower than other month, with a t-stat of 9.61. Even when we
include month fixed effects (which severely biases against our hypothesis), the difference is still
0.6%, and highly significant (Model 2). Next, we exclude September observations because we
want to see that this is a robust result and not just driven by the pronounced September effect.
9
Results in Models 3 and 4 indicate that even after excluding September, the after-holiday month
returns are still 0.6% lower than other times, with the result being significant at the 1% level.
Thus, there is a strong after-holiday effect and it is not just driven by September.
To rule out that what we document above is a spurious result rather than being truly
related to the holiday cycle, next we conduct a number of validation tests that exploits exogenous
variations in the school calendar.
B. US Regions
Although the school year starts in September for many states in the US, in some states in
the south, school calendars are historically different and start in August owing to agricultural
demands.4 We collected actual school start dates for each state for 2013, and identified 5 states
with an early August start: (Aug 7), Indiana (Aug 6), Nevada (Aug 2), Oklahoma (Aug 5),
Tennessee (Aug 5), and Hawaii (Aug 5). If the September effect is truly related to the end of the
summer holidays, then we would expect that for stocks from these states, the “September effect”
would be an August effect instead.
To test this, we classified companies by their head-quarter locations. Companies that are
headquartered in one of the 5 states above are coded with a “South” indicator. We hypothesize
that in a return regression, the interaction term between “South” and August would be
significantly negative, while the interaction term between “South” and September would be
weaker. This means that August, rather than September, is a particularly low return months for
these companies. Note that this test makes the implicit assumption that investors exhibit local
4
The typical school year has 180 days, usually beginning in September and ending in June. But in some southern
states, the school year begins in August and end in May, a legacy of the agrarian calendar that ensured students
were out of school in time for the planting season. (“School in August gets low grade”, WJS, Aug 19, 2012.)
10
bias: stocks headquartered in one state are disproportionally held by investors from that state,
who follow the local holiday calendar. But there is substantial prior evidence supporting the
notion of local bias (e.g., Covel and Moskowitz (1999)).
Table 4 reports the results. In Panel A, we simply tabulate the average monthly valueweighted returns for stocks headquartered in the five “southern” states. One immediate
observation is August is the lowest return month with an average return of -0.14%. Panel B
presents the regression results using all US stocks. We note that September and August both
exhibit significantly lower returns (consistent with Hong and Yu (2007). Consistent with our
conjecture, the interaction term between August and South is significantly negative, but the
interaction term between September and South is not. The coefficient suggests that returns are
lower than other times by 28 basis points on average, and the effect is significant at the five
percent. Thus for Southern state companies, low returns occur in August rather than September,
as we expect.
C. French school zones
As a second validation test, we exploit exogenous school calendar variations in France.
France has five major school holidays during a school year: Christmas, winter, spring, summer,
and All-Saints Day. In order to reduce tourist congestion, France is divided into three regions
that follow staggered holiday schedules for the winter and spring holidays, which take place in
late Feb/early March, and late April/early May respectively. While the school zones have
changed prior to 1993, it has remains the same since 1993 and is as depicted in Figure 1. All
three regions, however, enjoy the same dates for the Christmas, summer holidays, and All-Saints
holidays. For the staggered winter and spring holidays, Region C goes on holiday first, followed
by Region B, then Region A.
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This setting allows us to test a number of predictions. First, we expect the after-holiday
effect to be stronger for the three nationwide holidays than the regional holidays. Second, if we
perform a “placebo” test which regresses one region’s returns on the other two regions’ holiday
schedule, we do not expect Region B or C’s “after holiday” dummies to predict Region A’s
returns because these regions go on holiday before A and their “after holiday” dummies would
not line up correctly with the actual after holiday dates for Region A. On the other hand, Region
A’s “after holiday” indicator may predict the other two Regions’ returns because Region A is the
last to go on holiday.
Table 5 presents the French regional test results. For the purpose of this test, we collected
specific holiday dates for each region from 1993 to 2013. In Panel A, we focus on the difference
between common holidays and regional holidays. The results indicate that the after-holiday
effect is generally stronger after common holidays than regional holidays: for both zone A and C,
the after common holiday indicator is negative and significant, and of larger magnitude and
significance than the after regional holiday indicator. Although for Zone B, while its regional
holiday indicator is significantly negative, the common holiday indicator is not.
Panel B of Table 5 presents the placebo tests where we regress one region’s stock returns
on another’s after-holiday indicators. The results are exact as we would expect: Neither zones B
and C’s after-holiday indicators are significantly negative in zone A’s regression because both
regions go on holiday before zone A, so the after-holiday indicator is not correctly picking up
Zone A’s after-holiday dates. But in zone B and C’s regressions, the zone A after-holiday
indicator is significantly negative. This is what we expect because zone A is the last to go on
holiday. Overall the test in this subsection provide strong support to the notion that the anomaly
we document is truly an after-holiday effect.
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D. Chinese New Year
Our third validation test utilizes the Chinese New Year (CNY). While this holiday is
observed in a number of countries, it is often quite short (1-2 days in duration) and hence not in
our main holiday data set (for instance this is the case for Hong Kong and Singapore). However
as the CNY is culturally the most important holiday in the Chinese tradition, even though the
official holiday is short, businesses and individuals often unofficially take extra days off. Thus, if
there is an after holiday effect, we expect similar mechanisms to be at work and result in low
returns after the Chinese New Year in regions where this festival is observed.5
Table 6 presents the results. For this test, we collected market return data for China,
Taiwan, Singapore, and Hong Kong. Results in this table strongly indicate that returns are
particularly low after the CNY holiday in these countries/regions: it is lower than other times by
an average of 1.8-1.9%. This effect is in fact larger and more significant than the school holiday
effect in these countries.
Overall, the evidence in this section provide strong evidence that returns are low after
major (school) holidays. This phenomenon is global, and is not just driven by September. In the
next section we examine different hypotheses that can potentially explain this phenomenon.
III.
Explaining the After Holiday Effect
A. Seasonality in macro conditions
One possible explanation for the seasonality in stock returns is that macro variables
exhibit the same seasonality. For example, if industrial output numbers released after holidays
tend to be lower—which reflects lowered real economic activity during holidays—then the lower
stock returns after holidays will be a simple reflection of seasonality in the macro conditions.
5
In mainland China, the Chinese New Year holiday is accompanied by a market closure as well.
13
To check this possibility, we collect a host of macro variables from the St. Louis Fed and
other sources. The macro variables we examine include real economic indicators such as
industrial production, unemployment, and consumption growth; monetary conditions such as
inflation, short-term interest rate (3-month T-bill rate), corporate borrowing cost (BAA corporate
bond rate), and default spread (BAA – AAA corporate bond rate), and risk and sentiment
indicators: the VIX index, the Baker-Wurgler sentiment index, and the University of Michigan
consumer sentiment index. Table 7 compares the values of these indicators for the after holiday
period and other periods, and none of the variables examined exhibits the seasonality in stock
returns.6 Thus variation in macro conditions is not a plausible explanation for the after-holiday
effect.
B. Seasonality in earnings news
Another potential explanation to the after-holiday effect is that the same seasonality is
observed in corporate earnings news. If there is a disproportionate amount of negative news in
the after-holiday months, returns in those months would be low.
To test this hypothesis, we collected information on consensus analyst forecasts and
actual reported earnings from I/B/E/S from 1983 (since I/B/E/S started coverage) to 2012. We
calculate the difference between actual earnings and consensus forecast, and classify positive
differences as “good new”, negative differences as “bad news”, and 0 difference as “no news”.
Results are tabulated in Table 8. In Panel A, we compare the after-holiday periods versus other
periods, and in Panel B we compare holiday periods versus other periods. The table shows that
for all periods, slightly over 50% of earnings news are positive, more than one-third is negative
and 10% is neutral; but there is no compositional difference in corporate earnings news in either
6
We also compared the value of these variables during school holidays and other times, and do not find any
seasonality there. In addition, we fail to find seasonality in the changes of these variables.
14
after-holiday periods or the holiday periods themselves compared to other periods. Thus, the
news composition hypothesis is not supported. 7
C. The Investor Inattention Hypothesis
The investor inattention hypothesis maintains that the after-holiday low returns is due to
investor inattention during holidays, which results in slow incorporation of information,
particularly that of negative information. In order to test this hypothesis, we break it down into
two basic components. First, a necessary condition for the hypothesis to hold is that investors
exhibit inattention during holidays. Second, it is the inattention to news, and particularly negative
news, that drives the low return after holidays.
To test that investors are indeed less attentive during holidays, we follow prior literature
and examine volume data. However we bring two new elements to the volume analysis. First, we
are able to identify institutional buys and sells from data provided by Abel Noser Solutions, a
trading-cost consultancy that covers trade data for more than 150 US institutional groups,
accounting for roughly 1/6 of the institutional investor universe. Thus we can examine not only
the overall market volume, but also institutional buys and sells separately, and institutional trades
as a fraction of the total market volume. A second novel dataset that we exploit is the daily short
selling data from Data Explorer, which allows us to examine the daily new short positions
opened, a clear indication of the activity in the short selling market.
The investor inattention hypothesis makes a number of specific predictions regarding
volume. First, overall market volume is expected to fall during holidays to reflect less intense
7
We checked our results using different thresholds in the definition of positive and negative news, for example, by
defining the top (bottom) 10% or 25% of earnings surprise (actual earnings-consensus forecast /consensus forecast)
during the year as good (bad) news, and the results are qualitatively the same—the news composition is not
different for after-holiday months compared to other months. We find that news intensity is lower (fewer
corporate earnings announcements) in holiday periods compared to other periods. However, the news
composition is again not different between holiday months and other months.
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trading. Second, if inattention affects professional traders, we should find that institutional buys
and sells also go down during holidays. Indeed, to the extent that professional investors are likely
to be on vacation en mass during school holidays, institutional volume may even become a
smaller percentage of total market volume. Third, short selling should also go down during
holidays. When there is less short selling activity, theoretical arguments in Miller (1977) and
Diamond and Verrecchia (1987) predict lower future returns.
Table 9 presents evidence on market volume and institutional buys and sells. Specifically,
to compare volume meaningfully across different time periods, we focus on abnormal volume
reaction to earnings news. The dependent variables are thus the buy/sell/volume data in the twoday window of the earnings announcement date (event window [0, 1]), minus the average
buy/sell/volume for the same stock during a 10-day window before the event (event window [-20,
-11]). We regress these abnormal volume measures on a holiday indicator and other stock-level
controls such as size, book-to-market, and past returns. The results reported in Table 9, Panel A
indicate dramatic reductions in institutional abnormal buys/sells for news announced during
holidays, compared to those announced in other periods: abnormal institutional buys around
earnings announcements are about 14% lower during holidays, and abnormal institutional sells
drop by about 8%. These magnitudes are economically large and statistically significant. The last
two columns of the table show that the overall abnormal market volume around earnings
announcements also goes down during holidays, albeit by a much smaller magnitude (less than
1%), and not significant in the specification that controls for firm fixed effect. Overall, the
evidence suggests a dramatic drop in institutional trading in holidays.
Table 9 Panel B examines institutional volume (sum of buys and sells) as a fraction of
overall market volume. The market volume data is from CRSP and the institutional volume data
16
is from Abel Noser Solutions. Since the Abel Noser Solutions sample consists of roughly 1/6 of
the overall institutional volume, we need to take this into account when considering economic
magnitudes. The regression results show that institutional volume (as proxied by Abel Noser
volume) as a percentage of total market volume goes down significantly by about 0.74% during
holidays compare to other times. Since true institutional volume is roughly 6 times Abel Noser
volume, the estimate suggests that institutional volume as a percentage of market volume goes
down by around 4.5%. If institutional account for 50% of total trading on average, then this
means the reduction is 10% of the average institutional presence, an economicly large drop.
Table 10 presents evidence on short-selling. The short selling measure we use is the 1day new short positions initiated. This is a much more precise measure of short selling activities
than roller measure such as the short interest. We again focus on abnormal short selling around
earnings announcements. Results reported in the table indicate that short selling is dramatically
reduced during holidays: the abnormal short-selling volume goes down by about 18% across the
board, whether it is good news or bad news. The symmetric magnitude between good and bad
news indicates that traders are likely absent from the market.
Putting the evidence on short selling together with institutional buy and sells from the
previous table, we see that buys, sell, and short selling reduce by 14%, 8%, and 18%,
respectively during holidays. Not only are these effects economicly large, their comparison with
each other is noteworthy. Some institutional sells may be driven by client flows, but buys are
more discretionary. If professional investors are less attentive during the holidays, it is not
surprising that their discretionary trades—buys—are reduced by a larger fraction than sells.
Second, the similar magnitudes between the reduction in buys and reduction in short-selling (14%
and 18%) is remarkable given that these estimate come from entirely different datasets. Their
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similarity gives further credence to the notion of investor inattention during holidays, resulting in
a similarly large drop in trades across the board.
Collectively, our volume analysis provide strong support to the notion that the market is
collectively less attentive during holidays: Not only does overall market volume do down, but
institutional trades—buys, sell, and short selling—all go down dramatically, by 8%-18%. In fact,
we estimate that institutional trades as a fraction of total market volume goes down by about 4.5%
during holidays. Our evidence paints a picture that the market during holidays is less institutiondriven with reduced activities, including short selling. To the extent that institutional investors
play a significant role in the price discovery process, these patterns should affect the market’s
ability to incorporate information to prices, as we show next.
To demonstrate the second element of the investor inattention—namely that investor
inattention is related to the low after-holiday returns, we conduct two additional tests. First, if it
is investors’ inattention to news, and especially negative news, that is driving the after-holiday
low returns, we expect that the low return is pronounced among stocks that release news during
holidays (news stocks), and especially among stocks that release negative news during holidays
(bad news stocks). Second, we follow Dellavigna and Pollet (2009) and construct a measure that
reflects market’s speed of incorporate information called the delayed response ratio (DRR). The
higher the DRR, the more slowly is the market incorporating information into prices. We should
observe that DRR is high for news release during holidays, especially negative news.
Table 11 tests the first prediction regarding the type of stocks that exhibit low returns in
the after-holiday periods. Panel A presents univariate test result and Panel B shows regression
results. From Panel A, we see that the low after-holiday return is mainly attributable to news
stocks: the no-news group have an average monthly after-holiday return of 2.75% (significantly
18
positive), and the news group have an average monthly after-holiday return of only 0.15% (also
significantly positive), and the difference is highly significant. Moreover, the low return is
mainly attributable to bad news stocks: among news stocks, good news stocks have an average
monthly after-holiday return of 0.286% (significantly positive), and the bad news stocks have an
average monthly after-holiday return of -0.13% (insignificantly different from zero). Thus, while
other stocks generally exhibit positive returns, stocks that release negative news during holidays
have after-holiday returns that are essentially zero. Regression results in Panel B confirm these
qualitative conclusions.
In table 12, we explicitly analyze the speed of market’s response to news using the
methodology in Dellavigna and Pollet (2009). Specifically, we calculate the total market
response (abnormal market return) to companies’ earnings news in a 30-day period or a 28-day
(4 week period). We calculate two delayed response ratios (DRRs). First, using 30-day as the
total response window, DRR [+2, +30] is the fraction of the total response that occurs during
event day +2 to event day +30. (In other words, the response during the event day 0 and +1 are
considered “immediate response). Second, using 28 days as the total response window, DRR [+7
+28] is the fraction of the total response that occurs during the 2nd week onwards (thus first-week
reaction is considered immediate). We then regress these DRR measures on a holiday dummy in
addition to a number of firm level controls. A higher DRR means the market responds to news
with more delay.
As we expect from the investor inattention hypothesis, the holiday indicator is
significantly positive—indicating higher DRRs, or lower speed of incorporation of news into
prices—in the regression for negative news releases, but not for positive news. The coefficient of
0.661 for DRR[+2, +30] means delayed response is 66.1% higher for negative news released
19
during holidays than other times, controlling for firm and time effects, and this increase is
significant at the 5% level. In contrast, delayed response for positive news is not larger for news
released during holidays than other times; the coefficients on the holiday dummy is either
negative or positive but not significant. Thus the market does react to news more slowly during
holidays, and the effect is asymmetric and concentrated on negative news.
Thus, the market’s speed of responding to news indeed reduces during holidays, but the
effect is asymmetric – it is pronounced for negative news but not evident for positive news.
These findings are consistent with the investor inattention hypothesis.
D. Cross-sectional patterns of the after-holiday effect
The previous subsection presented evidence that is consistent with the investor
inattention hypothesis. But it still leaves open the following question: if this pricing pattern
reflects market inefficiency, why is it not eliminated by profit motivated arbitrageurs? At least
part of the answer, we believe, lies in one piece of evidence that we have already presented
above: Institutional investors collectively accounts for a smaller fraction of the total market
volume in holidays than other times. That is, the likely arbitrageurs are absent from the market.
When professional traders are absent from the market, arbitrage has limited ability to correct
pricing anomalies (Shleifer (1997)), resulting in inefficiencies persisting.
If professional investors’ absence during holidays is at least a partial explanation to the
after-holiday effect, this has interesting cross-sectional implications for the strength of the
phenomenon. It implies that the after-holiday effect to be stronger among larger stocks with
higher institutional ownership. We test this prediction by examining the strength of the effect in
different subsample of stocks sorted by size and institutional ownership.
20
Table 13 presents the results. Panel A reports the key coefficients on the after-holiday
dummy from separate size-sorted portfolio return regressions and Panel B reports the same for
portfolios sorted by institutional holding. Panel A indicates that the after holiday effect is
decidedly a large-firm effect: the after-holiday returns are in fact positive and significant for the
portfolio of small stocks, and are significantly negative for the portfolio of large stocks. Panel B
suggests that the after-holiday effect is more driven by firms with high institutional holdings, as
the coefficient on the after-holiday indicator is positive and marginally significant for the low
institutional holding portfolio but negative (albeit insignificant) for the high institutional holding
portfolio.
The concentration of the after-holiday effect among large stocks and stocks with high
institutional holdings is a stark contrast to many anomalies documented before. Often, anomalies
are more pronounced among smaller, relatively obscure stocks with low institutional holding.
Liquidity and other impediments to trade in those cases prevent arbitrage activity from fully
exploit the pricing inefficiencies. Here in contrast, the anomaly is stronger among larger (hence
more liquid) stocks with higher institutional ownership. One consistent explanation for this
pattern is that the after-holiday effect is driven by investor inattention during holidays, which
affect professional investors at least as much as it does individual investors.
IV.
Conclusions
In this paper, we document a strong asset pricing anomaly – returns in the month after
major school holidays is 0.5% to 1% lower than other time. This phenomenon is global. It is
related to the September effect (that September has notoriously low returns historically) which is
widely known among Wall Street traders, but is not subsumed under it. Exploiting exogenous
21
variations in school calendars, we show that there is a genuine effect that returns are indeed
lower after major (school) holidays.
We hypothesize and provide evidence that the after-holiday low returns is due to investor
inattention during holidays, resulting in news, and especially negative news, being incorporated
slowly into prices. We show that seasonality in macro fundamentals and corporate earnings news
cannot explain the effect we document. But consistent with the investor inattention hypothesis,
trading is dramatically reduced during holidays: institutional buys, sells, and short selling all
reduce by 14%, 8%, and 18% respectively compared to other times. Furthermore, we find that
the low after-holiday returns are driven by stocks release news, and especially negative news,
during holidays. We provide concrete evidence that response to news, again especially to
negative news, is slower during holidays. Finally, we document that the after-holiday low return
anomaly is stronger among larger stocks with higher institutional holdings, which is in sharp
contrast to many anomalies that are more prevalent among small, illiquid stocks with low
institutional ownership. We believe that this cross-sectional pattern provides further credence to
the investor inattention hypothesis: the apparent lack of arbitraging activity to eliminate the
anomaly may be precisely due to the fact that even institutional investors are on vacation and less
attentive during long holidays. Indeed we find that institutional trading volume as a fraction of
total market volume reduces by 5% during holidays. Therefore, limits of arbitrage (Shleifer and
Vishny (1994)) are at work precisely because of the lack of institutional attention during holidays.
22
References
Coval, Joshua D., and Tobias J. Moskowitz. 1999, Home Bias at Home: Local Equity Preference
in Domestic Portfolios, Journal of Finance 54, 2046-2073.
DellaVigna, Stefano, and Joshua Pollet, 2009, Investor Inattention and Friday Earnings
Announcements, Journal of Finance 64, 709-749.
French, Kenneth R., 1980. Stock Returns and the Weekend Effect, Journal of Financial
Economics 8, 55-69.
Gibbons, Michael R. and Patrick Hess, 1981, Day of the Week Effects and Asset Returns, The
Journal of Business 54, 579-596.
Gultekin, Mustafa N. and N. Bulent Gultekin, 1983, Stock Market Seasonality: International
Evidence, Journal of Financial Economics 12, 469–81.
Hong, Harrison and Jialin Yu, 2009, Gone Fishin: Seasonality in Trading Activity and Asset
Prices, Journal of Financial Markets 12, 672-702.
Kamstra, Mark, Lisa A. Kramer, and Maurice D. Levi, 2003, Winter Blues: A SAD Stock
Market Cycle, American Economic Review 93, 324-343.
Keim, Donald B., 1983, Size Related Anomalies and Stock Return Seasonality, Journal of
Financial Economics 12, 13–22.
Reinganum, Marc R., 1983, "The Anomalous Stock Market Behavior of Small Firms in January:
Empirical Tests for Tax-loss Selling Effects," Journal of Financial Economics 12, 89–104.
Rozeff, Michael S. and William R. Kinney, Jr., 1976, Capital Market Seasonality: The Case of
Stock Returns, Journal of Financial Economics 3, 379–402.
Shleifer, Andrei, and Robert Vishny, 1997, The Limits of Arbitrage, Journal of Finance 51, 3555.
23
Figure 1. French school regions
This figure presents the three French school regions—A, B, and C, after 1993. Prior to 1993, the zoning was
different but it has been the same since 1993. Zone A consists of: Academies of Caen, Clermont-Ferrand, Grenoble,
Lyon, Montpellier, Nancy-Metz, Nantes, Rennes, Toulouse. Zone B consists of Academies of Aix-Marseille,
Amiens, Besançon, Dijon, Lille, Limoges, Nice, Orleans-Tours, Poitiers, Reims, Rouen, Strasbourg,
Zone C consists of Academies of Bordeaux, Créteil, Paris, Versailles. For the winter (Feb/March) and spring
(April/May) holidays, Region C goes on holiday first, followed by B, and then by A. All three regions have the same
dates for the Christmas, summer, and All-Saints holidays.
24
Table 1: Average Return by Calendar Month
This table reports the average return (in %) by calendar month for 47 countries. US data is from CRSP and covers
the period 1/1926-12/2012. International data is from MSCI Total Return Index and covers 1/1970-12/2012.
Country
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
S ep
Oct
Nov
Dec
ARGENTINA
7.00
11.19
7.25
5.42
12.22
12.62
2.22
AUSTRALIA
1.67
0.04
1.44
2.14
0.65
0.27
1.09
5.86
6.72
-2.29
-0.46
12.07
1.12
-0.26
0.41
0.04
AUSTRIA
0.82
2.42
1.56
1.92
0.27
0.21
2.72
0.88
-0.41
-1.72
-0.63
-0.11
BELGIUM
2.42
1.75
1.21
2.92
-0.59
3.09
0.27
1.39
0.08
-1.15
-0.08
0.62
BOTSWANA
1.08
2.85
4.18
1.84
2.58
2.83
2.64
3.70
5.86
1.21
1.26
0.77
-0.61
BRAZIL
17.36
12.19
7.41
CANADA
1.88
1.21
1.01
11.23
8.03
3.26
9.70
4.43
9.19
5.52
7.42
16.78
0.33
1.21
0.28
1.13
0.99
-1.20
-0.19
1.63
CHILE
3.23
3.09
2.59
1.02
2.41
2.13
2.87
1.46
-0.60
0.52
1.80
1.20
CHINA
-3.67
2.85
2.67
-1.90
2.82
1.67
2.37
0.67
-2.33
1.02
0.76
-0.12
COLUM BIA
2.38
3.56
0.16
-0.26
5.52
0.62
-0.58
2.92
0.64
2.91
0.78
3.76
5.06
CZECH
1.53
1.02
2.14
1.97
1.06
-0.74
3.86
0.36
-1.31
-0.03
-1.39
4.28
DENM ARK
3.37
0.43
0.43
2.14
1.41
1.29
1.60
0.07
-1.14
1.26
-0.08
2.59
FINLAND
2.84
0.11
2.87
3.95
-1.04
-1.62
0.41
-1.41
-1.28
4.75
2.92
0.76
FRANCE
2.25
1.68
2.07
2.80
0.20
-1.00
1.04
0.35
-1.45
0.77
0.95
1.78
GERM ANY
1.26
1.08
1.49
2.01
-0.83
0.84
1.29
-0.83
-1.63
1.28
1.15
2.23
GREECE
4.46
1.44
1.03
4.46
-0.98
0.32
3.84
-0.78
-0.72
-1.71
-1.55
1.79
HONGKONG
3.77
3.72
-1.16
2.40
2.28
1.01
2.84
-0.75
-0.56
3.18
-0.30
4.32
HUNGARY
6.37
0.24
1.50
4.75
0.12
0.46
3.72
-0.71
-1.51
0.57
-0.19
5.06
INDIA
0.81
2.63
-1.79
0.57
0.71
2.22
1.70
1.84
1.55
-1.27
2.18
4.55
INDONESIA
3.51
1.17
2.81
2.96
3.31
2.24
2.25
0.15
-2.23
-0.19
2.27
8.29
IRELAND
2.81
0.88
2.20
2.70
-0.97
-0.91
-0.97
-0.85
-2.36
0.47
-0.47
3.10
ISRAEL
1.65
0.28
-0.34
1.86
0.79
-0.66
0.01
-0.08
0.01
0.44
2.05
2.80
ITALY
4.07
1.78
1.73
1.97
-0.77
-0.86
0.30
0.94
-1.53
0.09
0.71
1.54
JAPAN
1.29
0.94
2.00
1.45
-0.04
0.66
-0.06
-0.69
-0.73
-0.83
0.75
2.46
KOREA
4.95
-1.53
1.90
2.46
-0.46
-0.63
2.23
-1.24
-0.38
0.74
3.04
2.25
M ALAYSIA
1.74
3.30
-0.60
1.94
0.70
-0.03
1.20
-2.96
-0.48
2.78
0.56
4.78
M EXICO
1.97
1.56
4.53
1.90
4.35
1.18
1.99
0.55
1.01
2.88
3.83
4.09
NETHERLANDS
2.04
0.65
2.39
2.54
0.16
0.50
1.50
0.07
-2.16
0.39
0.74
2.53
NEWZEALAND
1.10
-1.29
1.52
2.99
-0.12
-0.46
2.97
0.00
-1.06
0.42
0.39
0.54
NORWAY
3.32
0.46
0.89
4.14
1.42
0.50
2.50
0.24
-1.85
0.26
-0.27
2.07
PAKISTAN
5.28
5.30
2.29
0.25
-5.09
1.15
3.58
-0.39
1.83
2.31
1.44
1.83
PERU
1.12
3.41
3.06
3.60
2.03
0.39
1.64
1.52
3.67
0.14
1.90
2.84
PHILIPPINES
2.86
0.72
0.21
2.33
2.59
0.34
1.76
-2.80
1.02
1.30
0.83
3.90
POLAND
4.82
1.69
0.09
5.26
4.59
-0.54
3.99
2.19
-2.25
0.90
0.92
4.04
PORTUGAL
2.25
1.60
0.82
0.23
0.06
-1.31
0.09
0.23
-0.82
0.78
0.13
1.56
RUSSIA
0.58
3.79
6.34
4.74
3.67
4.90
-0.58
-1.53
-4.57
2.84
1.21
6.21
SINGAPORE
3.88
1.05
0.30
1.72
1.67
0.89
0.80
-1.71
-1.00
0.98
0.59
3.51
SOUTHAFRICA
1.25
1.31
1.60
2.97
0.44
-0.74
0.31
1.21
0.28
2.26
1.53
4.23
SPAIN
2.54
2.20
1.11
1.97
0.78
0.66
0.52
0.46
-1.45
0.98
1.43
1.00
SRILANKA
2.06
0.57
-0.57
1.04
3.01
4.56
2.13
-1.21
4.86
0.66
-0.40
1.00
SWEDEN
3.20
3.14
1.52
2.83
0.24
0.38
2.63
-1.81
-1.70
1.09
2.64
1.91
SWITZERLAND
1.65
0.15
1.22
1.20
0.02
0.76
0.93
-0.19
-1.36
1.23
1.11
2.08
TAIWAN
3.77
4.07
0.97
1.90
-0.48
-0.39
0.82
-1.33
-1.97
-1.18
2.06
3.67
THAILAND
4.76
0.65
-0.08
3.73
-0.30
0.92
0.77
-0.50
-0.17
0.80
0.58
4.83
TURKEY
8.75
3.77
0.37
9.37
-0.90
5.30
3.73
-0.48
6.33
4.59
3.77
10.22
UK
2.42
1.67
1.14
3.01
-0.39
-0.44
0.88
1.25
-0.80
0.66
0.78
2.49
US
1.40
0.29
1.33
1.51
0.52
0.51
0.59
0.33
-0.54
1.03
1.56
1.89
Average
3.04
1.99
1.54
2.90
1.12
0.98
1.79
0.11
-0.03
0.96
1.15
3.59
25
Table 2. Market Seasonality: Return Differences by Calendar Month
This table reports the difference (in %) between the average return for each calendar month and the average return
for other months for 47 countries. US data is from CRSP and covers the period 1/1926-12/2012. International data is
from MSCI Total Return Index and covers 1/1970-12/2012. *, **, *** indicates that the difference is statistically
distinguishable from 0 using a two-tailed test, at the 10%, 5%, and 1% level, respectively.
Country
Jan
Feb
Mar
Apr
ARGENTINA
0.35
4.53
0.6
-1.23
AUSTRALIA
0.72
-0.9
0.5
1.2*
AUSTRIA
0.13
1.73*
0.87
1.22
BELGIUM
1.47*
0.8
0.26
1.97***
BOTSWANA
-1.22*
0.55
1.88***
-0.46
0.53
0.34
BRAZIL
7.98*
2.82
-1.97
1.85
-1.35
-6.12*
CANADA
0.97
0.3
0.1
-0.58
0.3
-0.62
CHILE
1.4
1.26
-0.81
0.58
0.3
CHINA
-4.2*
2.15
-2.42
2.29
COLUM BIA
1.47
-1.93
-2.35
3.43*
CZECH
May
Jun
Jul
Aug
S ep
Oct
Nov
Dec
5.57
5.97
-4.43
-0.29
-0.67
0.15
-0.79
0.07
-8.95**
-7.12*
5.42***
0.17
-1.2*
-0.54
-0.91
-0.42
-0.48
1.77***
0.19
-1.1
-2.41**
-1.32
-0.8
-1.54***
-0.68
2.4***
0.44
-0.87
-2.1***
-1.03
-0.33
1.63***
1.4
3.56
-1.09
-1.04
-1.54
-2.91***
0.33
-4.95**
-0.18
-3.86
-1.96
7.4*
0.23
0.08
-2.11***
-1.09
0.72
1.68
1.04
-0.37
-2.43*
-1.31
-0.03
-0.63
1.02
1.14
1.84
0.14
-2.86
0.49
0.23
-0.65
1.85
-1.47
-2.67
0.83
-1.45
0.82
-1.31
1.66
2.97***
0.46
-0.05
1.08
0.91
0
-1.8
2.8**
-0.7
-2.37**
-1.09
-2.45
3.22**
2.25**
-0.69
-0.68
1.02
0.3
0.18
0.49
-1.05
-2.26***
0.15
-1.19*
1.47**
FINLAND
1.73
-1
1.76
2.85
-2.14*
-2.72**
-0.69
-2.52
-2.38*
3.65*
1.81
-0.34
FRANCE
1.29
0.72
1.12
1.84
-0.75
-1.96***
0.09
-0.61
-2.41**
-0.18
0
0.83
GERM ANY
0.48
0.3
0.72
1.23
-1.61**
0.06
0.51
-1.61*
-2.41***
0.5
0.37
1.45**
GREECE
3.49*
0.47
0.06
3.49
-1.95
-0.65
2.88*
-1.74
-1.69
-2.68
-2.51
0.82
HONGKONG
2.04
1.99
-2.89***
0.67
0.55
-0.72
1.11
-2.48**
-2.29*
1.45
-2.03
2.59**
HUNGARY
4.67
-1.46
-0.2
3.05*
-1.58
-1.24
2.03
-2.41
-3.21**
-1.13
-1.89
3.36**
INDIA
-0.49
1.32
-3.1
-0.74
-0.6
0.91
0.39
0.53
0.24
-2.58
0.87
3.24***
INDONESIA
1.3
-1.04
0.6
0.75
1.1
0.03
0.04
-2.06
-4.44*
-2.4
0.06
6.08*
IRELAND
2.34
0.41
1.73*
2.23**
-1.44
-1.38*
-1.44
-1.32
-2.83***
0
-0.94
2.63***
ISRAEL
0.91
-0.45
-1.07
1.13
0.06
-1.4
-0.72
-0.82
-0.72
-0.3
1.32
2.06*
ITALY
3.24**
0.94
0.9
1.14
-1.6**
-1.69*
-0.53
0.11
-2.36**
-0.74
-0.12
0.71
JAPAN
0.69
0.34
1.4
0.85
-0.64
0.06
-0.66
-1.29
-1.33**
-1.43
0.15
1.86***
KOREA
3.84
-2.64
0.79
1.34
-1.57
-1.74
1.12
-2.35*
-1.5
-0.37
1.92
1.14
M ALAYSIA
0.66
2.23
-1.68
0.86
-0.38
-1.11
0.13
-4.04**
-1.56
1.7
-0.52
3.7***
M EXICO
-0.51
-0.93
2.04
-0.58
1.87
-1.31
-0.49
-1.94
-1.48
0.39
1.34
1.61
NETHERLANDS
1.09
-0.3
1.45*
1.59**
-0.78
-0.44
0.55
-0.88
-3.1***
-0.55
-0.21
1.58***
NEWZEALAND
0.51
-1.87**
0.94
2.4***
-0.7
-1.04
2.39***
-0.58
-1.65*
-0.16
-0.19
-0.05
NORWAY
2.18**
-0.68
-0.25
3***
0.28
-0.64
1.36
-0.9
-2.99***
-0.87
-1.41
0.93
PAKISTAN
3.63
3.65*
0.64
-1.4
-6.74***
-0.5
1.93
-2.03
0.19
0.67
-0.21
0.18
PERU
-0.99
1.3
0.95
1.49
-0.08
-1.72
-0.47
-0.59
1.56
-1.97
-0.21
0.73
PHILIPPINES
1.61
-0.54
-1.05
1.07
1.34
-0.91
0.51
-4.05**
-0.24
0.04
-0.43
2.64
POLAND
2.68
-0.45
-2.05
3.12
2.45
-2.68
1.85
0.05
-4.39**
-1.24
-1.22
1.9
PORTUGAL
1.78
1.13
0.35
-0.24
-0.41
-1.78**
-0.38
-0.24
-1.29
0.32
-0.34
1.09
DENM ARK
RUSSIA
-1.72
1.49
4.04
2.44
1.37
2.6
-2.88
-3.83
-6.87**
0.54
-1.09
3.91
SINGAPORE
2.82*
-0.01
-0.76
0.66
0.61
-0.17
-0.26
-2.77***
-2.06**
-0.08
-0.47
2.45**
SOUTHAFRICA
-0.14
-0.08
0.22
1.58
-0.95
-2.13
-1.08
-0.17
-1.11
0.87
0.14
2.84**
SPAIN
1.52*
1.18
0.1
0.96
-0.24
-0.36
-0.5
-0.56
-2.47***
-0.04
0.42
-0.01
SRILANKA
0.59
-0.9
-2.04
-0.44
1.53
3.09
0.66
-2.68
3.38*
-0.82
-1.88
-0.47
SWEDEN
1.86*
1.8*
0.18
1.49
-1.1
-0.96
1.29**
-3.15***
-3.04***
-0.25
1.3
0.57
SWITZERLAND
0.91
-0.59
0.48
0.47
-0.71
0.03
0.2
-0.92
-2.09***
0.49
0.38
1.34***
TAIWAN
2.78
3.08
-0.02
0.91
-1.47
-1.38
-0.17
-2.32
-2.96*
-2.18
1.06
2.68
THAILAND
3.43
-0.68
-1.41
2.4
-1.64
-0.41
-0.56
-1.83
-1.5
-0.53
-0.75
3.49**
TURKEY
4.18
-0.8
-4.2
4.8
-5.47
0.73
-0.84
-5.05
1.76
0.02
-0.8
5.65*
UK
1.37
0.61
0.08
1.96
-1.44
-1.49**
-0.18
0.2
-1.86**
-0.39
-0.28
1.43**
US
0.53
-0.58
0.46
0.64
-0.35
-0.36
-0.28
-0.54
-1.41**
0.16
0.69
1.02**
26
Table 3. The After-holiday Effect
This table reports regression results relating school holidays and market level returns. The data is from MSCI Total
Return Index, 1/1970-12/2012. The “After Holiday Month” is a dummy variable that equals 1 for months after a
major school holiday, and zero otherwise. A major school holiday is a school holiday that is at least 2 weeks long. tstatistics are reported in parentheses. *, **, *** indicates statistical significance at the 10%, 5%, and 1% level using
a two-tailed test, respectively.
Full Sample
After-holiday-month dummy
Intercept
Year fixed effects
Month fixed effects
Country fixed effects
Clustered by country
number of observations
R-squared
Model 1
-0.011***
(-9.61)
0.016***
-53.92
Model 2
-0.006***
(-3.87)
--
Yes
No
Yes
Yes
11807
22.20%
Yes
Yes
Yes
Yes
11807
6.11%
Exclude September
Model 3
Model 4
-0.005***
-0.006***
(-3.40)
(-3.07)
---
Yes
No
Yes
Yes
10823
4.16%
Yes
Yes
Yes
Yes
10823
5.14%
27
Table 4. US Southern States and August Returns
Panel A tabulates mean value-weighted returns of stocks headquartered in five states that have an early August start
of the school year: Georgia (Aug 7), Indiana (Aug 6), Nevada (Aug 2), Oklahoma (Aug 5), Tennessee (Aug 5), and
Hawaii (Aug 5). Panel B presents regression results. The dependent variable is monthly stock return. The universe is
all CRSP stocks. The indicator variable “South” equals 1 if a stock is headquartered in one of the five states listed
above and 0 otherwise. “September” and “August” are indicator variables for those calendar months, respectively. *,
**, *** indicates statistical significance at the 10%, 5%, and 1% level using a two-tailed test, respectively.
Panel A: Univariate result
Calendar Month
Value-Weighted Return
1
2
3
4
5
6
7
8
9
10
11
12
-0.03%
0.75%
2.02%
2.73%
1.62%
0.27%
0.87%
-0.14%
0.75%
2.42%
2.88%
2.72%
Panel B: Regression result
Intercept
September
South
Sep*South
August
August*South
N
R squared
Coefficient
1.083
-1.579***
0.021
0.031
-0.412***
-0.285**
T-stat
-0.51
(-50.23)
-0.04
-0.25
(-13.11)
(-2.27)
1,886,718
3%
28
Table 5. French School Regions
This table presents results using French school regions. Zones A, B, and C are as defined in Figure 1. “After
common holiday” is an indicator variable that equals 1 for a day that is within 30 days after a common holiday in
France (Christmas, summer, or All Saints holidays). “After regional holiday” is an indicator variable that equals 1
for a day that is within 30 days after a regional holiday (winter and spring holidays). “After zone X holiday” is an
indicator variable that equals 1 for a day that is within 30 days after region X’s holiday. *, **, *** indicates
statistical significance at the 10%, 5%, and 1% level using a two-tailed test, respectively.
Panel A:Common holidays versus regional holidays
All Zones
Zone A
Zone B
Zone C
0.183***
0.121**
0.143***
0.187***
(14.5)
(2.22)
(6.51)
(14.28)
-0.124***
-0.129***
0.042
-0.131***
(-8.11)
(-6.86)
(1.48)
(-8.2)
0.006
-0.051***
-0.102***
0.013
(0.29)
(-3.53)
(-4.77)
(0.59)
Year Fixed Effect
Yes
Yes
Yes
Yes
Month Fixed Effect
Yes
Yes
Yes
Yes
R2
0.002171
0.002497
0.002234
0.002263
N
2,724,500
571017
448,077
1,705,406
Intercept
After common holiday
After regional holiday
29
Panel B: Placebo tests
Zone A
Intercept
.
After common holiday
.
After Zone B holiday
0.122**
0.121**
(2.21)
(2.21)
-0.130***
-0.130***
(-7.07)
(-7.07)
0.036
.
(0.5)
After Zone C holiday
0.065*
.
(1.92)
R-squared
0.002502
0.002519
obs
559,837
559,837
Zone B
Intercept
.
After common holiday
.
After_Zone A holiday
0.142***
0.142***
(6.31)
(6.32)
0.009
0.009
(0.19)
(0.19)
-0.031**
.
(-2.12)
After_Zone C holiday
0.082**
(2.3)
R-squared
0.002259
0.002324
obs
411,208
411,208
Intercept
0.188***
0.187***
(14.3)
(14.28)
-0.131***
-0.131***
(-8.2)
(-8.2)
Zone C
.
After common holiday
.
After Zone A holiday
-0.106***
.
(-4.18)
After Zone B holiday
-0.005
(-0.23)
R-squared
0.002378
0.002262
obs
1,705,406
1,705,406
30
Table 6. The Chinese New Year
This table presents results examining returns after the Chinese New Year. Data is from MSCI total return index for
China, Taiwan, Singapore, and Hong Kong, from 1/1970 to 12/2013. (The data sample from China is shorter, and
starts in 1990). “After Chinese New Year dummy” is an indicator variable that equals 1 for the month after the
Chinese New Year holiday 0 otherwise. “After other holiday dummy” is an indicator variable that equals 1 for the
month after the other school holidays (2 weeks or longer in duration) and zero otherwise for each country. *, **, ***
indicates statistical significance at the 10%, 5%, and 1% level using a two-tailed test, respectively.
After-Chinese-New-Year dummy
After-other-holiday dummy
Year fixed effects
Month fixed effects
Country fixed effects
Clustered by Country
number of observations
R-squared
Model 1
-0.019**
(-2.20)
-0.008
(-1.32)
No
No
No
No
1572
0.37%
Monthly Index Return
Model 2
Model 3
-0.019***
-0.018*
(-2.80)
(-1.80)
-0.009***
-0.011***
(-3.01)
(-5.54)
Yes
No
Yes
Yes
1572
9.44%
Yes
Yes
Yes
Yes
1572
11.54%
t statistics in parentheses
* p<.1, ** p<0.05, *** p<0.01
31
Table 7. Seasonality in Macro Conditions
This table compares the value of macro variables in the months after major school holidays with other months.
After-holiday months
Other months
t-stat (diff)
Unemployment
Consumption growth
39.55
5.82
0.55
39.52
5.83
0.56
0.01
0.08
0.18
Monetary conditions:
Inflation
3m T-bill yield
BAA bond yield
Default spread
TED spread
0.32
3.66
7.04
1.19
0.63
0.29
3.67
7.05
1.2
0.62
-1.14
0.04
0.06
0.17
-0.23
20.3
0.00904
85.88
20.3
-0.00298
85.01
0.04
0.12
-0.6
Real economic activity:
Industrial Production
Risk and sentiment indicators:
VIX
BW Sentiment
U. Mich Cons. Sent.
32
Table 8. Composition of corporate earnings news
This table compares the composition of corporate earnings news in after-holiday periods versus other periods (Panel
A), and in school holiday periods and other periods (Panel B). Positive (negative) news is defined as actual reported
earnings exceeding (lower than) consensus analysts forecast as at 5 days prior to the earnings report.
Panel A: After-holiday periods versus other periods
After holiday months Other months
Proportion negative
36.38%
34.26%
Proportion zero
10.90%
11.73%
Proportion positive
52.71%
54.01%
Difference
-2.13%
0.83%
1.30%
t-stat (diff)
-0.74
0.62
0.71
Panel B: holiday periods versus other periods
Holiday months
Proportion negative
33.60%
Proportion zero
11.61%
Proportion positive
54.79%
Difference
1.84%
-0.18%
-1.66%
t-stat (diff)
0.64
-0.14
-0.9
Other months
35.43%
11.43%
53.14%
33
Table 9. Volume analysis
Panel A presents regression analysis of abnormal volume (buy/sell/volume) around earnings announcements. The
dependent variable is abnormal buy/sell/volume, measured as the buy/sell/volume measure during the 2-day window
at the earnings announcement (event days [0,1]) minus the average buy/sell/volume during a 10-day period prior to
the announcement (event days [-20, -11]). Holiday is an indicator variable that equals 1 if the earnings
announcement occurs during a school holiday and zero otherwise. Surprise volatility is the stock return volatility
during the 20-days around the earnings announcement. Logsize is the natural log of 1 plus the market capitalization
of equity. Panel B presents regression analysis of institutional volume (proxied by total buys and sells from Abel
Noser Solution data) at the stock level as a fraction of total market volume. The dependent variable is the daily
stock-level total buys and sells from Abel Noser Solutions divided by total daily market volume from CRSP. *, **,
*** indicates statistical significance at the 10%, 5%, and 1% level using a two-tailed test, respectively.
Panel A: Abnormal volume around earnings announcement, holiday versus other times
Dependent var = abnormal volume around earnings measured as Log Var[0,1]-LogVar[-20,-11]
buy
buy
sell
sell
Volume
Volume
-0.1437***
-0.1366***
-0.0831***
-0.0683**
-0.0096***
-0.0026
(-5.85)
(-5.47)
(-3.13)
(-2.52)
(-5.23)
(-1.55)
0.0254
0.5015
-1.7552**
3.5139***
0.0131
0.3496***
(0.03)
(0.45)
(-2.07)
(2.92)
(0.39)
(8.28)
logsize
0.0355***
0.1985***
-0.0276***
0.2090***
0.0031***
0.0401***
(4.54)
(7.75)
(-3.27)
(7.54)
(5.72)
(28.62)
Constant
0.3213***
-1.1970***
1.0044***
-1.1393***
0.1612***
-0.3521
(4.76)
(-5.66)
(13.76)
(-4.98)
(20.67)
(-0.00)
Earnings surprise decile
Yes
Yes
Yes
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
YES
YES
Firm FE
No
Yes
No
Yes
No
YES
Observations
68,496
68,496
68,496
68,496
158,558
158,558
R-squared
0.005
0.115
0.004
0.111
0.081
0.309
holiday
Surprise Volatility
34
Panel B: Institutional volume as a fraction of total market volume
Dependent Var = Institutional Volume/Total Market Volume
Holiday
-0.0066***
-0.0074***
(-24.76)
(-28.53)
bm
16.5911
13.4744***
-1.62
-2.78
size
-0.0141***
-0.0143***
(-29.22)
(-17.73)
0.3307***
0.2810***
-48.29
-24.83
8,608,751
8,608,751
R-squared
0.082
0.151
Year FE
YES
YES
Firm FE
No
YES
Constant
Observations
35
Table 10. Short sell volume
This table presents regression analysis of abnormal short selling around earnings announcements. The dependent
variable is the short selling in the 2-day window at the earnings announcement (event days [0, 1]), minus the short
selling during a 10-day window prior to the announcements (event days [-20, -11]). Holiday dummy is an indicator
variable that equals 1 if the earnings announcement is made during a school holiday and 0 otherwise. Log size is the
natural log of a firm’s market capitalization of equity. Log BM is the log of a firm’s book-to-market ratio. Past
return is the return decile in the past 12 month. Positive news means the announced earnings exceeds the consensus
forecast. Negative news means the announced earnings is below the consensus forecast. *, **, *** indicates
statistical significance at the 10%, 5%, and 1% level using a two-tailed test, respectively.
Dependent var = abnormal short selling around earnings measured as Log Var[0,1]-LogVar[-20,-11]
All News
Positive News
Negative news
Model 1
Model 2
Model 1
Model 2
Model 1
Model 2
-0.166***
-0.188***
-0.171***
-0.189***
-0.146***
-0.183***
(-13.09)
(-14.62)
(-10.55)
(-11.13)
(-6.25)
(-6.77)
0.015***
0.040**
0.008
0.003
0.032***
0.061
-3.91
-1.96
-1.55
-0.11
-4.24
-1.52
-0.012
-0.002
-0.015
-0.013
-0.017
0.005
(-1.52)
(-0.10)
(-1.42)
(-0.55)
(-1.20)
-0.14
0.047***
0.044**
0.054***
0.053***
0.033
0.026
-3.36
-2.57
-3.17
-2.75
-1.22
-0.67
Earnings surprise decile
Yes
Yes
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
Firm fixed effects
No
Yes
No
Yes
No
Yes
Cluster by firm
No
Yes
No
Yes
No
Yes
number of observations
31521
31521
18909
18909
9798
9798
R-squared
0.011
0.013
0.01
0.013
0.012
0.011
Holiday dummy
Log Size
Log BM
Past Return
36
Table 11. Which type of stocks contribute to the after-holiday effect?
This table compares returns after school holidays among different subsets of stocks. New stocks are those that
release corporate earnings news during holidays. No news stocks are those that do not release corporate earnings
during holidays. Good (bad) news stocks are those that release positive (negative) earnings news during holidays,
i.e., their actual reported earnings is above (below) consensus analyst forecast. Size is the log of a firm’s market
capitalization. BM is the log of a firm’s book-to-market ratio. *, **, *** indicates statistical significance at the 10%,
5%, and 1% level using a two-tailed test, respectively.
Panel A: Univariate comparison of average monthly return
After-holiday return
After-holiday return
New stocks
No news stocks
t-stat (diff)
0.00154
0.0275
-26.10***
Good news stocks
Bad news stocks
t-stat (diff)
0.00286
-0.00132
-2.94***
Panel B: Regression analysis Dep var = after-holiday return
News stock
Negative News stock
-0.1077***
-0.0507***
-0.0645***
-0.0538***
(-22.93)
(-10.21)
(-13.13)
(-10.64)
-0.0189**
-0.0314***
-0.0186**
-0.0246***
(-2.29)
(-3.71)
(-2.25)
(-2.90)
size
lnbm
-0.0315***
-0.0483***
(-51.39)
(-28.84)
1.3005
3.2346
(0.29)
Constant
(0.47)
0.1263***
0.5062***
0.0459
0.5308***
(101.79)
(68.03)
(1.50)
(10.90)
571,332
524,734
571,332
524,734
R-squared
0.025
0.031
0.088
0.092
Year FE
YES
YES
YES
YES
Firm FE
NO
NO
YES
YES
Observations
37
Table 12. Delayed Market Response
This table reports analysis of the Delayed Response Ratio (DRR) for earnings announcements. Positive (negative)
news is where firms’ reported earnings exceeds (falls short of) prevailing consensus. The holiday dummy equals 1 if
the earnings news is released during major school holidays and 0 otherwise. Friday dummy equals 1 if the earnings
news is released on a Friday and 0 otherwise. Surprise is the magnitude of the earnings surprise, measured as actual
earnings minus prevailing consensus, divided by prevailing consensus. *, **, *** indicates statistical significance at
the 10%, 5%, and 1% level using a two-tailed test, respectively.
Positive News
Holiday dummy
Friday dummy
Surprise
Year fixed effects
Month fixed effects
Firm fixed effects
Clustered by firm
Observations
R-squared
Negative News
DRR [+2, +30]
DRR [+7, +28]
DRR [+2, +30]
DRR [+7, +28]
-0.177
(-1.04)
-0.041
(-0.56)
0.819
(1.51)
0.057
(0.29)
-0.077
(-0.92)
-0.037
(-0.06)
0.661**
(2.18)
0.088
(0.98)
0.197
(0.80)
0.767**
(2.42)
0.059
(0.60)
0.421
(1.17)
Yes
Yes
Yes
Yes
66,027
0.07%
Yes
Yes
Yes
Yes
66,027
0.04%
Yes
Yes
Yes
Yes
39,006
0.09%
Yes
Yes
Yes
Yes
39,006
0.06%
38
Table 13: The after-holiday effect in the cross-section of stocks
This table presents regression results of the after-holiday effect in separate stock portfolios formed by sorting stocks
on size (Panel A), and institutional holdings (Panel B). We divide the universe of CRSP stocks into three equal
portfolios by size or institutional holdings, and compute value-weighted average returns for each portfolio over time.
We then regress each portfolio return time series on an after-holiday indicator and other controls. The table reports
the key coefficients on the after-holiday dummy from separate regressions. *, **, *** indicates statistical
significance at the 10%, 5%, and 1% level using a two-tailed test, respectively.
Panel A: Regression results for portfolios sorted by firm size. Dep var = value-weighted return
Full sample
VWRET-RF
Exclude September
VWRET-RF
0.012***
0.017***
(4.51)
(4.88)
0.001
0.002
(1.35)
(1.09)
-0.000**
-0.001**
(-2.13)
(-2.06)
Contol variables in all regressions
Yes
Yes
Year fixed effect
Yes
Yes
Clustered by year
Yes
Yes
After-holiday dummy: Small firms
After-holiday dummy: Medium firms
After-holiday dummy: large firms
Panel B: Regression results for portfolios sorted by institutional holding. Dep var = value-weighted return
After-holiday dummy: Low inst. Holding
After-holiday dummy: Med inst. Holding
After-holiday dummy: High inst. holding
Full sample
VWRET-RF
Exclude September
VWRET-RF
0.005
0.009**
(1.44)
(2.35)
0.001
0.000
(0.60)
(0.04)
-0.000
-0.000
(-0.73)
(-0.63)
Contol variables in all regressions
Yes
Yes
Year fixed effect
Yes
Yes
Clustered by year
Yes
Yes
39