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Paderborn University
www.taf-wps.cetar.org
No. 54 / April 2020
revised August 2020
Ortmann, Regina / Pelster, Matthias /
Wengerek, Sascha Tobias
Covid-19 and Investor Behavior
COVID-19 and investor behavior∗
Regina Ortmann†Matthias Pelster‡Sascha Tobias Wengerek§
Finance Research Letters, 101717
https://doi.org/10.1016/j.frl.2020.101717
Abstract How do retail investors respond to the outbreak of COVID-19? We use transaction-
level trading data to show that investors significantly increase their trading activities as the
COVID-19 pandemic unfolds, both at the extensive and at the intensive margin. Investors, on
average, increase their brokerage deposits and open more new accounts. The average weekly
trading intensity increases by 13.9% as the number of COVID-19 cases doubles. The increase
in trading is especially pronounced for male and older investors, and affects stock and index
trading. Following the 9.99%-drop of the Dow Jones on March 12, investors significantly reduce
the usage of leverage.
Keywords: Trading Behavior; Retail Investors; Risk-Taking; Pandemic; COVID-19
JEL Classification: G10, G11, G12, G40, G41.
∗Data were obtained under a non-disclosure agreement with a financial institution. We thank our data
provider for use and explanations of their data. We thank Thomas Müller for outstanding research support. We
are grateful for helpful comments and suggestions from seminar participants at Paderborn University. Regina
Ortmann gratefully acknowledges financial support by the German Research Foundation (DFG) - Collabora-
tive Research Center (SFB/TRR) Project-ID 403041268 - TRR 266 Accounting for Transparency. Any errors,
misrepresentations, and omissions are our own.
†Paderborn University. Warburger Str. 100, 33098 Paderborn, Germany, Phone: +49 (5251) 60-1780, e-mail:
regina.ortmann@upb.de.
‡Paderborn University. Warburger Str. 100, 33098 Paderborn, Germany, Phone: +49 (5251) 60-3766, e-mail:
matthias.pelster@upb.de.
§Paderborn University. Warburger Str. 100, 33098 Paderborn, Germany, Phone: +49 (5251) 60-5559, e-mail:
sascha.tobias.wengerek@upb.de.
Electronic copy available at: https://ssrn.com/abstract=3589443
1 Introduction
The novel coronavirus has led to unprecedented repercussions on daily life and the economy. The
outbreak makes investors, policy makers, and the public at large aware of the fact that natural
disasters can inflict economic damage on a previously unknown scale (Goodell, 2020). While the
aggregate effect of the pandemic on the stock market (Baker et al., 2020a; Ramelli and Wagner,
2020; Zhang et al., 2020) and the spending behavior of households (Baker et al., 2020b) have
been documented, little is known about the behavior of retail investors during such a turbulent
time. Considering that retail trades move stock prices in the direction of their trades (Barber
et al., 2009; Burch et al., 2016; Han and Kumar, 2013) and in particular retail short selling has
predictive ability for future (negative) stock returns (Kelley and Tetlock, 2016), it is, however,
important to investigate their behavior in these unprecedented conditions at the micro-level to
better understand aggregate market outcomes. We investigate trading patterns and financial
risk-taking of a large sample of retail investors based on their individual trading records during
the outbreak of COVID-19.
We use two lines of argumentation to express contrasting expectations about investor behavior
during the COVID-19 outbreak. First, the outbreak of the pandemic is in many regards com-
parable to terrorist attacks (see, e.g., Goodell, 2020): it is an exogenous shock, that has drastic
consequences on everyday life, raises public fear, and causes great (economic) uncertainty. In-
vestor behavior in the aftermath of terrorist activity is associated with more risk averse choices,
such as a reduced trading intensity and a reduced flow to risky assets (Levy and Galili, 2006;
Luo et al., 2020; Wang and Young, 2020). Burch et al. (2016) show heavy retail investor selling
in the crisis period set off by 9/11 that drives down asset prices. In line with these results, but
against the background of the outbreak of COVID-19, Bu et al. (2020) survey Chinese students
in Wuhan and find substantially lower general preferences for risk. Individuals that are more
exposed to COVID-19 consequences display a decreased willingness to take risky investments
and more pessimistic beliefs on the economy. Thus, in response to the outbreak of COVID-19,
investors may reduce their market exposure and risk-taking.
Second, in line with this increased uncertainty, press articles, media reports, and expert opinions
display a torn image of the future economic development and, thus, of optimal investment and
portfolio strategies. The outbreak of COVID-19 has led to significant financial market declines
and increased financial market risks around the world (Zhang et al., 2020). Central banks
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and governments have thrown their policy instruments into the market and launched support
programs never seen before (see Figure 1). In spite of these support programs, a great deal of
uncertainty persists. With the exact global economic impacts not yet clear, different opinions
circulate. Whereas, for example, President Donald Trump confidently proclaimed that there
will be a quick V-shaped recovery of the US economy and Hanspal et al. (2020) report that
US households expect a faster recovery of the stock market relative to previous crashes, Janet
Yellen expressed that it is common for economic growth after a crisis to remain on a lower track
for years, not months (Lee, 2020). Against the backdrop of these inconclusive expectations, it is
highly interesting to investigate investors’ trading activities during the outbreak of COVID-19.
Please place Figure 1 about here
We show that investors increase their average weekly trading intensity by 13.9% as the number
of COVID-19 cases doubles. Investors, on average, add funds to their accounts, open more new
accounts, and establish more new positions. We observe the largest increase in trading between
February 23 and March 22. Yet, investors also significantly reduce their usage of leverage after
the 9.99%-drop of the Dow Jones Industrial Average (Dow) on March 12.
The remainder of our paper proceeds as follows. We present the data and our methodology in
the next section. In section 3, we present the results. In the final section, we discuss our findings
and conclude.
2 Data and methodology
We use transactional-level brokerage data from a discount broker that offers an online trading
platform to retail investors under a UK broker license. Our data sample contains all trades that
the investors executed with the broker between August 1, 2019 and April 17, 2020. The data
contain the exact time-stamp and instrument of the trade, together with an indicator for long
or short positions, and the leverage. In total, the dataset comprises 45,003,637 transactions
executed by 456,365 investors. Additionally, it includes the deposits to and withdrawals from
the brokerage accounts. The data also contain details of push notifications that inform investors
of volatility events (see Arnold et al., 2020). Lastly, the dataset comprises basic demographic
information. We obtain data on the number of COVID-19 cases from the European Centre for
Disease Prevention and Control.
2
Electronic copy available at: https://ssrn.com/abstract=3589443
We study the relation between the outbreak of COVID-19 and investors’ trading activities using
an OLS regression analysis. We use several variables to proxy investors’ trading activities.
Trading intensity denotes the number of trades in a given week. The variable takes a value of
zero for investors who do not trade in a given week. Leverage, a pure measure of risk-taking,
denotes the leverage employed for a trade. Short sale is a dummy variable that takes a value of
one, if a trade establishes a short position, and zero otherwise. Abnorm. net deposits denotes
the number of deposits minus the number of withdrawals on a given day, divided by the average
net deposits prior to the outbreak of the pandemic. Abnorm. first deposits denotes the number
of deposits by investors who opened a new account on a given day, divided by the average first
deposits prior to the outbreak of the pandemic. Buy-sell imbalances (BSI) denote the relation
between long minus short to total positions. Finally, abnormal trading volume in an industry
denotes the trading volume on day tdivided by the average trading volume in that industry
over the last six months.
To capture the outbreak of the pandemic, we use the following variables. COVID-19 denotes the
logarithm of the number of corona cases plus one. Dow drop is a dummy variable that takes a
value of one on March 13, the day after the Dow and the FTSE, the UK’s main index, recorded
major losses, and zero otherwise. The Dow fell a record 2,352.60 points (9.99%) to close at
21,200.62. The FTSE dropped more than 10% and recorded its worst day since 1987. Lastly,
we use three dummy variables to define various stages of the outbreak. The first stage (Jan. 23
- Feb. 22 ) begins when China ordered the lockdown. At this time, investors will have started
to understand the importance of the disease, as this lockdown affected supply chains in Europe
and other parts of the world. The second stage (Feb. 23 - Mar. 22 ) begins when Italy ordered
the lockdown in February, as then the disease had become a pandemic that reached Europe.
The third stage (Mar. 23 - Apr. 17 ) begins when the UK ordered the lockdown in March, as a
large part of countries across the world had already issued lockdowns or severe restrictions on
public life by then (see Figure 1).
Our specification includes investor fixed effects to control for observed and unobserved hetero-
geneity across investors such as their demographics or wealth. We also include a full set of
asset class dummies to control for different trading behaviors across asset classes. Lastly, we
control for push notifications before investors’ trades, as Arnold et al. (2020) show that such
push notifications increase risk-taking and trading within a 24-hour time period.
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3 Results
We present the evolution of investors’ trading activities in Figure 2 in detail. We observe a
significant increase in index trading, mostly between February 23 and March 23, which decreases
again after March 23. Slightly less pronounced, we observe an increase in stock trading, followed
by a decline after March 23. Contracts for difference (CFD) trading on stocks shows several
spikes over the course of the pandemic. Crypto trading shows a distinct spike following the
drop of the Dow on March 12. Figure 2(b) shows a decline in leverage-usage across asset classes
between February 23 and March 23, that is most pronounced following the drop of the Dow.
Panel (c) shows an increase in short-selling using CFDs on stocks, but no clear trend across
other asset classes.
Please place Figure 2 and Table 1 about here
Table 1 presents our main results. Panel A, Model 1 shows a 13.9% increase in the average
weekly trading intensity, compared to the average trading before the pandemic, as the number
of COVID-19 cases doubles. The increase in trading is mainly driven by male investors (Model
4) and by older investors (Model 5). Model 2 shows that the trading intensity increased by
222%, compared to the average trading before the pandemic, following the 9.99%-drop of the
Dow on March 12, which is largely driven by the spike in cryptocurrency trading (untabulated).
Finally, Model 3 shows that the largest increase in trading is observed between February 23 to
March 22. Table 1, Panel B, shows that the increase in trading is driven by increased stock and
index trading, while CFDs on stocks, cryptocurrencies, and gold are less affected. The increase
in trading is also prevalent for new created positions in stocks and indizes (Panel C).
Table 2 shows that investors, on average, add additional funds to their trading accounts. The
abnormal net deposits increase by .41 (Model 1) as the cases number doubles. The increase in
fund-flow is driven by both new (Model 2) and established investors (Model 3). Thus, investors
increase their trading activities not only at the intensive but also at the extensive margin (see
also Figure 3).
Please place Table 2 and Figure 3 about here
Panels D and E of Table 1 show a large decline in leverage-usage across all genders and age
groups during the outbreak. The largest decline can be observed following the Dow drop on
4
Electronic copy available at: https://ssrn.com/abstract=3589443
March 12. As a response, investors reduced their average leverage-usage by 172 percentage
points.
Panels F and G show that investors increase their propensity to take short positions by, on
average, 2% of their propensity to engage in short positions before the outbreak of COVID-
19. We observe an increase in short selling activities across all asset classes (Panel G), which is
especially pronounced for the more recent time periods (Panel F, Model 3) and younger investors
(Panel F, Model 5).1
Figure 4 presents BSI over time. Investors, on average, take long stock positions, and this
tendency increases during the outbreak of the pandemic. BSI for index positions and gold
move around zero, indicating neutral positions, on average. Cryptocurrencies show two spikes
towards long positions around February 23 and March 23. CFD stock positions overall show
strong variation during the outbreak, with more short positions until March 23, and a tendency
towards long positions afterwards.
Please place Figure 4 about here
Lastly, we study the investor behavior with a focus on industries, based on the North American
Industry Classification System (NAICS). We study the abnormal trading volume and the frac-
tion of short sales jointly for stock trading and CFDs on stocks. Figure 5 shows the evolution for
the five industries that record the largest values in these variables during our sample period. We
observe the highest abnormal trading volume in Transit and Ground Passenger Transportation,
Motion Picture and Sound Recording Industries,Accommodation,Water Transportation, and
Air Transportation. We find the highest short selling in Motion Picture and Sound Recording
Industries,Accommodation,Air Transportation,Supportive Activities for Transportation, and
Administrative and Support Services, which includes travel-related companies such as TripAd-
visor, Expedia, or TUI. We show that the trading volume starts to increase during the period
from January 23 to February 23, in particular for the Accommodation and Water transportation
industries. The timing coincides with the first cruise ship having a major outbreak on board and
1In additional (untabulated) analyses, we study the trading patterns and risk-taking of investors during other
recent market downturns, such as the drastic drop of the Dow in December 2018. A comparison of the results
from past market downturns and activities around the COVID-19 outbreak indicates that investors’ activities
around the outbreak of COVID-19 are unique, in line with the unprecedented nature of the crisis. In particular,
changes in trading amount to at most 5% of the effect size that we observe during the outbreak of the pandemic.
Moreover, we observe that investors, on average, withdraw funds from their accounts, open fewer new accounts,
and do not significantly change their risk-taking during other recent market downturns.
5
Electronic copy available at: https://ssrn.com/abstract=3589443
being quarantined from February 4 onward. We also find an early increase in short selling in
the most affected industries, such as the Accommodation,Air Transportation, or Administrative
and Support Services industries, at the beginning of February, more than a month before the
large spikes in March.
Please place Figure 5 about here
4 Discussion
We show that investors increase their trading activities as the COVID-19 pandemic unfolds, both
at the extensive and at the intensive margin. The number of investors who first open an account
with the broker increases, while at the same time established investors increase their average
trading activities. Investors, on average, significantly increase their weekly trading intensity by
13.9% as the number of COVID-19 cases doubles. In particular, investors open more stock and
index positions, but do not move to safe-haven (gold) or particularly “risky” (CFDs on stocks,
cryptocurrencies) investments. The increase in trading is especially pronounced for male and
older investors, and largest during the period from February 23 to March 22. Investors also
marginally increase their tendency to engage in short selling. Stock trading increases most for
industries that tend to be losers as the crisis progresses. Here, especially travel-related industries
are exposed to early short selling at the beginning of February, in line with the notion that retail
short selling has predictive ability for future stock returns (Kelley and Tetlock, 2016).
Our results indicate that, in line with the torn image that press articles, media reports, and
expert opinions paint these days, investors’ trading activities are also not clear-cut. Our findings
stand in contrast to investors’ reactions to other shocks that increase uncertainty, such as terrorist
attacks, which are associated with reduced flows to risky asset classes (Wang and Young, 2020)
and heavy retail investor selling (Burch et al., 2016). While investors increase their trading
intensity and more readily open new positions, we nonetheless show that investors act more
cautiously following the drop of the Dow on March 12. Following the 9.99%-drop of the Dow,
investors reduce their leverage-usage, which is in line with the notion that investors make more
risk-averse choices due to public fear (Levy and Galili, 2006; Luo et al., 2020; Wang and Young,
2020). The fact that (i) buy-sell imbalances in index positions are close to zero and (ii) some
investors take long stock positions while others short single name stocks using CFDs, underscores
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that investors have different expectations, in line with the torn picture experts and media outlets
paint. Investors who take long stock or index positions may buy into the narrative of the fast
economic recovery once the pandemic passes (Hanspal et al., 2020), and believe that the lockdown
offers a favorable opportunity to enter the stock market, while those taking short positions may
hold the opinion that this narrative is too optimistic. Inconsistencies between investors’ short-
term and long-term expectations created by unlimited quantitative easing programs (Gormsen
and Koijen, 2020) may further contribute to ambiguous investor behaviors.
A caveat of our analysis is that investors in our dataset may not be representative of the average
household. Investors likely select a brokerage service based on their preferences. Notwith-
standing this limitation, we believe that our study provides important insights into the trading
activities of retail investors during the outbreak of the pandemic. Our study provides initial
insights that may inform future research that attempts to explore the impact of the outbreak of
a pandemic on retail investor behavior further.
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Financial Studies 30, 801–834.
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8
Electronic copy available at: https://ssrn.com/abstract=3589443
Figure 1: Key events during the outbreak of COVID-19
This figure shows the key events during the outbreak of the pandemic.
Dec
Jan
Feb
Mar
Apr
Novel pneumonia detected (Wuhan)
(2019−12−31)
First Death to COVID−19
(2020−01−11)
China
(2020−01−23)
Italy
(2020−02−22)
FED (IR −0.5%)
(2020−03−03)
FTSE (− 8.11%)
Dow (−7.79%)
(2020−03−09)
Global pandemic (WHO)
(2020−03−11)
BoE (IR −0.5%)
FTSE (− 10.87%)
Dow (− 9.99%)
(2020−03−12)
FED (IR −1% and BPP)
(2020−03−15)
Dow (− 12.93%)
(2020−03−16)
France
(2020−03−17)
ECB −> PEPP
(2020−03−18)
United States
(2020−03−20)
Germany
(2020−03−22)
United Kingdom
(2020−03−23)
Germany (€600bil)
United Kingdom −> CBILS
United States ($2.2tr)
(2020−03−27)
FED ($2.3tr EP)
(2020−04−09)
EU (€540bil)
Events aaaaa
Information Lockdown Central Bank intervention Governmental emergency program Stock market reaction
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Figure 2: Trading activities over time
This figure presents the trading intensity, leverage-usage, and short sale propensity over time (with 99% confidence
intervals).
Jan 23
Feb 23
Mar 23
Dow drop
0
1
2
3
Oct
Jan
Apr
Trading intensity
Asset class
CFD_stock
Crypto
Gold
Index
Stock
(a) Trading intensity
Jan 23
Feb 23
Mar 23
Dow drop
0
10
20
30
40
Oct
Jan
Apr
Leverage
Asset class
CFD_stock
Crypto
Gold
Index
(b) Leverage
Jan 23
Feb 23
Mar 23
Dow drop
0.2
0.4
0.6
Oct
Jan
Apr
Short sales
Asset class
CFD_stock
Crypto
Gold
Index
(c) Short sales
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Figure 3: Number of active investors
This figure presents the number of active investors over time.
Jan 23
Feb 23
Mar 23
Dow drop
30000
60000
90000
Oct
Jan
Apr
Investors by week
Asset class
CFD_stock
Crypto
Gold
Index
Stock
Figure 4: Buy-sell imbalances during the COVID-19 outbreak
This figure presents the buy-sell imbalances over time.
Jan 23
Feb 23
Mar 23
Dow drop
−0.1
0.0
0.1
0.2
Oct
Jan
Apr
Buy−sell imbalance
Asset class
CFD_stock
Crypto
Gold
Index
Stock
Figure 5: Most affected industries
This figure presents the abnormal trading volume and short sale propensity of the five most affected industries,
respectively, over time.
Jan 23
Feb 23
Mar 23
Dow drop
0
10
20
30
Oct
Jan
Apr
Abnormal trading volume
Industry Transit/Ground Transp.
Motion Picture/Sound
Accommodation
Water Transp.
Air Transp.
(a) Abnormal trading volume
Jan 23
Feb 23
Mar 23
Dow drop
0.0
0.2
0.4
0.6
Oct
Jan
Apr
Short sales
Industry Adm./Support Serv.
Motion Picture/Sound
Accommodation
Support Act. for Transp.
Air Transp.
(b) Fraction of short sales
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Table 1: Regression results: Trading activities
This table reports results from an OLS regression on the trading activities of investors. Standard errors are
double-clustered at the individual investor level and over time; t-statistics are in parentheses. ** and * denote
statistical significance at the 1% and 5% levels, respectively.
Panel A: Trading intensity
Model 1 Model 2 Model 3 Model 4 Model 5
Dependent Trading Trading Trading Trading Trading
variable intensity intensity intensity intensity intensity
COVID-19 0.2220∗0.1202 0.2129∗
(2.3004) (1.5625) (2.2849)
Dow drop 3.5557∗∗
(11.4704)
Jan. 23 - Feb. 22 0.2763
(1.1377)
Feb. 23 - Mar. 22 2.7410∗∗
(3.3521)
Mar. 23 - Apr. 17 0.6378
(1.2035)
Cases ·male 0.1130∗∗
(4.0556)
Cases ·18-24 −0.1714∗∗
(−3.4184)
Cases ·25-34 −0.0150
(−0.4196)
Cases ·35-44 0.0542
(1.4619)
Cases ·45-54 0.0950∗
(2.4387)
Cases ·55-64 0.0475
(1.3193)
Push message control Yes Yes Yes Yes Yes
Asset class dummy Yes Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes Yes
Obs. 14,113,014 14,525,010 14,525,010 14,088,650 14,072,248
Adj. R20.36 0.37 0.37 0.36 0.36
Panel B: Trading intensity by asset classes
Model 1 Model 2 Model 3 Model 4 Model 5
Sample Stocks CFD_stock Index Crypto Gold
COVID-19 0.0363∗∗ 0.0142 0.1813∗∗ −0.0008 −0.0165
(5.1362) (0.9807) (4.0297) (−0.0463) (−1.4860)
Push message control Yes Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes Yes
Obs. 14,113,014 14,113,014 14,113,014 14,113,014 14,113,014
Adj. R20.37 0.34 0.30 0.27 0.23
Panel C: Trading intensity (new positions) by asset classes
Model 1 Model 2 Model 3 Model 4 Model 5
Sample Stocks CFD_stock Index Crypto Gold
COVID-19 0.0195∗∗ 0.0068 0.0910∗∗ −0.0028 −0.0083
(5.1803) (0.9776) (4.0396) (−0.3065) (−1.4974)
Push message control Yes Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes Yes
Obs. 14,113,011 14,113,011 14,113,011 14,113,011 14,113,011
Adj. R20.37 0.33 0.30 0.28 0.23
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Table 1: Regression results: Trading activities (cont.)
Panel D: Leverage
Model 1 Model 2 Model 3 Model 4 Model 5
Dep. var. Leverage Leverage Leverage Leverage Leverage
COVID-19 −0.3019∗∗ −0.3155∗∗ −0.2406∗∗
(−8.3412) (−5.8471) (−4.0663)
Dow drop −1.7197∗∗
(−6.9803)
Jan. 23 - Feb. 22 0.4080∗
(2.0808)
Feb. 23 - Mar. 22 −1.0652
(−1.4160)
Mar. 23 - Apr. 17 −2.9917∗∗
(−8.9368)
Cases ·male 0.0146
(0.3624)
Cases ·18-24 0.0033
(0.0484)
Cases ·25-34 −0.0970
(−1.5689)
Cases ·35-44 −0.0963
(−1.6319)
Cases ·45-54 −0.0114
(−0.1945)
Cases ·55-64 0.0040
(0.0639)
Push message control Yes Yes Yes Yes Yes
Asset class dummy Yes Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes Yes
Obs. 4,771,217 4,946,112 4,946,112 4,767,966 4,758,012
Adj. R20.64 0.64 0.64 0.64 0.64
Panel E: Leverage by asset classes
Model 1 Model 2 Model 3 Model 4
Dep. var. Leverage Leverage Leverage Leverage
Sample CFD_stock Index Crypto Gold
COVID-19 −0.1530∗∗ −0.5289∗∗ 0.0045∗∗ −0.5121∗∗
(−11.5243) (−12.9833) (2.7170) (−7.3752)
Push message control Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes
Obs. 1,040,042 650,338 1,174,571 591,974
Adj. R20.64 0.76 0.55 0.79
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Table 1: Regression results: Trading activities (cont.)
Panel F: Short sales
Model 1 Model 2 Model 3 Model 4 Model 5
Dependent Short Short Short Short Short
variable sales sales sales sales sales
COVID-19 0.0056∗∗ 0.0055∗∗ 0.0028∗∗
(7.3213) (5.5340) (3.3955)
Dow drop 0.0158∗
(2.5069)
Jan. 23 - Feb. 22 −0.0004
(−0.0798)
Feb. 23 - Mar. 22 0.0315∗∗
(3.5548)
Mar. 23 - Apr. 17 0.0364∗∗
(6.9650)
Cases ·male 0.0000
(0.1031)
Cases ·18-24 0.0061∗∗
(4.4759)
Cases ·25-34 0.0044∗∗
(5.4906)
Cases ·35-44 0.0020∗∗
(2.9686)
Cases ·45-54 0.0010
(1.4507)
Cases ·55-64 −0.0002
(−0.2507)
Push message control Yes Yes Yes Yes Yes
Asset class dummy Yes Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes Yes
Obs. 4,771,217 4,946,112 4,946,112 4,767,966 4,758,012
Adj. R20.15 0.15 0.15 0.15 0.15
Panel G: Short sales by asset classes
Model 1 Model 2 Model 3 Model 4
Dependent Short Short Short Short
variable sales sales sales sales
Sample CFD_stock Index Crypto Gold
COVID-19 0.0112∗∗ 0.0033∗∗ 0.0039∗∗ 0.0041
(5.2606) (2.9362) (4.5214) (1.9366)
Push message control Yes Yes Yes Yes
Investor-fixed effects Yes Yes Yes Yes
Obs. 1,047,042 650,338 1,174,571 591,974
Adj. R20.15 0.04 0.09 0.08
14
Electronic copy available at: https://ssrn.com/abstract=3589443
Table 2: Regression results: Account deposits
This table reports results from an OLS regression on deposits and withdrawals. Standard errors are robust;
t-statistics are in parentheses. ** and * denote statistical significance at the 1% and 5% levels, respectively.
Model 1 Model 2 Model 3
Dependent Abnorm. net Abnorm. first Abnorm. net
variable deposits deposits deposits
Sample Full New Established
sample investors investors
(Intercept) 1.0611∗∗ 1.0007∗∗ 1.0532∗∗
(9.1315) (32.0302) (18.6130)
COVID-19 0.4132∗∗ 0.2825∗∗ 0.1373∗
(5.9015) (12.0879) (2.5400)
Obs. 261 261 261
Adj. R20.19 0.55 0.04
Figure A.1: Abnormal trading volume during the COVID-19 outbreak
This figure presents the abnormal trading volume over time. We follow Barber and Odean (2008) and define
broker-specific abnormal trading volume on day t,AVtas
AVt=Vt
¯
Vt
,
where Vtdenotes the trading volume on day tand ¯
Vtdenotes the average trading volume of the last six months
with the broker.
Jan 23
Feb 23
Mar 23
Dow drop
1
2
3
4
5
6
Oct
Jan
Apr
Abnormal trading volume
Asset class
CFD_stock
Crypto
Gold
Index
Stock
15
Electronic copy available at: https://ssrn.com/abstract=3589443
Figure A.2: Abnormal account deposits
This figure presents abnormal net cashflows and abnormal first deposits over time. We define abnormal cashflows
and first deposits as the net cashflows and first deposits divided by their rolling averages of the last six months,
respectively.
Jan 23
Feb 23
Mar 23
Dow drop
0
1
2
3
4
Oct
Jan
Apr
Abnormal net cashflows
(a) Abnormal net cashflows
Jan 23
Feb 23
Mar 23
Dow drop
1.0
1.5
2.0
2.5
3.0
Oct
Jan
Apr
Abnormal first deposits
(b) Abnormal first deposits
16
Electronic copy available at: https://ssrn.com/abstract=3589443
Figure A.3: Trading differences between established and new investors
This figure presents the trading intensity, leverage-usage, and short sale propensity over time (with 99% confidence
intervals), separately for investors who already traded in 2019 and for investors who started their trading activities
in 2020.
Jan 23
Feb 23
Mar 23
Dow drop
2.5
5.0
7.5
10.0
12.5
Oct
Jan
Apr
Trading intensity
Investor
Established
New
(a) Trading intensity
Jan 23
Feb 23
Mar 23
Dow drop
4
5
6
7
8
Oct
Jan
Apr
Leverage
Investor
Established
New
(b) Leverage
Jan 23
Feb 23
Mar 23
Dow drop
0.1
0.2
0.3
0.4
0.5
Oct
Jan
Apr
Short sales
Investor
Established
New
(c) Short sales
17
Electronic copy available at: https://ssrn.com/abstract=3589443
Table A.1: Regression results: Buy-sell imbalances
This table reports results from an OLS regression on the buy-sell imbalances of the trades that investors execute
with the broker. Standard errors are robust; t-statistics are in parentheses. The data are from a discount
brokerage firm.
Panel A: COVID-19 cases
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Sample Full sample Stocks Index CFD_stock Gold Crypto
(Intercept) 0.0392 0.0883 0.0030 0.0005 0.0027 0.0420
(6.6026) (9.9580) (0.4532) (0.0528) (0.2578) (4.1970)
COVID-19 cases 0.0013 0.0033 −0.0006 −0.0020 0.0010 −0.0015
(1.1269) (1.0929) (−0.7201) (−0.9595) (0.6540) (−0.7712)
Obs. 261 197 223 205 223 261
R20.0049 0.0131 0.0014 0.0040 0.0014 0.0023
Panel B: Dow drop
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Sample Full sample Stocks Index CFD_stock Gold Crypto
(Intercept) 0.0412 0.0983 0.0013 −0.0064 0.0057 0.0348
(9.2828) (11.1840) (0.2753) (−0.6972) (0.7511) (4.5540)
Dow drop 0.5050 (omitted) −0.0483 0.1991 0.0110 0.7167
(113.7420) (−10.0074) (21.5844) (1.4536) (93.7970)
Obs. 261 197 223 205 223 261
R20.1612 0.0000 0.0020 0.0111 0.0000 0.1156
Panel C: Time period dummies
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Sample Full sample Stocks Index CFD_stock Gold Crypto
(Intercept) 0.0394 0.0881 0.0030 −0.0026 0.0047 0.0397
(6.8349) (10.9603) (0.4722) (−0.2624) (0.4859) (4.2425)
Jan. 23 - Feb. 22 0.0051 0.0234 −0.0076 −0.0367 −0.0116 −0.0137
(0.4471) (1.6721) (−0.5773) (−0.8921) (−0.6285) (−0.6508)
Feb. 23 - Mar. 22 0.0139 0.0684 −0.0102 −0.0267 0.0076 0.0094
(0.6627) (3.2012) (−1.1767) (−0.5861) (0.5856) (0.2228)
Mar. 23 - Apr. 17 0.0220 0.0298 −0.0028 0.0418 0.0216 −0.0185
(1.6966) (0.4164) (−0.3467) (2.6243) (0.9203) (−0.7049)
Obs. 261 197 223 205 223 261
R20.0077 0.0255 0.0023 0.0192 0.0043 0.0029
18
Electronic copy available at: https://ssrn.com/abstract=3589443
