What Happens to Stocks When Investors Sneeze?
Christos Pantzalis a and Erdem Ucar b,*
Abstract: We investigate whether allergy onset, which creates changes in investors’ health, cognitive
functioning, and mood, can also affect stock market outcomes. By using daily pollen counts to measure
allergy onset at different locations, we show that firms located in areas with severe allergy problems
exhibit declines in trading volume and stock returns. Consistent with the view that local investors are
instrumental for stock market outcomes, we show that these results are stronger for firms with a large
local investor base. Moreover, we find a decline in investor demand for firm information, as proxied by
Google search volume, for firms in areas with high pollen counts. This result provides a possible
mechanism for the trading activity results that we show. Finally, we illustrate that our allergy onset
findings can provide the basis for a profitable trading strategy.
a
Department of Finance, College of Business Administration, BSN 3403, University of South Florida,
Tampa, FL 33620, USA. Phone: 813-974-3262. Email: [email protected]
b
California State University Fullerton, Mihaylo College of Business and Economics. SGMH-5113, 800
N. State College Blvd., Fullerton, CA 92831. Email:[email protected]
*
Contact author
Acknowledgements:
The authors would like to acknowledge the support of the American Academy of Allergy, Asthma and
Immunology (AAAAI) that provided us with the National Allergy Bureau (NAB) pollen and mold data.
1
What Happens When Stock Markets Sneeze?
1. Introduction
In contrast to traditional finance theory’s assumption that financial markets are efficient and
investors behave rationally, a growing body of evidence suggests that, in addition to market frictions,
stock market outcomes are affected by factors that are not value-relevant. For example, recent studies
have shown that weather or seasonal factors affect financial outcomes (Saunders (1993), Hirshleifer and
Shumway (2003), Kamstra et al. (2003), and Kamstra et al. (2007) among others) because they create
cognitive biases and mood changes that influence investors’ behavior in financial markets. In this study,
we examine a different and previously overlooked factor that can potentially create changes in investors’
physical health, cognitive abilities, as well as mood, and test whether it affects stock market outcomes. In
particular, in this paper, we utilize total pollen counts from various National Bureau of Allergy (NAB)
stations throughout the U.S as a proxy for the onset of severe allergy effects and investigate whether and
how it can affect investor behavior and stock market outcomes.
Seasonal allergy or allergic rhinitis is a health issue which affects a big portion of the population
in the U.S.1 Recent studies also document an increasing trend in the number of people who are affected by
allergy problems. 2 Pollens associated with allergic rhinitis or seasonal allergies 3 can trigger symptoms of
deterioration in physical health, cognitive functioning, and mood. For example, Marcotte (2014) argues
that allergies can have an effect on fatigue, mood, focus, the speed and accuracy of problem solving and
The American College of Allergy, Asthma & Immunology’s (ACAAI) website (http://acaai.org/news/factsstatistics/allergies) reports that “allergic rhinitis, often called hay fever, is a common condition that causes symptoms
such as sneezing, stuffy nose, runny nose, watery eyes and itching of the nose, eyes or the roof of the mouth. These
nasal allergies are estimated to affect approximately 50 million people in the U.S., and its prevalence is increasing,
affecting as many as 30 % of adults and up to 40 % of children.” A study suggests that pollen counts are expected to
increase by 30% by 2020 and they are expected to double by 2040 due to climate change, warmer temperatures,
early spring and longer allergy season. (http://www.huffingtonpost.com/2013/03/31/2013-allergy-seasoncould_n_2989655.html )
1
2
For example, the following website
“http://www.prweb.com/releases/allergy_drugs_allergens/allergic_rhinitis/prweb3610254.htm” , indicates that the
report by Global Industry Analysts on US allergy drugs market suggest an increase in projected sales of allergy
drugs consistent with a “continued rise in the number of patients suffering from allergies”.
3
We use seasonal allergies, allergic rhinitis, allergy onset, pollen allergy, allergic disorder, and allergy
interchangeably in this paper.
2
reasoning. Similarly, Komarow et al. (2005) report “fatigue, depression, reductions in cognition as
manifestations related to allergy along with inflammatory response.” Fineman (2002) suggests that
allergic rhinitis affects quality of life, work productivity, learning, cognitive functioning, decision
making, and self-perception.
We conjecture that the average investor’s market behavior can deviate significantly from its norm
during high pollen count days when suffering from severe pollen allergy is common. Specifically, we
argue that high pollen counts associated with severe allergy problems can affect market participation and
stock pricing, especially among stocks with a sizeable local investor component. Accordingly, and given
retail investors bias toward local stocks (e.g. Huberman (2001)) we focus on stocks whose pricing is more
likely to exhibit a sizeable local component. Loughran and Schultz (2004) in their study of weather
impact on local trading suggest that NASDAQ firms are smaller and younger (i.e., more recently listed)
than NYSE firms and thus more likely to attract local investors. Thus, in order to clearly identify the local
pricing effects from allergy onsets, we follow Loughran and Schultz (2004) and conduct our main tests on
a sample restricted to NASDAQ firms.
In line with our expectations, we find that allergy problems associated with high pollen levels at
different locations affect local stocks. A firm whose headquarter location suffers from a high pollen level
experiences about a 2.5% decline in daily trading volume compared to other firms located in areas
enjoying a zero pollen count day. This finding is consistent with the notion that high pollen count levels
can cause investor distraction due to fatigue, lower productivity, and lower levels of cognitive
functioning. Furthermore, we find that stock returns decline when firms’ locations experience days with
high local pollen count numbers. We report that the daily stock return of a firm whose headquarter
location suffers from a high pollen level decreases by about 10% when compared to the average firm
whose headquarter location has a zero pollen number day. This finding is consistent with the notion that
allergies may affect local investors’ mood negatively.
3
Next, we show that the stock volume and return results are stronger for firms that are expected to
have a greater local ownership. This finding highlights the role of local investors for stock market
outcomes and suggests that our results emerge through the local investor channel. Moreover, the trading
volume and stock return results remain robust when we control for other factors like time effects, industry
and location effects along with weather and the seasonal affective disorder (SAD) onset effect. We also
devise a trading strategy based on local pollen counts and demonstrate that it yields an annual return of
about 11%. Finally, we provide further evidence in support of our conjecture that high pollen days are
associated with greater investor inattention. Following recent studies (e.g. Da et al. (2011) and Drake et
al. (2012) among others) we use Google search volume to measure investor attention and/or investor
demand for information in recent literature and show that investor attention declines significantly when a
firms’ local investor base is in an area suffering from severe allergy problems.
Our paper contributes to several strands of literature. First, it is related to the literature that
discusses the impact of allergic disorders on peoples’ lives. Burton et al. (2001) summarize related studies
and report that an important portion of the U.S. population “suffers with allergic disorder either
seasonally or perennially.” They state that the condition affects not only physical health 4 but also the
quality of life. For example, Burton et al. (2001) suggest that allergic disorder affects the quality of life
through “generalized systemic manifestations, including fatigue, weakness, etc.” and that there is a
decrease in productivity for workers with allergies in high pollen count days. Similarly, Boesquet et al.
(1994) find that people with this type of allergy problems have lower scores in tests that examine social
functioning, mental health, and energy compared to people without allergic disorder. Marcotte (2014)
reports that seasonal pollen allergies affect an important portion of school age children and finds a
The American Academy of Allergy Asthma &Immunology (AAAAI) state that “seasonal allergic rhinitis,
commonly referred to as hay fever, affects millions of people worldwide. Symptoms include sneezing, stuffiness, a
runny nose and itchiness in your nose, the roof of your mouth, throat, eyes or ears.”
(http://www.aaaai.org/conditions-and-treatments/library/at-a-glance/outdoor-allergens.aspx) Burton et al. (2001)
suggest that the symptoms of the condition which affect physical health “may include itching and irritation of the
nose; copious watery nasal discharge; nasal congestion; paroxysmal sneezing; and itching of the eyes, ears, and
palate.” The Asthma and Allergy Foundation of America (AAFA) reports indirect costs of allergy as missed work or
school, lost productivity, death, etc.” (http://www.aafa.org/display.cfm?id=9&sub=30)
4
4
significant reduction in school performance for children affected by pollen allergies. Komarow et al.
(2005) suggest there is also a negative impact of seasonal allergy on athletic performance. Previous
studies also highlight the negative psychological and mood effects of allergy on people (Marshall et al.
(2002), and Blaiss (2000) among others.) Moreover, medical treatment, such as allergy medicine, can
influence people’s cognitive abilities and productivity. Cockburn et al. (1999) find that workers on allergy
medication have approximately 12% lower productivity compared to those who are not on such
medication. Gilmore et al. (1996) show that workers who take a sedating antihistamine have almost 50%
more risk of on-the-job injury compared to others. Considering the fact that a sizeable fraction of the
population suffers from seasonal allergies, it is reasonable to assume that the average investor also
experiences mood symptoms, a decline in cognitive ability (e.g. inattention), as well as health problems,
during high pollen count days. Therefore, disturbing effects of allergies on investors’ lives are also
expected to have an impact on stock markets and we show this impact through declines in trading volume
and stock return.
Second, our study is also related to a body of literature examining factors that can create changes
in investors’ cognitive skills and/or mood, such as weather or seasonal factors, and how these factors
affect financial outcomes. Saunders (1993) and Hirshleifer (2003) consider sunshine and cloud cover and
find that they affect stock returns suggesting that weather can change mood and thus shift investor
sentiment. Loughran and Schultz (2004) find lower trading volume for stocks of firms located in cities
experiencing severe blizzards. Kamstra et al. (2003 and 2007) demonstrate that seasonal affective
disorder (SAD) influences financial outcomes. Edmans et al. (2007) and Pantzalis and Park (2014)
examine the impact of yet another factor that is not related to firm fundamentals—sport events— and
demonstrate that it can generate changes in investor mood thereby affecting stock market outcomes. We
contribute to this literature by introducing a different, previously overlooked factor— allergy onset— that
is known to affect people’s health condition and mood, and by confirming that it also has an impact on
stock market outcomes.
5
Our paper is also related to the literature dealing with the phenomenon of local bias. Ivkovic and
Weisbenner (2005) and Massa and Simonov (2006) suggest that retail investors’ propensity to invest
excessively in local stocks can be attributed to their information advantage. Others have argued that the
local bias phenomenon could be traced to familiarity (Huberman (2001)) and social networking effects
(Hong, Kubik, and Stein (2004, 2005)). Consistent with the notion that local bias causes stocks from the
same geographic area to share a sizeable local pricing component, Pirinsky and Wang (2006) find that
there is a strong comovement in returns of firms whose headquarters share the same location. Hong,
Kubik and Stein (2008) demonstrate that the stock price consequences of local bias are stronger in areas
with relatively few firms per capita. Consistent with this literature, our results imply that local bias,
reflected in disproportionately large local ownership, can be a mechanism that enables localized allergy
onsets that affect health, cognitive functioning, and mood of local investors to have an influence on stock
market outcomes of local firms.
In a recent study closely related to this paper, McTier et al. (2013) study the impact of influenza
on the U.S. stock market. Using weekly flu incidences from the mid-Atlantic region to proxy New York
City area influenza rates, they demonstrate that influenza has an impact on institutional investors and
market-makers. Our paper is similar to theirs in that both papers analyze the impact of a health related
factor on stock market outcomes. Yet, the two papers are also different in two crucial respects. One
important difference between our paper and theirs is that McTier et al (2013) use a regional health
measure as a New York City proxy and focus on NYSE stocks, whereas our paper uses a finer-level (i.e.
city-level) geographic measure of a local health factor and examines its impact on stock market outcomes
of both firms with a sizeable expected local investor component (NASDAQ firms) as well as other firms.
Thus, the focus of our investigation is on local trading and therefore we utilize a more direct measure of
local investors’ health conditions. Another important difference is that McTier et al. (2013) fail to show
the relation between health factors and local trading, which is one of our study’s main contributions. To
our knowledge, our paper is the first that shows the local trading impact of a factor that affects local
investors’ physical health and cognitive abilities. In order to provide some evidence beyond the local
6
allergy effects that was the focus of our study and in order to provide some comparison to the method
used in McTier et al. (2013), we also test and confirm that high pollen counts in the New York City area
affect NYSE aggregate trading.
2. Data, Sample Selection, and Summary Statistics
Prior literature suggests that local bias is more pronounced for NASDAQ firms. For example,
Loughran and Schultz (2004) suggest that the impact of locality is more important for NASDAQ firms
and local investors play a bigger role in NASDAQ stocks because NASDAQ firms are smaller and started
public trading more recently compared to other firms. Given that our study’s focus is on effects that are
manifested through local investor trading, we use a sample selection method similar to Loughran and
Schultz (2004), and our main sample includes daily observations of NASDAQ firms from the CRSP
dataset. We exclude the observations of firms if their stock price is less than $3 for a given day in order to
avoid including stocks that are illiquid and traded infrequently. We also require firms to have location
information in COMPUSTAT. We use daily pollen count data from 28 pollen count stations located in
cities throughout the U.S. 5 for the years 2003 through 2012 from the National Allergy Bureau (NAB) of
the American Academy of Allergy, Asthma and Immunology (AAAAI) 6. This dataset includes detailed
information on tree, weed, and grass pollens and total pollen count, i.e. the number of grains of plant
pollen per cubic meter of air in a 24-hour period.7 We adopt a conservative approach in assigning pollen
counts to firms by matching firm headquarter locations with locations of pollen count stations. In
particular, we assign pollen count information from a pollen station to firms located within a 25 mile
radius from the particular pollen count station and exclude observations by firms located outside the 25
5
We have the daily local pollen count information for 28 different US cities. These are New York (NY), Atlanta
(GA), Seattle (WA), San Jose (CA), Tampa (FL), San Antonio (TX), Charlotte (NC), Pittsburgh (PA), Louisville
(KY), Austin (TX), St. Louis (MO), Baltimore (MD), Bellevue (NE), Omaha (NE), Centennial (CO), Dayton (OH),
Salt Lake City (UT), Eugene (OR), Greenville (SC), Little Rock (AR), Madison (WI), Rochester (NY), Sarasota
(FL), Reno (NV), Twin Falls (ID), Springfield (NJ), Waterbury (CT), St. Claire Shores (MI).
6
We thank the National Allergy Bureau (NAB) and the American Academy of Allergy, Asthma and Immunology
(AAAAI).
7
http://www.aafa.org/display.cfm?id=9&sub=19&cont=264
7
mile radius. After matching observations of daily stock information with daily pollen information, our
main sample includes 901,781 daily firm observations for the years spanning the 2003-2012 period.8
Panel A of Table 1 demonstrates summary statistics for our main sample of NASDAQ firms.
Average daily return is about 0.10% and average daily dollar volume is $23.7 million. Average market
value for our observations is $3.5 billion. Average daily total pollen count is 165 grains of plant pollen
per cubic meter whereas its median value is 28 grains of plant pollen per cubic meter. Prior literature
suggests that there is an impact of SAD effects on stock markets (Kamstra et al. (2003) and (2007) among
others). We also report summary statistics for the SAD Onset variable, which we use in some of our tests.
This variable is based on Kamstra et al. (2007) and extracted from Mark Kamstra's website.9 In Panel B,
we report summary statistics of firm and stock variables for all NASDAQ firms with available
observations for our sample period. In other words, Panel B includes NASDAQ firms in areas without
pollen count information or located within more than 25 miles to pollen count stations as well as our main
sample. Similarly, Panel C and Panel D display summary statistics for the different sub-samples used in
additional tests later in the paper. In particular, Panels C reports statistics for firms within 10 miles of
pollen count stations and Panel D provides statistics for firms within 50 miles of pollen count stations.
The sub-samples in Panels B, C and D have average stock returns that are very similar to the one in the
main sample. Dollar volume and market value is somewhat lower for all NASDAQ firms (in Panel B)
than our main sample (Panel A). Considering the notion that our main sample includes observations of
firms located near pollen count stations in some big cities such as New York, San Jose, San Antonio,
Seattle and etc., it is not surprising that our main sample’s average firm is larger; bigger firms typically
can be found in big metropolitan areas. A look at Panels C and D also lends support to this point; firm
size and dollar volume increases as distance to the pollen count cities decreases.
[ Insert Table 1 about here ]
8
This number is the number of observations that has non-missing observations for our main regression variables in
our main tests. Note that, for some tests, the number of observations varies due to missing information for some
regression variables.
9
http://www.markkamstra.com
8
3. Empirical Results: Trading Volume
3.1. Trading Volume
In order to see whether allergy onset reflected in high pollen numbers has any distracting effects
on local investors’ trading activities, we examine the impact of local pollen numbers on dollar volume.
We posit that there will be a lower level of trading volume when pollen levels are high and use the
following regression model for dollar volume tests:
DolVol =β0+ β1Pollen+ β2 AvgDolVol
+ε
(1)
where DolVol is daily dollar volume of a stock for a given day and it is defined as price multiplied by
volume number10. AvgDolVol represents the average dollar volume for a given day t, and we calculate it
as the average daily dollar volume for the (-41,-11) window relative to day t. Pollen is the total number of
daily local pollen count divided by 1,000.11 In the empirical tests, we also include indicator variables for
year, month, and weekday fixed effects as control variables. In our main regressions, year dummy
variables help to control time varying patterns whereas month dummy variables help to control for
seasonal and time of the year factors. Weekday dummy variables control for certain anomalies, such as
the Monday or Friday effects. Also note that our regressions are pooled multivariate regressions with
robust standard errors using firm-level clustering.
[Insert Table 2 about here ]
The results, shown in Table 2, demonstrate a negative a relation between Pollen and DolVol.
Consistent with our conjecture, our multivariate regressions suggest a decline in trading volume when the
local investor base is experiencing allergy related problems. As expected, previous trading activity
measured by AvgDolVol has a positive relation with current trading volume. After controlling for
AvgDolVol and weekday, month, and year fixed effects, Column 1 in Panel A shows about a 2.5%
10
We appropriately scale NASDAQ volume numbers by dividing two, by following Butler et al. (2005), in order
eliminate any double counting in the empirical tests.
11
We scale daily total pollen numbers by 1000 for better presentation of coefficients for this variable in the return
regressions later in the paper. For consistency, we use the same variable definition in the trading volume regressions
in this section.
9
decline in dollar volume for firms whose headquarter location suffers from severe allergy problems (i.e.
high pollen count) compared to the average firm whose headquarter location has a zero pollen count day.
Next, we analyze whether our results are robust to the inclusion of additional factors. For example, one
may argue that some industries have lower trading volume activity and this might drive the results. We
therefore also test whether the results change if we include industry dummy variables defined based on
Fama-Frenh 48 industry classifications. As shown in Column 2 of Table 2, our results are robust to
industry fixed effects. One may also argue that total pollen count is a proxy for other things associated
with firm location. In order to ensure that our results are indeed driven by pollen counts and not by
location of the firm itself, we also run a regression with location dummies in Column 3. Our results
remain robust after controlling for location fixed effects, supporting the view that the pollen effects we
found are not driven by location-specific characteristics.
In order to ensure that the pollen count effects in our results are not driven by the seasonal
affective disorder (SAD) effect, we include the SAD onset variable, defined as in Kamstra et al. (2007),
into the regression shown in Column 4.12 Our findings show that the pollen count effect still holds. This
supports the notion that it is not a proxy for the SAD onset effect. Another possibility is that pollen count
is a proxy for weather, and that weather effects rather than pollen could be the main driver of our results.
In order to investigate the validity of this argument, we add a sky cover variable that measures local cloud
coverage the empirical tests in the last column.13 The results shown in Column 5 confirm that our pollen
effects on trading volume remain robust after controlling for weather effects. In Panel B we repeat our
tests from Panel A after replacing the continuous pollen count variable with an indicator variable. In
particular, when we use Very high, which is an indicator variable of very high pollen numbers based on
12
Kamstra et al. (2003) reports that seasonal affective disorder (SAD) is a medical condition that affects a big
number of people during short days in the fall and especially in the winter. Kamstra et al. (2003) argue that this
condition leads to depression as well as risk aversion. Consistent with this view, Kamstra et al. (2003) and Kamstra
(2007) show that SAD can affect stock returns. They suggest that that there is a variation in investor risk aversion
across the seasons associated with physiological changes across the seasons and this seasonal variation can help to
explain returns.
13
Prior literature finds that weather has an impact on equity markets (Saunders (1993), Hirshleifer and Schumway
(2003), and Loughran and Schultz (2004) among others). Similar to Loughran and Schultz (2004), we use weather
data that shows cloud cover for the U.S. airports closest to our pollen count stations. This dataset is in the
International Surface Weather Observations Data format and uses U.S. METAR sky cover variable.
10
the NAB’s scale, instead of Pollen, we find very similar trading volume results. In sum, our analysis of
the impact of local allergy onset on dollar volume provides results that support the view that allergy
related symptoms that affect investors’ physical health as well as cognitive abilities may cause local
investors to trade less than in normal times.
3.2. Local Trading: Additional Tests
In this section, we run additional tests as robustness checks and in order to further explore the
impact of allergy related effects on trading by using daily trading volume and share turnover instead of
the daily dollar volume as our measures of the extent of trading activity. We first analyze the relation
between total local pollen count, Pollen, and daily stock volume, Vol, by using a regression setting similar
to the one shown in regression model (1). Panel A of Table 3 demonstrates the impact of local pollen
count on daily stock volume after controlling for AvgVol, the average stock volume for a given day t, and
we calculate it as the average daily stock volume for the (-41,-11) window relative to day t. As expected,
AvgVol has a positive relation with daily stock volume. On the other hand, Pollen has a negative relation
with stock volume consistent with our conjecture and earlier results.
[ Insert Table 3 about here ]
We also obtain similar results when we control for industry and location effects in Columns 2 and
3, respectively. In the last two columns, we control for SAD onset and sky cover and find that the pollen
effect results still hold. Overall, the results shown in Panel A support our earlier findings and suggest that
allergy related effects can cause a decline in local trading. Next, as an additional robustness check, we
investigate the effect of Pollen, on local trading measured by the share turnover, Turnover.14 We employ a
regression setting similar to the regression model (1) and control for AvgTurnover (which is calculated as
the average Turnover for the (-41,-11) window) as well as other variables. The results shown in Panel B
are consistent with our earlier findings. As expected, AvgTurnover has a positive effect on Turnover.
More importantly, Pollen has a negative effect on Turnover even after controlling for additional factors,
14
Turnover is calculated as by the dividing the ratio of stock volume to number of shares outstanding and
multiplying by 1,000.
11
such as industry and location fixed effects as well as the effects of SAD onset and sky cover. In sum, our
findings in Table 2 and Table 3 suggest that local investors suffering from seasonal allergy related
problems trade less.
4. Stock Return Results
4.1. Stock Return Tests
Previous sections show that allergy related symptoms lead to lower levels of local trading. Next,
we examine whether the negative health effects associated with pollen allergy, and in particular mood
effects reported in the literature (Mercotte (2014), Komarow et al. (2005) and Fineman (2002)) can also
affect stock returns. There is ample evidence in the literature that factors affecting investor attention
and/or mood can have an impact on stock returns (e.g., Edmans et al. (2007), Pantzalis and Ucar (2014)
among others). Accordingly, we analyze stock returns around days associated with severe pollen counts in
a multivariate test setting using the following regression model:
Return =β0+ β1 Pollen+ β2 MarketReturn
+ε
(2)
where Return is daily stock return of a firm for a given day t. MarketReturn is the market return for a
given day t. We use the value-weighted market index return of CRSP in the first panel and the equalweighted market index return of CRSP in the second panel of Table 4. Similar to the earlier tests, Pollen
represents daily local total pollen numbers.
[ Insert Table 4 about here ]
The regression results in the first column of Panel A of Table 4 show a negative a relation
between Pollen and stock returns after accounting for time (weekday, month and year) fixed effects. The
effect of pollen on returns seems to be not just statistically, but also economically significant: a firm
located in an area that suffers from a high pollen levels experiences a 10% decline in daily stock return
compared to the average firm whose headquarter location enjoys a zero pollen count day. In the following
columns we investigate whether the results are robust to additional factors introduced in our earlier tests.
Specifically, we include industry fixed effects in the second column, location fixed effects in the third
12
column, the SAD onset variable in the fourth column, and the local cloud coverage in the fifth column.
The negative effect of Pollen on stock returns remains strong throughout these tests. This finding suggests
that severe allergic symptoms affecting local investor bases are the main driver of the decline in stock
returns. Moreover, we obtain very similar results when we employ an equally-weighted market return in
our tests, as shown in Panel B. When we use an indicator (Very high) instead of a continuous (Pollen)
variable for pollen, we find very similar stock return results. We provide these results in Panel C. Overall,
the negative relation between allergy onset and local stock returns remains robust after several different
tests used in this table.
4.2. Trading Portfolio Returns
In this section we analyze the profitability of a portfolio trading strategy based on the total pollen
count effect. Specifically, we examine returns to an equal-weighted portfolio that consists of short
positions in firms that are located in areas with very high pollen levels (i.e., where the previously defined
Very high indicator variable takes the value of one) and long positions in firms that are located in areas
with other pollen levels. We hold these companies in the portfolio during all trading days for which we
have pollen data available. 15 We analyze the daily abnormal return performance by employing the Carhart
four-factor model. Following Hirshleifer et al. (2009), DellaVigna and Pollet (2009), and Pantzalis and
Ucar (2014), the standard errors are corrected for heteroskedasticity and autocorrelation using the NeweyWest estimator with twelve lags.
[ Insert Table 5 about here ]
Table 5 shows the aforementioned trading portfolio has a statistically significant daily alpha value
of 0.00044%. In other words, it outperforms on a risk-adjusted basis by 4.4 basis points per day. This
alpha value suggests that our portfolio earns an abnormal return of almost 1% in a month or about an
abnormal return of 11% per year. Overall, the trading portfolio results highlight the economic importance
15
If long or short portfolio observations are missing for a given day, we use market returns for the missing part of
the portfolio.
13
of our results and suggest that the impact of local health, as proxied by pollen allergy, can provide a
blueprint for potentially profitable trading strategies.
5. Additional Tests
5.1. Lagged allergy onset effect
Arguably, the allergy onset effect we uncover in our previous tests could also affect stock
investors behavior and market outcomes on the next day of the pollen count. In order to investigate this
point, we examine the impact of lagged pollen levels on stock market outcomes. In Table 6, we repeat the
stock return tests by using a one-day lagged pollen level variable. In particular, we repeat the tests
reported in Table 4 by using the lagged Pollen variable in Panel A of Table 6 and by using the lagged
Very high indicator in Panel B of Table 6. The results support the notion that allergy onset effects on
stock returns last at least one more day after the pollen count information is collected. We also repeat the
dollar volume analysis reported in Table 3 by using lagged Pollen variable in Panel A of Table 7 and by
using lagged Very high in Panel B of Table 7. The results in table 7 are similar to those reported in Table
3 and support the notion that allergy onset on a particular day are associated with lower trading the next
day as well. Overall, Tables 6 and 7 together with our prior evidence suggest that allergy onset’s impact
on stock market outcomes is not restricted to a concurrent effect but also includes a more lasting effect
extending at least one more trading day.
[ Insert Table 6 about here ]
[ Insert Table 7 about here ]
5.2. Other Exchanges and Investor Information Demand and Attention
In this section, we turn our attention to the stocks of firms that are traded on other exchanges, for
which local investors are supposed to play a relatively smaller role. In a recent paper, McTier et al. (2013)
suggest that higher levels of regional flu cases have an impact on NYSE stocks’ trading volume and
volatility. They derive this result by using weekly flu levels for the mid-Atlantic region as a proxy for
New York City flu levels. In addition, they find no relation between local flu levels—proxied by regional
14
data—and stock returns. In contrast to McTier et al. (2013), we use a more precise local measure—daily
local pollen count—to gauge local health effects. In particular, we use city-level pollen numbers and
examine whether local health related factors have an impact on stock trading. In order to analyze results
from a method comparable with that of McTier et al. (2013), we investigate whether aggregate level
NYSE stock trading is affected by local allergy onset proxied by New York City pollen counts. We use a
regression setting similar to the regression model (1) and present the results in Table 8.
[ Insert Table 8 about here ]
In Table 8, DolVol measures the average daily dollar volume of NYSE stocks for a given day t.
AvgDolVol is calculated as the average value of DolVol for the (-41,-11) window relative to day t. Pollen
shows the local total pollen count in New York City for a given day. We also control for year, month, and
weekday effects. The results shown in Column 1 demonstrate that NYSE trading volume is negatively
related with New York pollen counts. A one-standard deviation increase in total pollen count in New
York City leads to about a 0.093 standard deviation decline in average concurrent trading volume in the
NYSE. After we control for the seasonal affective disorder proxied by the SAD onset variable in Column
2, our finding becomes even stronger. Furthermore, as shown in the last column, our results also remain
robust when we control for weather effects proxied by cloud coverage of New York City. These results
imply that, in addition to the local effects demonstrated earlier, allergy induced by high pollen counts in
New York City can have an impact on aggregate level of NYSE trading.
Next we turn our attention to a potential mechanism that may be responsible for the impact of
higher pollen levels on investor behavior. Specifically, we conjecture that high pollen counts can cause
investors to become less diligent and attentive in terms of seeking value-relevant information about the
firms they trade in. Recent studies (i.e., Da et al. (2011) and Drake et al. (2012)) suggest Google Search
Volume accurately captures investors’ demand for information. Thus, if demand for value-relevant
information is a good proxy for investor assiduousness, higher (lower) levels of Google Search Volume
for stocks should be indicative of higher (lower) levels of investor attention and diligence. We conjecture
that investors suffering from severe allergy related cognitive problems will exhibit lower demand for
15
value relevant information. To test for the validity of this conjecture, we follow Drake et al. (2011) and
use the Google Search Volume Index (SVI) dataset, which we access on Michael Drake’s website.16
Michael Drake’s website provides Google SVI variable for the S&P 500 firms for a limited period of
time.17 After matching this dataset with our main sample, we use a subsample of NASDAQ firm
observations for the period 2005 to 2008.
For the sake of comparison, we also match the Google SVI variable with firms traded on any
stock exchange and included in the CRSP dataset for the same period. By using a structure similar to
Drake et al. (2012), we also create an abnormal Google SVI variable. We define abnormal Google SVI as
the SVI for a given day in week t minus the average SVI for the same weekday over the past weeks
between the week t-1 and the week t-4, divided by the average SVI for the same weekday over the past
weeks between the week t-1 and the week t-4. In Table 9, we report Google Search Volume Index
(Google SVI) and abnormal Google SVI values for NASDAQ firms and also all the CRSP firms traded on
any stock exchange with the available SVI data based on daily local pollen status. In particular, we
examine the SVI values for the firms whose headquarter locations are associated with very high daily
pollen levels and all the other firms whose headquarter location is associated with other, lower levels of
daily pollen counts. (i.e., for groups for which Very high is equal to one or equal to zero).
[ Insert Table 9 about here ]
Table 9 displays average values of Google SVI and abnormal SVI for firms located in and out of
areas with very high pollen numbers, as well as differences between the means of these two groups and
the corresponding p-values. Panel A shows our results for firms from the main sample (i.e. the sample of
NASDAQ firms) and Panel B shows our findings for the sample drawn using firms from all exchanges.
As Panel A displays, there are significant differences between NASDAQ firms located in high pollen
16
http://byuaccounting.net/drake/ProgramsData1.php
Drake’s website provides Google SVI for the years between 2005 and 2008. Their dataset was created by feeding
the ticker symbols of S&P 500 firms into the Google Trends website (http://www.google.com/trends). SVI values of
0 means that that users’ propensity to search for that tickers symbol on that particular day wasn’t significant enough
to be captured by the Google Trends tool. See http://www.google.com/intl/en/trends/about.html#7 for more general
information about the Google SVI data.
17
16
areas and NASDAQ firms located outside high pollen areas in terms of both Google SVI and abnormal
Google SVI. Firms in high pollen areas are associated with significantly lower demand for value-relevant
information than those located in areas not experiencing an allergy onset. It is notable that these findings
are highly statistically significant although we only have a small subsample of firms with Google SVI due
to data limitations. Panel B, where the sample is not restricted to NASDAQ firms, also demonstrates
similar, albeit weaker, results, with statistically significant Google SVI and abnormal Google SVI
differences between firms in and out of allergy prone areas. Considering the point that local investors play
a bigger role for NASDAQ firms (Loughran and Schultz, 2004), finding lower level of statistical
significance for all firms is somewhat expected. In sum, Table 9 provides support for the notion that a
potential mechanism for the allergy related effects reported in the earlier tests can be the adverse impact
of allergy related problems on investor attention and diligence. Predictably, this mechanism is more
pronounced for the firms are expected have a bigger local investor base.
6. Role of Local Stock Ownership
6.1. Distance to Source of Allergy Onset
The previous tests demonstrate a decline in trading volume and stock returns for firms when local
investor bases experience severe seasonal allergy problems. Since local pollen counts affect local
investors’ health, one may expect that the channel through which local pollen count affects stock market
outcomes is the local investor channel. Consistent with the prior literature that finds more localized
trading for NASDAQ stocks, our main empirical tests focus on the NASDAQ firms in order to analyze
the impact of local investors’ health on stock market outcomes for local firms. In the next series of tests
we attempt to shed additional light on the importance of local investors. First, we focus on how the
impact of allergy onset changes as the distance between pollen counts and firm location changes. Recall
that, in the empirical tests heretofore, we assign pollen count information from a pollen station to firms
located within a 25 mile radius from the particular pollen count station. Now, we also look at what
happens to the impact of allergy onset on stock market outcomes when we assign pollen count
17
information from different distances. In particular, we examine firms that are located within a longer (50
mile) or shorter (10 mile) radius from the particular pollen count station. By doing so, we can compare
how the allergy onset effect changes as the distance to the area pollen count station changes. In Table 10
we show the test results for the 25 mile distance (which are also reported in Table 4 but included here for
the sake of comparison) along with the results we obtain when we repeat the main stock return tests for
the 10 mile- and 50 mile distances. Panel A uses Pollen variable and Panel B uses the Very high indicator
variable. The results in both panels indicate that the impact of allergy onset on stock returns decreases
with distance to the pollen count station. Given local bias, i.e. investors’ preference for local stocks, this
table highlights the role of local investors on the allergy onset effect.
[ Insert Table 10 about here ]
Similarly, we investigate how the impact of severe allergy problems on trading volume changes
with distance to pollen count stations. In particular, we repeat the main dollar volume tests reported in
Table 2 by using difference distances—10 miles and 50 miles and report them along with the 25 mile
distance results (shown in Table 2 but included here for comparison sake) in Table 11. Panel A uses the
Pollen variable and Panel B uses the Very high variable. Both panels demonstrate that as the distance to
source of severe allergy problems—proxied by the location of the pollen count station—decreases, the
impact of allergy onset on dollar volume increases. Moreover, when we control for the other factors
previously introduced in Columns 2-5 of Table 2, we find very similar results. Overall, both Table 10 and
Table 11 highlight the importance of a sizeable local investor base for the allergy onset effects that we
show in this paper by presenting stronger findings when the number of local investors likely to be
affected by allergy onset is higher.
[ Insert Table 11 about here ]
6.2. Other Proxies for the Role of Local Stock Ownership: Stock Return Tests
In this section, we employ several different methods to assign firms in high and low local
ownership subsamples used to analyze and compare allergy onsets effects on stock market outcomes. We
first focus on the role of local stock ownership on the relation between allergy onset and stock returns.
18
Table 12 shows results of stock return tests, similar to those reported in Column 1 of Table 4, but
performed separately using local stock ownership subsamples formed based on different methods. This
table only presents the coefficients of Pollen and Very High for the corresponding regressions that also
include the market return (the value-weighted market return) as well as control variables of year, month,
and weekday dummy variables. The first tests in Table 12 examine the effects of allergy onset on stock
returns separately estimated for subsamples of firms headquartered in areas with few and many firms per
capita, respectively. The rationale for this classification method is based on Hong et al (2008) whose
findings suggest that stock price consequences of local bias are more pronounced in areas with relatively
fewer firms per capita, due to an “only game in town” effect. Their findings imply that there will be
greater (lower) levels of local ownership when firms are located in areas with few (many) firms per
capita. Accordingly, we use Census data to construct a variable that captures this “only game in town”
effect measured by the county-level ratio of the number of firms headquartered in the county divided by
the county population. Next, we divide our sample into two subsamples based on the median value of this
variable and repeat the main stock return tests separately for the subsample that includes firms located in
areas with a few firms per capita (Column 1) and the subsample that includes firms located in areas with
many firms per capita (Column 2). The former subsample contains firms that are expected to have a
greater local ownership, whereas the latter contains firms that are expected to have a lower local
ownership. Column 1 shows that the coefficient of Pollen is statistically significant and its magnitude is
almost twice as large as that of the coefficient from the main stock return findings presented earlier in the
paper. On the other hand, Column 2 reports that the coefficient of Pollen is statistically insignificant. We
have very similar results when we use Very High in Panel B. When we compare the magnitudes of the
Pollen and Very High coefficients across the subsamples of different levels of local ownership, we find
that the difference in the allergy onset effects is statistically significant. These findings support the notion
that the impact of allergy related problems on stock returns is stronger when local investors’ role in stock
pricing is more prominent.
[ Insert Table 12 about here ]
19
Previous studies (i.e. Hong et al. (2004) and Brown et al. (2008)) suggest that income and
education are important in determining retail investors’ stock market participation and stock ownership.
Consequently, local stock ownership is expected to increase with local income and education levels.
Therefore, we use these measures as proxies for the likelihood that local stock ownership is sizeable.
First, in Columns 3 and 4, we use local income as a proxy for local stock ownership. We identify the
county median household income for each year provided by the Census data and use it as the local income
measure for firms with headquarters in the particular county.
18
Next, we divide our sample into two
subsamples based on the median value of local income and repeat the main stock return tests for these two
subsamples. Firms located in areas with higher local income levels are expected to have a greater local
ownership whereas firms headquartered in areas with lower income are expected to have a lower local
ownership. Column 3 reports that, using the high county income subsample, the coefficient of Pollen is
statistically significant and almost twice as large in magnitude as the one from the main findings reported
earlier in the paper. In contrast to the results from Column 3, the results obtained from the low local
ownership subsample (Column 4) indicate a statistically insignificant coefficient for Pollen. When we use
the Very High indicator variable in Panel B, we find very similar results too. Moreover, there is a
statistically significant difference in Pollen and Very High coefficients between subsamples of firms with
different levels of local ownership proxied by local income.
Next, we use local education as a proxy for local stock ownership. We use the proportion of the
population holding college degrees in a given county provided by the Census data to measure local
education. In Columns 5 and 6, we divide our sample into two based on the median value of local income
and re-run the main stock return regressions. The tests reported in Column 5 use the subsample of firms
headquartered in counties with higher levels education (i.e., firms with a greater local stock ownership)
whereas those reported in Column 6 use the subsample of firms headquartered in counties with lower
levels of education (i.e., firms with a lower local stock ownership). The results of Columns 5 and 6 are
18
We use interpolation to generate income and education data for the years without available Census information.
20
very similar to those shown in the previous columns and also support the notion that the stock return
impact of severe allergy problems is more pronounced for firms with more sizeable local investor bases.
The next test examines the differences between firms headquartered in very large metropolitan
areas and firms headquartered in other smaller and less urban areas. Prior literature demonstrates the
differences between firms located in large metropolitan areas and those located in other areas. Access to
information about firms located in bigger cities is better compared to those in smaller cities or other areas
(Loughran and Schultz, 2005, Loughran, 2008). The only-game-in-town effect found by Hong, Kubik,
and Stein (2008) is expected to be stronger in less densely populated areas, where the per capita number
of local firms tends to be small, compared to the densely populated areas, where the per capita number of
firms tends to be larger. Thus, based on the aforementioned studies, firms in large metropolitan areas are
expected to have relatively smaller local ownership compared to those located in other areas. In Columns
7 and 8, we investigate the differences in the allergy onset between firms located in the three largest
metropolitan areas (New York, Los Angeles, and Chicago) and firms located in all other areas. Once
again, the results indicate that allergy effects are more pronounced for firms with more sizeable local
ownership and insignificant for firms with smaller levels local ownership. Moreover, we find similar
results when we repeat the same tests after controlling other factors as we first did in Table 4. Overall, the
results in Table 12 demonstrate that the stock return impact of severe allergy problems becomes even
more pronounced when the local component of stock pricing becomes larger.
6.3. Other Proxies for the Role of Local Stock Ownership: Trading Volume Tests
In order to examine whether local ownership also affects the impact of allergy onset on trading
activity, we repeat the above subsample analysis for dollar volume and report the results in Table 13.
Table 13 presents results of dollar volume tests using the model in Column 1 of Table 2 for different local
stock ownership subsamples formed based on the previously described methods. This table only presents
the coefficients of Pollen and Very high for the corresponding regressions that also include the average
dollar volume as well as control variables of year, month, and weekday dummy variables. Columns 1 and
2 re-examine our dollar volume tests for firms in high and low local ownership subsamples respectively,
21
formed based on the number of local firms per capita measure. In Column 1, Pollen has a higher
coefficient value and a more pronounced impact for firms with a greater local ownership compared to the
main dollar volume findings reported earlier. On the other hand, in Column2, Pollen has a smaller and
less significant coefficient.
[ Insert Table 13 about here ]
Next, we examine the role of local investors in the relation between trading activity and allergy
onsets by using local income (Columns 3 and 4) and local education (Columns 5 and 6), as a proxy for
local stock ownership, respectively.
The results show that the coefficient of Pollen is statistically
significant for firms largely held by local investors and its magnitude is much stronger than the one
obtained from the main dollar volume regressions presented earlier in the paper. On the other hand,
Pollen does not have a statistically significant impact on trading volume of firms expected to have a
smaller local stock ownership. In addition, Panel A shows statistically significant Pollen coefficients for
local ownership subsamples in Columns 3-6, but the effect is significantly stronger when local ownership
is more pronounced. The last two columns also present somewhat similar findings when we examine the
subsamples of firms located in the large metropolitan and other areas, respectively. When we use Very
High in Panel B, we find similar results. Moreover, when we repeat the same tests after controlling for the
other factors first introduced in Table 2, we find similar results for all the tests in Table 13. Overall, these
tests provide additional support to the earlier findings. Consistent with the stock return findings shown in
Table 12, the results in Table 13 indicate that the decline in dollar volume associated with local investors’
severe allergy problems is significant and more pronounced for firms largely held by local investors. Both
the stock return and dollar volume tests in this section highlight the importance of the local investors for
stock market outcomes.
7. Conclusion
We introduce a new factor, allergy onset, which has a strong influence on both physical health
and cognitive functioning along with possible mood effects, and hypothesize that it affects retail
22
investors’ stock market behavior. We use daily pollen counts to measure the degree of local allergic
symptoms and demonstrate that trading volume declines when investors experience these symptoms. One
potential mechanism driving this finding is that investors’ assiduousness is compromised by allergy
related problems. We provide evidence that, indeed, investors in areas with very high pollen counts
display signs of lower attention and diligence as proxied by a lower demand for value-relevant
information about their local investments. We also find a decline in stock returns of firms with a large
local retail ownership during high pollen days, consistent with the notion that local investors who suffer
from allergy symptoms experience negative mood states. In addition to introducing a previously
overlooked factor that has important stock market outcomes’ implications, our paper also contributes to
the local bias literature by highlighting that these effects are particularly strong among local stocks for
which local investors’ preference is strongest.
23
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25
Table 1 - Summary Statistics
This table shows summary statistics (number of observations, mean, median, standard deviation, 25th and 75th
percentiles). Return is daily stock return represented as in percentages. DolVol is daily dollar volume and
represented in millions. Market value is market value of firms. Pollen is the total local pollen count determined by
the NAB's Pollen Count stations. The SAD Onset variable is based on Kamstra et al. (2007) and it is from Mark
Kamstra's website. Panel A demonstrates summary statistics of for the main regression sample. Panel B reports
summary statistics for all the NASDAQ firm observations on the main sample dates. Panel C and Panel D present
summary statistics of the firms within different distances to pollen count used in Table 10. Panel C (D) reports
statistics for firms within 10 (50) miles of pollen count stations.
N
Panel A: Main Sample
Return (%)
901,781
DolVol (mil)
901,781
Market value ($ mil)
901,781
Pollen count
901,781
SAD Onset
901,781
Panel B: All NASDAQ firms
Return (%)
5,121,628
DolVol (mil)
5,121,628
Market value ($ mil) 5,121,628
Panel C: 10 miles to pollen count
Return (%)
476,849
DolVol (mil)
476,849
Market value ($ mil)
476,849
Panel D: 50 miles to pollen count
Return (%)
1,215,018
DolVol (mil)
1,215,018
Market value ($ mil) 1,215,018
Mean
Median
Std. dev.
p25
p75
0.0099%
23.721
3,513.47
165.1185
0.01
0.0000%
1.141
368.444
28
0.003
3.5308%
165.790
19,272.15
647.258
0.219
-1.4085%
0.190
147.645
8
-0.094
1.4402%
5.732
1,021.25
97
0.123
0.0099%
8.927
1,426.29
0.0000%
0.464
239.627
3.6240%
69.214
9,178.22
-1.3793%
0.056
90.149
1.3954%
2.652
648.973
0.0010%
36.904
3,513.47
0.0000%
1.543
368.444
3.5473%
224.30
19,272.15
-1.4232%
0.262
147.645
1.4659%
8.722
1,021.25
0.0098%
18.870
2,832.15
0.0000%
0.895
327.40
3.6376%
143.453
16,688.13
-1.3910%
0.133
128.055
1.4144%
4.710
903.54
26
Table 2 – Dollar Volume Tests
DolVol is daily dollar volume of a stock for a given day. AvgDolVol is equal to the average dollar volume for a given day t and it is calculated by calculating
the average dollar daily volume from the (-41,-11) window relative to day t. In Panel A, Pollen shows daily local total pollen count divided by 1,000 for a given
day. In Panel B, Very high is an indicator variable which takes value of 1 if daily pollen count is considered as very high for a given day based on the scale
determined by the National Allergy Bureau (NAB) and zero otherwise. This table also includes control variables of year, month, and weekday dummy variables.
We use Fama-French 48 industry definitions in determining industry dummy variables. This table also has the SAD onset variable of Kamstra et al. (2007) in
some regressions. The last test includes local cloud coverage to proxy weather effects. Regressions have robust standard errors using firm-level clustering.
Robust p-values are in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A
Dependent Variable: DolVol
Pollen
AvgDolVol
Weekday, month,
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-345,672.0894
(0.003)***
0.9716
(0.000)***
(2)
-352,029.2462
(0.003)***
0.9706
(0.000)***
(3)
-347,103.7744
(0.003)***
0.9709
(0.000)***
(4)
-340,010.1819
(0.003)***
0.9716
(0.000)***
(5)
-349,925.8241
(0.003)***
0.9716
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5969467
(0.000)***
901781
0.805
4747928
(0.001)***
900166
0.805
5510089
(0.001)***
901781
0.805
6382074
(0.000)***
901781
0.805
Yes
6198372
(0.001)***
899762
0.805
27
Panel B
Dependent Variable: DolVol
VeryHigh
AvgDolVol
Weekday, month, year fixed effects
Industry fixed effects
Location fixed effects
Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-1342774
(0.032)**
0.9715
(0.000)***
(2)
-1328899
(0.034)**
0.9706
(0.000)***
(3)
-1224186
(0.042)**
0.9709
(0.000)***
(4)
-1295046
(0.036)**
0.9716
(0.000)***
(5)
-1360735
(0.031)**
0.9716
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5895483.7635
(0.001)***
901781
0.805
4682824.0585
(0.001)***
900166
0.805
5429535.4159
(0.001)***
901781
0.805
6307258.6942
(0.000)***
901781
0.805
Yes
6123045.2426
(0.001)***
899762
0.805
28
Table 3 – Other Trading Volume Tests
In Panel A,Vol is daily volume of a stock for a given day and AvgVol is equal to the average volume for a given day t and it is calculated by calculating the
average daily volume from the (-41,-11) window relative to day t. In Panel B, Turnover is the ratio of daily stock volume to number of shares outstanding
divided by 1,000 and AvgTurnover is defined as the average Turnover from the (-41,-11) window relative to day t. Pollen shows daily local total pollen count
divided by 1,000 for a given day. This table also includes control variables of year, month, and weekday dummy variables. We use Fama-French 48 industry
definitions in determining industry dummy variables. This table also has the SAD onset variable of Kamstra et al. (2007) in some regressions. The last test
includes local cloud coverage to proxy weather effects. Regressions have robust standard errors using firm-level clustering. Robust p-values are in parentheses
(* significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A
Dependent Variable: Vol
Pollen
AvgVol
Weekday, month,
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-8,521.64
(0.004)***
0.942
(0.000)***
(2)
-8,797.77
(0.004)***
0.9396
(0.000)***
(3)
-9,145.81
(0.002)***
0.9397
(0.000)***
(4)
-8,358.06
(0.005)***
0.942
(0.000)***
(5)
-8,796.21
(0.003)***
0.9418
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
171740
(0.000)***
901781
0.748
119854
(0.000)***
900166
0.748
142812
(0.000)***
901781
0.748
183675
(0.000)***
901781
0.748
Yes
188719
(0.000)***
899762
0.748
29
Panel B
Dependent Variable: Turnover
Pollen
AvgTurnover
Weekday, month, and year fixed
effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Number of observations
Adjusted R-squared
(1)
(2)
(3)
(4)
(5)
-0.0591
(0.011)**
0.6564
(0.000)***
-0.0613
(0.011)**
0.6353
(0.000)***
-0.0675
(0.002)***
0.6378
(0.000)***
-0.0587
(0.011)**
0.6564
(0.000)***
-0.0642
(0.006)***
0.6534
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
2.6134
(0.000)***
901781
0.127
1.1358
(0.000)***
900166
0.129
2.2020
(0.000)***
901781
0.129
2.6406
(0.000)***
901781
0.127
Yes
2.9465
(0.000)***
899762
0.127
30
Table 4 – Stock Return Tests
Return is daily stock return for a given day. In Panels A and B, Pollen shows daily local total pollen count divided by 1,000 for a given day. In Panel C, Very
high is an indicator variable which takes value of 1 if daily pollen count is considered as very high for a given day based on the scale determined by the National
Allergy Bureau (NAB) and zero otherwise. MarketReturn is the market return for a given day t. We use the value-weighted market return in Panels A and C
whereas the equal-weighted market return in Panel B. This regression also includes control variables of year, month, and weekday dummy variables. Some tests
include industry dummy variables defined based on Fama-Frenh 48 industry classifications. This table also has the SAD onset variable of Kamstra et al. (2007)
in some regressions. The last test includes local cloud coverage to proxy weather effects. Some tests include location dummies defined based on pollen count
locations. The SAD onset variable is defined by following Kamstra et al. (2007). Regressions have robust standard errors using firm-level clustering. Robust pvalues are in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A: Market Return =Value-weighted market return
Dependent variable: Return
Pollen
MarketReturn
Weekday, month,
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-0.0002
(0.001)***
1.0405
(0.000)***
(2)
-0.0002
(0.001)***
1.0411
(0.000)***
(3)
-0.0002
(0.001)***
1.0405
(0.000)***
(4)
-0.0002
(0.001)***
1.0406
(0.000)***
(5)
-0.0002
(0.001)***
1.0404
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.0029
(0.000)***
901781
0.137
0.0010
(0.000)***
900166
0.138
0.0028
(0.000)***
901781
0.137
0.0032
(0.000)***
901781
0.137
Yes
0.0029
(0.000)***
899762
0.137
31
Table 4 cont.
Panel B: Market Return =Equal-weighted market return
Dependent variable: Return
Pollen
MarketReturn
Weekday, month,
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
(2)
(3)
(4)
(5)
-0.0002
(0.002)***
1.1721
(0.000)***
-0.0002
(0.002)***
1.1726
(0.000)***
-0.0002
(0.002)***
1.1721
(0.000)***
-0.0002
(0.002)***
1.172
(0.000)***
-0.0002
(0.002)***
1.1719
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.0003
(0.231)
901781
0.148
-0.0014
(0.000)***
900166
0.148
0.0003
(0.427)
901781
0.148
0.0005
(0.063)*
901781
0.148
Yes
0.0003
(0.211)
899762
0.148
32
Table 4 cont.
Panel C: Market Return =Value-weighted market return
Dependent variable: Return
(1)
VeryHigh
-0.0009
(0.000)***
MarketReturn
1.0405
(0.000)***
Weekday, month,
Yes
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
0.0028
(0.000)***
Observations
901781
Adjusted R-squared
0.137
(2)
-0.0010
(0.000)***
1.0411
(0.000)***
(3)
-0.0009
(0.000)***
1.0405
(0.000)***
(4)
-0.0009
(0.000)***
1.0406
(0.000)***
(5)
-0.0009
(0.000)***
1.0405
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.0010
(0.000)***
900166
0.138
0.0027
(0.000)***
901781
0.137
0.0032
(0.000)***
901781
0.137
Yes
0.0028
(0.000)***
899762
0.137
33
Table 5 - Performance of the Trading Portfolio
This table analyzes returns to a trading portfolio whose long and short positions are constructed based on local
pollen status. For each day, we construct an equal-weighted short portfolio composed of the firms that are located
in areas with very high pollen numbers and an equal-weighted long portfolio of the firms that are located in areas
with other pollen levels. Pollen category definitions (i.e. very high) are based on the NAB of the AAAAI
definitions. We assume the trading portfolio stops trading after pollen observation stations stop counting pollens
for a given year and starts trading again with the next year’s first pollen count observation. We compute daily
abnormal performance of our trading portfolio by regressing the trading portfolio returns on the market return, net
of the risk-free rate, (market excess return), the size (SMB), book-to-market(HML), and momentum (UMD)
factors. Heteroskedasticity and autocorrelation consistent standard errors are computed with the Newey-West
estimator using twelve lags. *,
**; *** shows statistical significance of p-values at 10%, 5%, and 1%
respectively.
Dependent Variable: Daily Return on the Trading Portfolio based on Pollen Status
Market excess return
SMB
HML
UMD
Constant
Observations
Adj. R-squared
-0.05434
(0.002)***
0.64601
(0.000)***
-0.04924
(0.274)
-0.07089
(0.008)***
0.00044
(0.010)***
2,515
0.178
34
Table 6 – Impact of Allergy Onset on the Next Day- Stock Return Tests
Return is daily stock return for a given day. In Panel A, Lagged Pollen shows daily local total pollen count divided by 1,000 for the previous day. In Panel B,
Lagged Very high is an indicator variable which takes value of 1 if daily pollen count is considered as very high for the previous day based on the scale
determined by the National Allergy Bureau (NAB) and zero otherwise. MarketReturn is the market return for a given day t. We use the value-weighted market
return in this table. This regression also includes control variables of year, month, and weekday dummy variables. Some tests include industry dummy variables
defined based on Fama-Frenh 48 industry classifications. This table also has the SAD onset variable of Kamstra et al. (2007) in some regressions. T he last test
includes local cloud coverage to proxy weather effects. Some tests include location dummies defined based on pollen count locations. The SAD onset variable
is defined by following Kamstra et al. (2007). Regressions have robust standard errors using firm-level clustering. Robust p-values are in parentheses (*
significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A:
Dependent variable: Return
Lagged Pollen
MarketReturn
Weekday, month,
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-0.0001
(0.044)**
1.0574
(0.000)***
(2)
-0.0001
(0.044)**
1.0579
(0.000)***
(3)
-0.0001
(0.049)**
1.0574
(0.000)***
(4)
-0.0001
(0.051)*
1.0573
(0.000)***
(5)
-0.0001
(0.042)**
1.0572
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.0025
(0.000)***
769789
0.138
0.0012
(0.000)***
768615
0.138
0.0024
(0.000)***
769789
0.138
0.0029
(0.000)***
769789
0.138
Yes
0.0026
(0.000)***
768204
0.138
35
Table 6 cont.
Panel B
Dependent variable: Return
Lagged VeryHigh
MarketReturn
Weekday, month,
and year fixed effects
Industry fixed effects
Location fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-0.0011
(0.000)***
1.0572
(0.000)***
(2)
-0.0011
(0.000)***
1.0577
(0.000)***
(3)
-0.0011
(0.000)***
1.0573
(0.000)***
(4)
-0.0011
(0.000)***
1.0571
(0.000)***
(5)
-0.0011
(0.000)***
1.0571
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
0.0025
(0.000)***
769789
0.138
0.0012
(0.000)***
768615
0.138
0.0024
(0.000)***
769789
0.138
0.0029
(0.000)***
769789
0.138
Yes
0.0026
(0.000)***
768204
0.138
36
Table 7 – Impact of Allergy Onset on the Next Day- Dollar Volume Tests
DolVol is daily dollar volume of a stock for a given day. AvgDolVol is equal to the average dollar volume for a given day t and it is calculated by
calculating the average dollar daily volume from the (-41,-11) window relative to day t. In Panel A, Lagged Pollen shows daily local total pollen
count divided by 1,000 for the previous day. In Panel B, Lagged Very high is an indicator variable which takes value of 1 if daily pollen count is
considered as very high for the previous day based on the scale determined by the National Allergy Bureau (NAB) and zero otherwise. This table
also includes control variables of year, month, and weekday dummy variables. We use Fama-French 48 industry definitions in determining
industry dummy variables. This table also has the SAD onset variable of Kamstra et al. (2007) in some regressions. The last test includes local
cloud coverage to proxy weather effects. Regressions have robust standard errors using firm-level clustering. Robust p-values are in parentheses
(* significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A
Dependent Variable: DolVol
(1)
(2)
(3)
(4)
(5)
Lagged Pollen
-331825.1041
-338061.8808
-327578.0687
-327150.7704
-337388.3271
(0.013)**
(0.015)**
(0.014)**
(0.014)**
(0.013)**
AvgDolVol
0.9729
0.9719
0.9722
0.9729
0.9729
(0.000)***
(0.000)***
(0.000)***
(0.000)***
(0.000)***
Yes
Yes
Yes
Yes
Yes
Weekday, month, year fixed effects
Industry fixed effects
Location fixed effects
Onset
Sky cover
Constant
Observations
Adjusted R-squared
Yes
Yes
Yes
5354507.3662
(0.002)***
769789
0.804
4101554.8979
(0.003)***
768615
0.804
4050540.9485
(0.005)***
769789
0.804
5835098.3565
(0.001)***
769789
0.804
Yes
5617042.1609
(0.002)***
768204
0.804
37
Table 7 cont.
Panel B
Dependent Variable: DolVol
Lagged VeryHigh
AvgDolVol
Weekday, month, year fixed
effects
Industry fixed effects
Location fixed effects
Onset
Sky cover
Constant
Observations
Adjusted R-squared
(1)
-1009330
(0.056)*
0.9729
(0.000)***
(2)
-984863.9426
(0.064)*
0.9719
(0.000)***
(3)
-847310.8771
(0.093)*
0.9722
(0.000)***
(4)
-965968.8176
(0.065)*
0.9729
(0.000)***
(5)
-1011337
(0.056)*
0.9729
(0.000)***
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
5288611.1469
(0.002)***
769789
0.804
4041432.4758
(0.003)***
768615
0.804
3973028.0777
(0.005)***
769789
0.804
5768945.7521
(0.001)***
769789
0.804
Yes
5545546.5103
(0.002)***
768204
0.804
38
Table 8- NYSE Dollar Volume Regressions
DolVol is average daily dollar volume of NYSE stocks for a given day. AvgDolVol is equal to the average dollar
volume for a given day t and it is calculated by calculating the average DolVol from the (-41,-11) window relative
to day t. Pollen shows daily local total pollen count of New York City divided by 1,000 for a given day. This
regression also includes control variables of year, month, and weekday dummy variables. This table also has the
SAD onset variable of Kamstra et al. (2007) in some regressions. The last test includes cloud coverage variable for
New York City to proxy weather effects. Regressions have robust standard errors using firm-level clustering. Robust
p-values are in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1 %.)
Dependent Variable: DolVol
Pollen
Avg DolVol
Weekday, month,
and year fixed effects
SAD Onset
Sky cover
Constant
Observations
Adjusted R-squared
-1,737,525
(0.064)*
0.5592
(0.000)***
-1,760,909.1
(0.060)*
0.5505
(0.000)***
-1,817,820.8
(0.051)*
0.5589
(0.000)***
Yes
Yes
Yes
Yes
30,796,000
(0.000)***
396
0.218
30,750,000
(0.000)***
396
0.218
Yes
33,002,000
(0.000)***
396
0.226
39
Table 9 - Google Search Volume Index (SVI) Tests
This table shows Google Search Volume Index (SVI) and abnormal Google SVI values for NASDAQ firms and all
firms based on daily local pollen status (very high pollen count numbers or not). We define Abnormal SVI for a
given day in week t minus the average SVI for the same weekday over the past weeks between the week t-1 and the
week t-4, divided by the average SVI for the same weekday over the past weeks between the week t-1 and the week
t-4. Google SVI dataset is from Michael Drake’s website and it is determined by Drake et al. (2012) and it is used
in their paper. Michael Drake’s website provides Google SVI dataset for the S&P 500 firms for the period of 2005
and 2008. This table also provides differences of mean values Google SVI and abnormal SVI for two group of
firms based on daily local pollen status (very high pollen count numbers or not) and p-value of differences. *, **,
*** denote significance at 10%, 5%, and 1%, respectively.
Panel A: NASDAQ Firms
Panel A.1. Google SVI
Very high
N
0
45,727
1
595
Panel A.2. Abnormal Google SVI
Very high
N
0
19,970
1
260
Panel B: All Firms
Panel B.1. Google SVI
Very high
N
0
107725
1
1757
Panel B.2. Abnormal Google SVI
Very high
N
0
67866
1
1185
Mean
0.8884
0.6296
Difference
0.2588
P-value (diff.)
(0.0027)***
Mean
0.0407
-0.00026
Difference
0.041
P-value (diff.)
(0.0326)**
Mean
0.9009
0.8359
Difference
0.0649
P-value (diff.)
(0.0701)*
Mean
0.0239
0.0122
Difference
0.0117
P-value (diff.)
(0.0849)*
40
Table 10 – Stock Return Tests with Different Distances
Return is daily stock return for a given day. In Panel A, Pollen shows daily local total pollen count divided by
1,000 for a given day. In Panel B, Very high is an indicator variable which takes value of 1 if daily pollen count is
considered as very high for a given day based on the scale determined by the National Allergy Bureau (NAB) and
zero otherwise. MarketReturn is the market return for a given day t. We use the value-weighted market return in
this table. This regression also includes control variables of year, month, and weekday dummy variables.
Regressions have robust standard errors using firm-level clustering. Robust p-values are in parentheses (*
significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A:
Dependent variable: Return
Distance to Pollen count
Pollen
MarketReturn
Weekday, month,
and year fixed effects
Constant
Observations
Adjusted R-squared
Panel B:
Dependent variable: Return
Distance to Pollen count
Very high
MarketReturn
Weekday, month,
and year fixed effects
Constant
Observations
Adjusted R-squared
10 mile
(1)
-0.0003
(0.001)***
1.0922
(0.000)***
25 mile
(2)
-0.0002
(0.001)***
1.0405
(0.000)***
50 mile
(3)
-0.0002
(0.001)***
1.0039
(0.000)***
Yes
Yes
Yes
0.0028
(0.000)***
476849
0.151
0.0029
(0.000)***
901781
0.137
0.0025
(0.000)***
1215018
0.121
10 mile
(1)
-0.0015
(0.000)***
1.0922
(0.000)***
25 mile
(2)
-0.0002
(0.001)***
1.0405
(0.000)***
50 mile
(3)
-0.0002
(0.001)***
1.0039
(0.000)***
Yes
Yes
Yes
0.0027
(0.000)***
476849
0.151
0.0029
(0.000)***
901781
0.137
0.0025
(0.000)***
1215018
0.121
41
Table 11 – Dollar Volume Tests with Different Distances
DolVol is daily dollar volume of a stock for a given day. AvgDolVol is equal to the average dollar
volume for a given day t and it is calculated by calculating the average dollar daily volume from the (41,-11) window relative to day t. In Panel A, Pollen shows daily local total pollen count divided by
1,000 for a given day. In Panel B, Very high is an indicator variable which takes value of 1 if daily
pollen count is considered as very high for a given day based on the scale determined by the National
Allergy Bureau (NAB) and zero otherwise. This table also includes control variables of year, month, and
weekday dummy variables. Regressions have robust standard errors using firm-level clustering. Robust
p-values are in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1 %.)
Panel A
Dependent Variable: DolVol
Distance to Pollen count
10 mile
25 mile
50 mile
(1)
(2)
(3)
Pollen
-345,672.09
-493824.5302
-269346.3348
(0.003)***
(0.025)**
(0.003)***
AvgDolVol
0.9716
0.9716
0.9713
(0.000)***
(0.000)***
(0.000)***
Weekday, month,year fixed effects
Yes
Yes
Yes
Constant
5969467
9579714.0580
4973791.6492
(0.000)***
(0.001)***
(0.000)***
Observations
901781
476176
1212438
Adjusted R-squared
0.805
0.806
0.804
Panel B
Dependent Variable: DolVol
Distance to Pollen count
10 mile
25 mile
50 mile
(1)
(2)
(3)
VeryHigh
-1909029
-1342774
-862640.8548
(0.083)*
(0.032)**
(0.089)*
AvgDolVol
0.9716
0.9715
0.9713
(0.000)***
(0.000)***
(0.000)***
Weekday, month,year fixed effects
Yes
Yes
Yes
Constant
9482463.8052
5895483.7635
4916134.0333
(0.001)***
(0.001)***
(0.000)***
Observations
476176
901781
1212438
Adjusted R-squared
0.806
0.805
0.804
42
Table 12 – Local Stock Ownership and Stock Return Tests
This table presents results of stock return tests reported in Column 1 of Table 4 for different local stock ownership subsamples by using different local
ownership measures. This table only presents the coefficients of Pollen and Very high for the corresponding regressions that also include the market return (the
value-weighted market return) as well as control variables of year, month, and weekday dummy variables. Regressions have robust standard errors using firmlevel clustering. Robust p-values are in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1 %.) This table also examines coefficient
(Pollen and Very High) differences across groups of firms with different levels of local stock ownership. In addition, this table presents the Wald test results to
determine whether the Pollen or Very High coefficients in stock return regressions are the same across groups of firms with different levels of local ownership.
This table reports the chi-square values provided by this test. Columns 1 and 2 focus on the number of firms per capita, which is the number local firms located
in a given firm's headquarter county scaled by county population. Columns 3 and 4 focus on local income (from the Census data) in a given firm's headquarter
county. Columns 5 and 6 focus on local education (from the Census data) in a given firm's headquarter county. Columns 7 and 8 focus on the differences
between firms located in the three largest metropolitan areas (New York, Chicago, and Los Angeles) and firms located in other areas.
Panel A
Subsamples
# of Firms per Capita
(1)
(2)
Subsamples
Small
Big
Dependent variable: Return
Pollen
-0.0004
-0.0000
(0.000)***
(0.990)
Coefficient Diff.
15.69
(0.000)***
Panel B
Subsamples
# of Firms per Capita
(1)
(2)
Subsamples
Small
Big
Dependent variable: Return
Very High
-0.00193
0.00002
(0.000)***
(0.949)
Coefficient Diff.
17.30
(0.000)***
Local Income
(3)
(4)
High
Low
Local Education
(5)
(6)
High
Low
-0.0004
-0.0000
(0.000)***
(0.760)
12.18
(0.000)***
-0.0004
-0.0001
(0.000)***
(0.346)
8.75
(0.003)***
Local Income
(3)
(4)
High
Low
Local Education
(5)
(6)
High
Low
-0.00176
-0.00014
(0.000)***
(0.643)
10.79
(0.001)***
-0.0013
-0.0005
(0.000)***
(0.156)
2.72
(0.099)*
Local Area
(7)
Other
(8)
Metropolitan
-0.0002
(0.000)***
0.0004
(0.117)
5.73
(0.017)**
Local Area
(7)
Other
(8)
Metropolitan
-0.0011
(0.000)***
0.0012
(0.085)*
9.98
(0.002)***
43
Table 13 – Local Stock Ownership and Trading Volume Tests
This table presents results of dollar volume tests reported in Column 1 of Table 2 for different local stock ownership subsamples by using different local
ownership measures. This table only presents the coefficients of Pollen and Very high for the corresponding regressions that also include the average dollar
volume as well as control variables of year, month, and weekday dummy variables. Regressions have robust standard errors using firm-level clustering. Robust
p-values are in parentheses (* significant at 10%; ** significant at 5%; *** significant at 1 %.) This table also examines coefficient (Pollen and Very High)
differences across groups of firms with different levels of local stock ownership. In addition, this table presents the Wald test results to determine whether the
Pollen or Very High coefficients in stock return regressions are the same across groups of firms with different levels of local ownership. This table reports the
chi-square values provided by this test. Columns 1 and 2 focus on the number of firms per capita, which is the number local firms located in a given firm's
headquarter county scaled by county population. Columns 3 and 4 focus on local income (from the Census data) in a given firm's headquarter county. Columns
5 and 6 focus on local education (from the Census data) in a given firm's headquarter county. Columns 7 and 8 focus on the differences between firms located in
the three largest metropolitan areas (New York, Chicago, and Los Angeles) and firms located in other areas.
Panel A
Subsamples
Subsamples
# of Firms per Capita
(1)
(2)
Small
Big
Dependent Variable: DolVol
Pollen
-335,301.6440 -237,708.0774
(0.002)***
(0.077)*
Coefficient Diff.
0.47
(0.494)
Panel B
Subsamples
Subsamples
# of Firms per Capita
(1)
(2)
Small
Big
Dependent Variable: DolVol
Very High
-1019903.032
-1333034.838
(0.102)
(0.059)*
Coefficient Diff.
0.24
(0.625)
Local Income
(3)
High
(4)
Low
Local Education
(5)
(6)
High
Low
Local Area
(7)
Other
(8)
Metropolitan
-729,974.0854 -48,244.4833
(0.003)***
(0.426)
7.83
(0.005)***
-786,786.1963
38,030.4938
(0.002)***
(0.559)
9.49
(0.002)***
-368,791.0670 -274,032.7020
(0.004)***
(0.141)
0.18
(0.673)
Local Income
Local Education
(5)
(6)
High
Low
Local Area
(3)
High
(4)
Low
-2279311.071
-402314.1116
(0.047)**
(0.078)*
2.74
(0.098)*
-2109597.758
-246254.7186
(0.052)*
(0.344)
2.90
(0.089)*
(7)
Other
(8)
Metropolitan
-1458743.49
(0.038)**
-629320.1686
(0.173)
0.98
(0.323)
44