Working
Paper
469
The Short-selling Skill of Institutions and
Individuals: a Market-wide and Out-of-sample
Analysis
Fernando Chague
Rodrigo De-Losso
Bruno Giovannetti
CEQEF - Nº46
Working Paper Series
Os artigos dos Textos para Discussão da Escola de Economia de São Paulo da Fundação Getulio
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FGV-EESP. É permitida a reprodução total ou parcial dos artigos, desde que creditada a fonte. Escola de Economia de São Paulo da Fundação Getulio Vargas FGV-EESP
The Short-selling Skill of Institutions and Individuals: a
Market-wide and Out-of-sample Analysis
∗
Fernando Chague
†, Rodrigo De-Losso
‡, Bruno Giovannetti
September 13, 2017
Abstract
Using market-wide data from the Brazilian stock lending market, we nd strong evidence of short-selling skill for some institutions and individuals. Skilled short-sellers present out-of-sample performance persistence, both over time and across stocks. Per-formance persistence is robust: by randomly splitting the sample across stocks, we show that performance in a group of stocks often predicts performance in another group of stocks. We then study how skilled short-sellers trade. We nd that most of their pro t does not come from rm-speci c private information, they follow short-term momentum strategies, and they do not display the disposition eect.
JEL Codes: G12, G14.
Keywords: short-selling, skilled investors, out-of-sample performance, short-term momentum, disposition eect
∗We thank Flávio Abdenur, Marco Bonomo, Markus Brunnermeier, Carlos Viana de Carvalho, Marcelo
Fernandes, Bernardo Guimarães, Marcos Nakaguma, Walter Novaes, Ruy Ribeiro, Pedro Sa , José Carlos de Souza Santos, and participants at seminars in the São Paulo School of Economics at Getúlio Vargas Foundation, Lubra n 2017 meeting, and EBFIN 2017 meeting for their valuable comments. We also thank Eduardo Astorino and Elias Cavalcanti for excellent research assistance.
†Sao Paulo School of Economics, FGV, Brazil. Corresponding author at: Rua Itapeva, 474. CEP
01332-000. Sao Paulo - SP, Brazil. Tel.: +55 11 3799-3582. E-mail: fernando.chague@fgv.br.
‡Department of Economics, University of Sao Paulo, Brazil. E-mail: delosso@usp.br. Sao Paulo School of Economics, FGV, Brazil. E-mail: bruno.giovannetti@fgv.br.
1 Introduction
The existence of skilled investors who can consistently beat the market is still contro-versial.1 In this paper, we focus on a particular type of investment skill: short-selling skill.
Short-selling is an important trading strategy that accounts for a large fraction of traded volume.2 Using market-wide data from the Brazilian stock lending market, we present
out-of-sample evidence of short-sellers, both institutions and individuals, who can consistently beat the market.
Focusing on selling to study investment skill has two advantages. First, short-selling deals are short-lived bets with well-de ned outcomes. Since a typical short-seller places many of these bets in a year, we can measure performance with few years of data. Second, once we compute performance at the deal-level (instead of at the portfolio level, as usual), we are able to split the data set both over time and across stocks to study out-of-sample performance persistence. That is, besides studying if past performance predicts future performance (persistence over time), we can also study if performance in one group of stocks predicts performance in another group of stocks (cross-sectional persistence). Importantly, since stocks can be divided in an arbitrary number of dierent ways, we can analyze the robustness of cross-sectional performance persistence.
Documenting the existence of short-selling skill among both institutions and individuals naturally contributes to the extensive and still active literature that searches for investment skills among market participants.3 However, we contribute more directly to the speci c
lit-1According to Berk and van Binsbergen (2015), an extensive literature in nancial economics has focused
on the question of whether stock picking or market timing talent exists. Interestingly, the literature has not been able to provide a de nitive answer to this question.
2According to Diether, Lee, and Werner (2009), short-sales were responsible for 24% of NYSE volume
and 31% of Nasdaq volume by 2005. Nowadays these shares should be higher. In Brazil, the volume share of short sales ranges from 18% to 39% during our sample period (2012 to 2014).
3For mutual funds, the empirical evidence from the early 90's is favorable to the existence of performance
persistence (Lehmann and Modest, 1987, Grinblatt and Titman, 1992, Goetzmann and Ibbotson, 1994, Brown and Goetzmann, 1995, and Elton, Gruber, and Blake, 1996). However, following studies show that performance is unpredictable once mutual fund expense ratios and momentum in stock returns are taken into account (Carhart, 1992, 1997, Gruber, 1996, Zheng, 1999, Wermers, 2000, Bollen and Busse, 2001). In turn, Berk and Green (2004) argue that performance persistence should be hard to be detected even in the presence of skill due to managers decreasing returns to scale. More recently, by looking at the monetary value
erature on short-selling. Skilled short-sellers are like reclusive celebrities: prized but elusive. Although they are highly regarded by practitioners and researchers because aggregate short-ing predicts returns,4 very few is known at the short-seller level. Using proprietary NYSE
order data, Boehmer, Jones, and Zhang (2008) disaggregate short-selling up to investor type (individual and institution) and nd that the average institution is more informed than the average individual. Using a data set constructed from occasional disclosures of very large short-selling positions in Europe, Jank and Smajlbegovic (2015) nd that hedge funds out-perform other short-sellers. Using proprietary data from discount brokerage rms, Kelley and Tetlock (2017) documents that aggregate short-selling by individuals predicts lower re-turns, which suggests the existence of individuals who may be skilled short-sellers. Using data from the Korean stock market, Wang, Lee, and Woo (2017) study short-selling by in-dividuals and nd that inin-dividuals who sell short more rms make higher and persistent pro ts.
We are the rst to use market-wide data to comprehensively study short-selling skill at the investor level. We observe all 4,575,324 stock loan deals by all institutions and indi-viduals in Brazil from 2012 to 2014. For each loan contract we have the stock traded, the loan quantity, the loan fee, the brokerage rate, the short-seller type (individual or institu-tion), a unique identi cation variable for the short-seller, the dates when the loan contract was both initiated and eectively terminated, the date when it was originally set to ter-minate, and whether it could be terminated earlier by the borrower, by the lender, or by both.5 We use all these variables to answer the following ve questions: What proportion
of fees collected by funds, Berk and van Binsbergen (2015) provide favorable evidence of skilled managers. Crane and Crotty (2017) show that index funds also have skill. With respect to individual investors, Odean (1999), Barber and Odean (2000), Grinblatt and Keloharju (2000), and Barber, Lee, Liu, and Odean (2009) nd that the average performance of individual investors is poor in the long-run. However, some studies show that the returns earned by individual investors over short horizons (up to a week) appear to be quite strong (Kaniel, Saar, and Titman, 2008, Kaniel, Liu, Saar, and Titman, 2012, Barber, Odean, and Zhu, 2009), and that there is performance persistence among individual investors (Coval, Hirshleifer, and Shumway, 2005).
4Looking at aggregate shorting, Diether, Lee, and Werner (2009), Engelberg, Reed, and Ringgenberg
(2012), and Rapach, Ringgenberg, and Zhou (2016) show that the opening of short positions predicts lower returns. In turn, Boehmer, Duong, and Huszár (2017) show that the covering of large short-positions predicts higher returns.
of institutions and individuals can consistently beat the market? Do they present robust out-of-sample performance persistence? By how much does their total pro t depend on the anticipation of private rm-speci c information? Do they contribute to the price discovery process by avoiding short-term price overshooting or by accelerating the incorporation of negative information into stock prices? Do they display the disposition eect when covering their positions? The goal of the rst two questions is to study whether short-selling skill indeed exists at the investor level. The goal of the last three questions is to provide some understanding on short-selling skill.
We rst say a short-seller is a P S-short-seller (a potentially skilled short-seller) if three minimum conditions are simultaneously satis ed: i) the average risk-adjusted return across her short-selling deals is positive and statistically signi cant, ii) her total pro t from shorting is positive, and iii) she closed at least 10 distinct short-selling deals. From a total of 4,107 institutions, we classify 367 (8.9%) as P S-short-sellers (P S-institutions hereinafter). They present a high average risk-adjusted return per deal of 0.85% (or 25.5% per year, considering the median duration of a shorting deal of 12 days), and a high average return net of loan and brokerage fees per deal of 0.80% (24% p.y.). They are responsible for 31.5% of the total shorting volume and 26.7% of the total number of shorting deals. With respect to individuals, we classify 1,484 (4.2%) as P S-short-sellers (P S-individuals hereinafter) from a total of 35,338 individuals. Their performance is even better. Their average risk-adjusted return per deal is 1.70% (51.0% p.y.), while their average net return per deal is 1.59% (47.7% p.y.). They are responsible for 0.3% of the total shorting volume and 2.5% of the total number of shorting deals.
We then study the sample performance of P S-short-sellers. We conduct out-of-sample exercises along two dimensions. First, we study performance persistence over time. We classify short-sellers as P S-short-sellers in 2012 and 2013 and compute their
out-of-lending market which does not track the loan deal over time. They nd thatfor the same stock, on the same daywell-connected stock borrowers pay signi cantly lower loan fees, a result that relates search costs in the equity lending market with short-selling restrictions.
sample performance in 2014. Second, we explore the fact that our performance analysis is at the deal-level to study cross-sectional performance persistence. We rst split stocks based on two rm-related variables: rm size and full-sample realized return. Do short-sellers who outperform on large caps also outperform on other stocks? Do short-short-sellers who outperform on stocks that presented a negative full-sample return also outperform on stocks that presented a positive full-sample return? We then randomly split the stocks many times to evaluate whether the evidence of cross-sectional performance persistence survives even in the least favorable sample splits. We classify short-sellers as P S-short-sellers using half of the stocks and compute their out-of-sample performance on the remaining stocks. We perform this cross-sectional out-of-sample analysis in 1,000 dierent randomly partitioned data sets.
The out-of-sample results provide strong evidence of performance persistence among short-sellers. With respect to persistence over time, institutions that are classi ed as P S-short-sellers in 2012 and 2013 outperform other institutions in 2014 by, on average, 0.30% per deal (risk-adjusted). Individuals who are classi ed as P S-short-sellers in 2012 and 2013 outperform other individuals in 2014 by 0.44% per deal. The results are the same if we consider net or gross returns, and if we compute medians.
With respect to persistence across stocks, short-sellers who outperform on large caps also outperform on other stocks. Moreover, short-sellers who outperform on stocks that presented a negative full-sample return also outperform on stocks that presented a positive full-sample return. Cross-sectional persistence is rather robust. In our 1,000 random sample splits, in virtually all partitioned data sets, P S-short-sellers outperform other short-sellers out-of-sample. The average across the 1,000 estimates of the out-of-sample excess performance of P S-institutions is 0.31% per deal (risk-adjusted) and the 5th percentile across the 1,000 estimates is 0.14% per deal. The average across the 1,000 estimates of the out-of-sample excess performance of P S-individuals is 0.91% per deal and the 5th percentile across the 1,000 estimates is 0.71% per deal. The fact that both P S-institutions and P S-individuals
continue to outperform other investors out-of-sample even in the least favorable sample splits (the 5th percentiles) is remarkable.
The robust out-of-sample evidence of performance persistence indicates that there is short-selling skill. Next, we study short-selling skill by examining P S-short-sellers trading activity. We answer two questions related to their ability in opening a short position and one question related to their ability in covering a short position.
According to the short-selling literature, an important portion of short-sellers superior performance comes from correctly anticipating rm-speci c news events.6 The economic
im-portance of such events to short-sellers' pro t, however, is an open empirical question which we can answer with our detailed data set. To do so, we compute the share of the P S-short-sellers' total pro t that can be attributed to these events. Since we observe both the initial and terminal dates of each deal, we can determine whether a rm-speci c event did occur during the life span of the deal. Considering all P S-institutions, we nd that 19.9% of their total shorting pro t (US$ 2.2 billion in the 3-year period) came from deals during which a negative earning announcement occurred, 17.1% from deals during which a negative mate-rial fact announcement7 occurred, and 3.2% from deals during which both events occurred.
Considering all P S-individuals, we nd that 12.8% of their total shorting pro t (US$ 50 mil-lion in the 3-year period) came from deals during which a negative earning announcement occurred, 6.9% from deals during which a negative material fact announcement occurred, and 3.4% from deals during which both events occurred. The fact that only a portion of short-sellers pro t came from these two types of rm-speci c events is reassuring. If this were not the case, the practice of insider trading by short-sellers could be more relevant to
6Christophe, Ferri, and Angel (2004) nd that aggregate short-selling increases few days prior to negative
earnings announcements. Engelberg, Reed, and Ringgenberg (2012) nd that aggregate short-selling is particularly strong around days when rm-speci c news is disclosed.
7According to Brazilian regulation, important corporate news announcements must the disclosed
imme-diately by the rm (usually referred to as material facts), and any company insider endowed with this information is prohibited to disclose it privately or to trade upon it. Because of their nature largely unan-ticipated and containing important information about the rm not yet impounded into prices , material facts are important piece of news that aects prices signi cantly. Examples of material facts include proposal of mergers, changes of CEOs, and announcements of secondary public oering of shares.
their pro t than genuine shorting skill. This is particularly true for negative material fact announcements, which are not scheduled and should be unpredictable for company outsiders. In our second analysis of P S-short-sellers ability in opening a short position, we relate their trading activity to past movements of stock prices. Along this dimension, we can broadly characterize investors as following either a momentum strategyi.e., the investor sells short after a stock price fallor a contrarian strategyi.e., the investor sells short after a stock price increase. An informed short-seller who follows a contrarian strategy would contribute to the price discovery process by avoiding short-term price overshooting. In contrast, an informed short-seller who follows a momentum strategy would contribute to the price discovery process by accelerating the incorporation of negative information into stock prices. Our results indicate that P S-short-sellers are short-term momentum traders. The results hold both in- and out-of-sample. This nding resonates with Yan and Zhang (2009) who, using publicly available data on portfolio holdings, provide evidence that skilled institutions tend to trade at short-term horizons and follow momentum strategies.
In our last analysis of shorting skill, we study the covering of a short position. One of the most robust features of unsophisticated investors behavior is the so-called disposition eect (i.e., the tendency of riding losses and realizing gains; see Barberis and Xiong, 2009). Accordingly, we test if P S-short-sellers are less susceptible to the disposition eect. To do so, we run deal-by-deal regressions of a dummy variable that indicates whether a deal was terminated earlier by the short-seller on a variable that measures the initial performance of the deal: the rst 5-day stock return since the opening of the short position. A negative rst 5-day return means a good start for the deal. If the short-seller responds to a good start by anticipating the covering of the deal, she is behaving according to the disposition eect.8 As
expected, P S-short-sellers do not display the disposition eect, while other short-sellers do. The results also hold both in- and out-of-sample.
The remainder of the paper is organized as follows. In Section 2 we describe our data set
8In 99.2% of the loan contracts in our data set the borrower has the option to return the stock to the
and present some basic statistics concerning short-selling in Brazil. In Section 3 we present evidence of the existence of short-selling skill. In Section 4 we examine P S-short-sellers trading activity to study shorting skill. Finally, Section 5 concludes.
2 Short-selling in Brazil
Short-selling is very common in Brazil. On average 25% of the traded volume comes from equities being sold short. This is similar to the number reported by Diether, Lee, and Werner (2009) for the US market in 2005 (24% for NYSE and 31% for Nasdaq). Figure 1 shows, on a monthly basis, the total number of shares traded, the total number of shares loaned, and the ratio between these two numbers.
[Figure 1 about here]
Stock lending is regulated by the Brazilian Securities and Exchange Commission (CVM); all shorting loans are registered at BM&FBOVESPA, which acts as the central-counterpart in this market. Recent articles on short-selling have explored this very detailed Brazilian equity lending market data. Chague, De-Losso, De Genaro, and Giovannetti (2014) show that aggregate shorting predicts returns in Brazil, Bonomo, Mello, and Mota (2015) test whether short-selling restrictions generate stock overpricing, and Chague, De-Losso, Genaro, and Giovannetti (2017) show that well-connected borrowers with lower search costs pay signi cantly lower loan fees.
2.1 Data set
Our data set contains all of the 4,575,324 equity loan contracts closed in Brazil from January 2012 to December 2014. For each loan contract we have the stock traded, the loan quantity, the loan fee, the brokerage rate, the short-seller type (individual or institution), a unique identi cation variable for the short-seller, the dates when the loan contract was both initiated
and eectively terminated, the date when it was originally set to terminate, and whether it could be terminated earlier by the borrower, by the lender, or by both.
We apply a single lter to the original data set. We exclude from the full sample all loan contracts that intersect an interest on equity ex-date.9 According to Brazilian law, the tax
treatment of interest on equity diers according to investor type: individual investors pay a tax rate of 15%, while nancial institutions are exempt. As a result, on days around the ex-date of interest on equity there are many tax arbitrage trades between individuals and nancial institutions in which individuals lend shares to nancial institutions at a higher loan fee. These loans deals are therefore unrelated to short-selling.
Our nal working sample contains 3,341,213 loan contracts on 357 dierent stocks. Panel A of Table 1 displays, for each year of the sample, the total number of loan contracts and the total number of distinct short-sellers by investor type. Panel B of Table 1 exhibits some statistics on the empirical distributions of two variables, the number of loan deals closed by short-sellers and the duration (measured in calendar days) of the loan contracts. Shorting deals are mostly short-term trades: the median number of days of a loan contract in our sample is 12 days for both types of investors.10
[Table 1 about here]
3 Is there short-selling skill?
There is solid evidence that aggregate shorting predicts future returns (see, for instance, Boehmer, Jones, and Zhang, 2008, Diether, Lee, and Werner, 2009, Engelberg, Reed, and Ringgenberg, 2012, and Rapach, Ringgenberg, and Zhou, 2016). Therefore, the short-selling market is a promising place to look for skilled investors. We directly infer the skill of a
9In Brazil, rms distribute cash to shareholders in the form of cash dividends, interest on equity, or a
combination of the two.
10We do not consider shorting deals that are covered on the same day (day trades), as these deals do not
short-seller by looking at the outcomes of her shorting deals. It is like inferring the skill of a poker player by looking at how many games she wins.
Our sample period is particularly suitable to measure short-sellers performance. Between January 2012 and December 2014, the Brazilian stock market experienced no overall trend, with high volatility. Hence, there were su cient ups and downs for short-sellers to show their skill (or their lack of) as Figure 2 illustrates.
[Figure 2 about here]
In Section 3.1, we rst classify short-sellers according to their in-sample individual per-formance. We show that some short-sellers are potentially skilled: they present good perfor-mance and high shorting activity. They are like poker players who play and win many games. In Section 3.2, we run several out-of-sample exercises to show that short-selling performance is persistent out-of sample. Back to our poker analogy, players who win some games are more likely to win other games. Taken together, these results support the existence of short-selling skill.
3.1 In-sample performance of short-sellers
To assess short-sellers performance, we rst compute for each shorting deal i its realized risk-adjusted return: Ra
i = 1 − Pi,1a /Pi,0a , where Pi,0a is the risk-adjusted price at which the
short-seller sells the stock and Pa
i,1is the risk-adjusted price at which the short-seller buys the
stock to cover the position.11 In Brazil, just as in the United States, equity transactions are
settled after three trading days, while equity loans are settled on the same day. Accordingly,
11We adjust prices for risk as follows. For each stock, we rst run time-series regressions of the daily
stock return on the four Fama-French-Cahart risk factors (the risk factors for Brazil were obtained at http://www.ne n.com.br). We then compound the residuals of these regressions (the idiosyncratic daily stock returns) to obtain a time-series of risk-adjusted prices of each stock.
a short-seller does not need to borrow a stock until the third day after taking her short position. Therefore, following Geczy, Musto, and Reed (2002) and Beschwitz, Bastian, and Massa (2015), we use in the computation of Pa
i,0 and Pi,1a the closing prices three trading
days earlier to the dates the loan contract was initiated and terminated, respectively.12
We then compute for each short-seller k the average risk-adjusted return across her shorting deals. To avoid considering dierent loan deals linked to the same shorting deal, we consider at most one loan deal per investor-stock-week (randomly chosen). Let Nk be the
number of loan deals used to compute short-seller k's average risk-adjusted return. We call these Nk deals distinct short-selling deals.
Additionally, we compute for each short-seller k her total pro t from shorting during the 3-year period, taking into account expenses with loan and brokerage fees. To compute short-seller k's total pro t, we use all her loan deals (not only the Nk).
We then say that short-seller k is a potentially skilled short-seller (P S-short-seller) if three minimum conditions are simultaneously satis ed: (i) her average risk-adjusted return across the Nkdistinct short-selling deals is statistically signi cant and positive13, (ii) her total
pro t is positive, and (iii) Nk ≥ 10. Short-sellers who do not meet any of these conditions
are classi ed as other short-sellers. Among these, there should be many dierent types of short-sellers: sporadic short-sellers who made few bets; skilled short-sellers who were unlucky; unskilled short-sellers; and potentially skilled investors who sell short for reasons other than directional trading such as long-short strategies, hedging, and liquidity supplying. Figure 3 shows the monthly evolution of the number of stocks traded, the number of deals closed, and the average duration of the shorting deals, for the median P S-short-seller and the median other short-seller. The median P S-institution sells short nine dierent stocks per month, closes about 45 loan deals per month, and stays about 20 days in a deal,
12In 99.2% of the loan contracts in our data set the borrower has the option to return the stock to the
lender earlier than the original expiration date of the loan contract. The early return process is automatic and costless. Therefore, it is reasonable to assume that when a short-seller closes her position (buys the stock back) she also terminates the loan contract to avoid unnecessary loan costs.
13We test condition (ii) using a standard t-test considering a 10%-con dence level, with standard-errors
while the median other institution sells short three stocks per month, closes about six loan deals per month, and stays about 16 days in a deal. The median P S-individual sells short two dierent stocks per month, closes about three loan deals per month, and stays about 17 days in a deal, while the median other individual sells short one stock per month, closes about two loan deals per month, and stays about 15 days in a deal.
[Figure 3 about here]
According to Panel A of Table 2, from a total of 4,107 institutions, 367 (8.9%) are P S-short-sellers, and from a total of 35,338 individuals, 1,484 (4.2%) are P S-short-sellers. P S-institutions are responsible 31.5% of the total shorting volume and 26.7% of the total number of shorting deals. P S-individuals are responsible for 0.3% of the total shorting volume and 2.5% of the total number of shorting deals.
[Table 2 about here]
Panel A of Table 2 also reports the average and median returns of each group. We com-pute three types of returns for each shorting deal: gross returns, which are risk-unadjusted and do not consider loan costs;14net returns, which are also risk-unadjusted but do consider
loan costs (loan and brokerage fees); and risk-adjusted returns, which are risk-adjusted and are the same used to classify short-sellers as potentially skilled. P S-institutions present an average (median) gross return per deal of 1.12% (0.56%), while other institutions present
14We compute gross returns as 1 − P
i,1/Pi,0, where Pi,0 is the price at which the short-seller sells the
stock and Pi,1 is the price at which the short-seller buys the stock back. As before, Pi,0 and Pi,1 are the
closing prices three trading days earlier to the dates when the loan contract was initiated and terminated, respectively.
0.10% (0.00%). P S-institutions present an average (median) net return per deal of 0.80% (0.40%), while other institutions present 0.10% (0.00%). Finally, P S-institutions present an average (median) risk-adjusted return per deal of 0.85% (0.54%), while other institu-tions present 0.16% (0.00%). These returns are economically important given the typical length of a shorting deal (median of 12 days, according to Table 1).
In turn, P S-individuals present an average (median) gross return per deal of 1.76% (1.55%), while other individuals present -0.57% (-0.14%). P S-individuals present an aver-age (median) net return per deal of 1.59% (1.44%), while other individuals present -0.72% (-0.26%). Finally, P S-individuals present an average (median) risk-adjusted return per deal of 1.70% (1.42%), while other individuals present -0.20% (0.06%). The dierence in the performance between P S-short-sellers and other short-sellers is even higher for individuals than for institutions.
Panel A of Table 2 also reports the average and median probability of each short-seller presenting the observed share of winning deals under the null of no skill. We count for each short-seller k the number of deals with positive net return (N+
k) among her Nk distinct
short-selling deals. We then use the binomial distribution to compute P N+
k; Nk, 0.5
, i.e., the probability of short-seller k presenting at least N+
k successes in Nk trials if her
success probability in a single deal is equal to 50%. The lower P N+
k; Nk, 0.5
, the higher the chance of short-selling k having a success probability greater than 50%. P S-institutions present an average (median) P N+
k; Nk, 0.5
of 0.09 (0.22), while other institutions present 0.75 (0.69). In turn, P S-individuals present an average (median) P N+
k; Nk, 0.5
of 0.11 (0.20), while other individuals present 0.73 (0.67).
Finally, Panel B of Table 2 reports the same statistics from Panel A but excluding stocks from nancial institutions.15 Brunnermeier and Oehmke (2014) argue that nancial
institu-tions may be vulnerable to predatory short-selling. In our sample, however, the performance of P S-short-sellers and other short-sellers remains qualitatively the same.
To illustrate performance at the individual level, Table 3 shows some statistics for the twenty P S-short-sellers (ten institutions and ten individuals) with the lowest P N+
k ; Nk, 0.5
. Column (2) presents the winning fraction computed as N+
k/Nk. The remaining columns
present for each short-seller the following variables: the number of distinct short-selling deals, Nk, the total pro t in thousands of dollars considering all deals, the average
risk-adjusted, gross, and net return across the Nk deals, and the total number of dierent stocks
sold short by the short-seller during the full sample.
[Table 3 about here]
Figure 2 indicates that P S-short-sellers trade more than other short-sellers. This could directly follow, however, from our imposition of Nk ≥ 10 for a short-seller to be classi ed
as potentially skilled. To evaluate whether P S-sellers trade more than other short-sellers, we compare P S-short-sellers' trading activity with the activity of other short-sellers who also have Nk ≥ 10. We regress three variables that capture dierent aspects of shorting
activity on a dummy variable that equals one if the short-seller is classi ed as potentially skilled. The dependent variables are the number of distinct selling deals of each short-seller (Nk), the average volume of each deal, and the number of distinct stocks sold short by
each short-seller in the full sample.
According to Panel A of Table 4, a P S-institution closes on average 298.5 more deals than an institution that is not potentially skilled. This represents an increase of 86.1%. Moreover, the average deal volume of a P S-institution is US$ 50 thousand greater (46.7% in relative terms). Finally, a P S-institution also sells short more stocks (20.1 more stocks on average, or 60.1% more). According to Panel B of Table 4, a P S-individual closes on average 5.37 more deals than a individual that is not potentially skilled. This represents an increase of 14.2%. On the other hand, the average deal volume of a P S-individual is smaller
(US$ 3 thousand smaller, 13.0% in relative terms). Finally, a P S-individual also sells short more stocks (4.9 more stocks on average, or 38.6% more).
[Table 4 about here]
Even under the null hypothesis of no skill, some investors would present good realized performance (type I error). The fact that P S-short-sellers display a more intense short-selling activity, however, goes against the type I error story. Nonetheless, additional evidence is certainly needed if we want to conclude that there is shorting skill. For this purpose, we next perform a number empirical exercises to show that P S-short-sellers performance continuous to be superior out-of-sample.
3.2 Out-of-sample performance of short-sellers
In this section we show that the good performance of P S-short-sellers is persistent out-of-sample. The rst exercise is a standard out-of-sample analysis with respect to time. We classify short-sellers as P S-short-sellers using their deals in 2012 and 2013 and study their performance using their deals in 2014.
The second exercise explores the fact that our performance analysis is at the deal-level. We rst split stocks based on two rm-related variables: rm size and full-sample realized return. We then randomly split the stocks 1,000 times to evaluate whether the cross-sectional evidence of performance persistence survives even in the least favorable sample splits. 3.2.1 Time-dimension out-of-sample
Using only the deals in 2014, we estimate the following conditional mean and median deal return
E reti,k|P S12,13k = π0+ π1P S12,13k
M ed reti,k|P Sk12,13 = π2+ π3P Sk12,13
where P S12,13
k is a dummy variable that equals one if short-seller k is classi ed as a P
S-short-seller considering only her deals in 2012-2013,16 and ret
i,k is the short-selling return
(gross, net, and risk-adjusted) of deal i closed by short-seller k in 2014. Both regressions are estimated separately for institutions and individuals.
Table 5 presents the results. There is evidence of performance persistence for both P S-institutions and P S-individuals. According to Panel A, institutions that are classi ed as P S-short-sellers in 2012-2013 have in 2014: expected gross return per deal of 0.609% (0.330% + 0.279%) v.s. 0.279% of institutions who are not classi ed as P S-short-sellers; median gross return per deal of 0.572% v.s. 0.215%; expected net return per deal of 0.380% v.s. 0.110%; median net return per deal of 0.427% v.s. 0.124%; expected risk-adjusted return per deal of 0.469% v.s. 0.171%; and median risk-adjusted return per deal of 0.523% v.s. 0.257%.
[Table 5 about here]
According to Panel B of Table 5, individuals that are classi ed as P S-short-sellers in 2012-2013 have in 2014: expected gross return per deal of 0.173% v.s. -0.284% of individuals who are not classi ed as P S-short-sellers; median gross return per deal of 0.532% v.s. 0.108%; expected net return per deal of 0.004% v.s. -0.433%; median net return per deal of 0.423% v.s. 0.001%; expected risk-adjusted return per deal of 0.326% v.s. 0.084%; and median risk-adjusted return per deal of 0.552% v.s. 0.276%.
16We apply the same criteria as before: to be considered a P S-short-seller, she must have statistically
positive average risk-adjusted return across the Nk distinct short-selling deals, positive total pro t, and
According to these results, short-sellers classi ed as potentially skilled in 2012-2013 present in 2014 a better performance than the ones not classi ed as potentially skilled in 2012-2013. To provide further evidence of performance persistence, we next take advantage of our deal-by-deal data to conduct out-of-sample exercises across stocks.
3.2.2 Stock-dimension out-of-sample: size and full sample return
The fact that our measure of performance is computed at the deal-level (as opposed to at the portfolio level) allows us to split the sample across stocks. In our rst cross-sectional out-of-sample exercise, we split stocks based on two rm-related variables: rm size and full-sample realized return. Do short-sellers who outperform on large caps also outperform on other stocks? Do short-sellers who outperform on stocks with negative full-sample return also outperform on stocks with positive full-sample return?
With respect to rm size, we rank all stocks in our sample according to their average market capitalization and assign the top 20 stocks to the group Big. Then, using only deals on stocks that are not in the group Big, we estimate
Ereti,k|P SBigk
= π0+ π1P SBigk
M ed
reti,k|P SkBig
= π2+ π3P SkBig
where P SBig
k is a dummy variable that equals one if short-seller k is classi ed as a P
S-short-seller considering only her deals on stocks from group Big, and reti,k is the short-selling
return (gross, net, and risk-adjusted) of deal i closed by short-seller k on stocks that are not in the group Big. Both regressions are estimated separately for institutions and individuals.
Table 6 present the results. There is evidence of performance persistence for both P S-institutions and P S-individuals. According to Panel A, S-institutions that are classi ed as P S-short-sellers within Big stocks have on other stocks: expected gross return per deal of 1.293% (0.719% + 0.574%) v.s. 0.574% of institutions who are not classi ed as P S-short-sellers; median gross return per deal of 0.665% v.s. 0.229%; expected net return per deal of 0.851% v.s. 0.262%; median net return per deal of 0.464% v.s. 0.077%; expected risk-adjusted return per deal of 0.837% v.s. 0.398%; and median risk-risk-adjusted return per deal of 0.554% v.s. 0.292%.
According to Panel B of Table 6, individuals that are classi ed as P S-short-sellers within Big stocks have on other stocks: expected gross return per deal of 0.311 (0.629% - 0.318%) v.s. -0.318% of individuals who are not classi ed as P S-short-sellers; median gross return per deal of 0.564% v.s. 0.000%; expected net return per deal of 0.142% v.s. -0.523%; median net return per deal of 0.438% v.s. -0.050%; expected risk-adjusted return per deal of 0.568% v.s. -0.027%; and median risk-adjusted return per deal of 0.712% v.s. 0.278%.
With respect to full-sample realized return, we call the stocks with negative cumulative return over 2012-2014 as Losers. Then, using only deals on stocks that are not in the group Losers, we estimate
E reti,k|P SLosersk = π0+ π1P SLosersk
M ed reti,k|P SkLosers = π2+ π3P SkLosers
where P SLosers
k is a dummy variable that equals one if short-seller k is classi ed as a P
S-short-seller considering only her deals on stocks from group Losers, and reti,k is the short-selling
return (gross, net, and risk-adjusted) of deal i closed by short-seller k on stocks that are not in the group Losers. Both regressions are estimated separately for institutions and individuals.
[Table 7 about here]
Table 7 present the results. Since the regressions are estimated using only stocks which presented a positive return over the full sample, we nd that on average all short-sellers lose on these stocks. However, consistent with the existence of performance persistence for both P S-institutions and P S-individuals, these investors perform on average better (lose less) than the others. According to Panel A, institutions that are classi ed as P S-short-sellers within stocks in group Losers have on other stocks: expected gross return per deal of -0.761% (0.211% - 0.972%) v.s. -0.972% of institutions who are not classi ed as P S-short-sellers; median gross return per deal of -0.422% v.s. -0.556%; expected net return per deal of -0.917% v.s. -1.098%; median net return per deal of -0.525% v.s. -0.634%; expected risk-adjusted return per deal of 0.398% v.s. 0.048%; and median risk-adjusted return per deal of 0.431% v.s. 0.168%.
According to Panel B of Table 7, individuals that are classi ed as P S-short-sellers within stocks in group Losers have on other stocks: expected gross return per deal of -0.450% (0.606% - 1.052%) v.s. -1.052% of individuals who are not classi ed as P S-short-sellers; median gross return per deal of 0.000% v.s. -0.489%; expected net return per deal of -0.609% v.s. -1.192%; median net return per deal of -0.095% v.s. -0.590%; expected risk-adjusted return per deal of 0.477% v.s. 0.024%; and median risk-adjusted return per deal of 0.661% v.s. 0.213%.
According to Tables 6 and 7, short-sellers who outperform on large caps also outperform on other stocks, and short-sellers who outperform on stocks with negative full-sample return also outperform on stocks with positive full-sample return. These results show that there is performance persistence across these dimensions, reinforcing the existence of short-selling skill. We next take the cross-sectional out-of-sample exercise one step ahead. Since stocks
can be randomly divided in an arbitrary number of ways, we perform 1,000 cross-sectional out-of-sample regressions. Our goal is to evaluate whether the evidence of performance persistence survives even in the least favorable sample splits.
3.2.3 Stock-dimension out-of-sample: 1,000 sample splits
We run 1,000 cross-sectional out-of-sample exercises to check performance persistence. Each out-of-sample exercise is based on the construction of two groups of stocks randomly selected, groups A and B. To assign stocks to either group, we rst divide all stocks in our sample into four quantiles according to their total shorting volume during the full sample period. We then randomly assign half of the stocks in each quantile to group A and the other half to group B. By doing so, we end up with two random groups of stocks with similar shorting volume. Using only deals on stocks from group B, we estimate
E reti,k|P SAk = π0+ π1P SkA
M ed reti,k|P SkA = π2+ π3P SkA
where P SA
k is a dummy variable that equals one if short-seller k is a P S-short-seller
consid-ering only her deals on stocks from group A, and reti,k is the short-selling return (gross, net,
and risk-adjusted) of deal i closed by short-seller k. Regressions are estimated separately for institutions and individuals.
Figures 4, 5, and 6 present the histograms of the 1,000 estimates of π1 (the
out-of-sample superior mean performance of P S-short-sellers) and π3 (the out-of-sample superior
median performance of P S-short-sellers), with respect to gross, net, and risk-adjusted re-turns. Clearly, the P S-short-sellers performance is persistent for both institutions and in-dividuals. In virtually all of the 1,000 out-of-sample exercises, investors who are classi ed as P S-short-sellers in half of the stocks have superior performance in the other half of the
stocks.
[Figures 4, 5, and 6 about here]
Table 8 tabulates the 1,000 estimates of π1 and π3. Panel A refers to the gross return
measure. Considering institutions, the average across the 1,000 estimates of π1 is 0.44%;
the median across the 1,000 estimates of π1 is 0.43%; and even the 5th percentile across the
1,000 estimates of π1 is positive (0.14%). The average of π3 is 0.33%; the median of π3 is
0.32%; and even the 5th percentile of π3 is positive (0.15%). Considering individuals, the
average of π1 is 0.91%; the median of π1 is also 0.91%; and even the 5th percentile of π1 is
positive (0.71%). The average of π3 is 0.76%; the median of π3 is also 0.76%; and even the
5th percentile of π3 is positive (0.62%).
[Table 8 about here]
Panel B of Table 8 refers to the net return measure. Considering institutions, the average across the 1,000 estimates of π1is 0.50%; the median across the 1,000 estimates of π1is 0.49%;
and even the 5th percentile across the 1,000 estimates of π1 is positive (0.12%). The average
of π3 is 0.39%; the median of π3 is 0.38%; and even the 5th percentile of π3 is positive
(0.18%). Considering individuals, the average of π1 is 0.92%; the median of π1 is also 0.92%;
and even the 5th percentile of π1 is positive (0.71%). The average of π3 is 0.77%; the median
of π3 is also 0.77%; and even the 5th percentile of π3 is positive (0.62%).
Panel C of Table 8 refers to the risk-adjusted return measure. Considering institutions, the average across the 1,000 estimates of π1 is 0.31%; the median across the 1,000 estimates
(0.05%). The average of π3 is 0.26%; the median of π3 is 0.25%; and even the 5th percentile
of π3 is positive (0.12%). Considering individuals, the average of π1 is 0.62%; the median of
π1 is also 0.62%; and even the 5th percentile of π1 is positive (0.47%). The average of π3 is
0.52%; the median of π3 is also 0.52%; and even the 5th percentile of π3 is positive (0.41%).
In all three panels of Table 8, the 5th percentiles across the 1,000 estimates of π1 and
π3 are positive. This is remarkable. Even even in the less favorable random splits of our
sample, both P S-institutions and P S-individuals continue to outperform other investors out-of-sample.
Aggregate predictability
Because of the well-documented predictability power of aggregate shorting over future re-turns, short-sellers are often collectively regarded as sophisticated investors with superior information (for instance, Boehmer, Jones, and Zhang, 2008, Diether, Lee, and Werner, 2009, Engelberg, Reed, and Ringgenberg, 2012, and Rapach, Ringgenberg, and Zhou, 2016). Based on this literature, an alternative way to present the evidence of performance persis-tence is by verifying whether the out-of-sample predictability power of aggregate shorting comes from P S-short-sellers.
To test this, we regress the 20-day ahead return of stock s on day t (rets,t+1→t+20) on
its number of shorted shares divided by its number of traded shares on day t (relsss,t).
We compute relss using deals from all short-sellers (relssall), using deals only from P
S-short-sellers (relssP S), and using deals only from other short-sellers (relssothers). Since we
are interested in out-of-sample performance of aggregate shorting, we rst de ne P S-short-sellers using only deals on stocks from group A, and then run the out-of-sample predictability regressions using deals on stocks from group B. That is, for each one of the 1,000 samples randomly created we run the following panel regressions
rets,t+1→t+20 = β0+ βallrelssalls,t + s,t
rets,t+1→t+20 = β1+ βP SrelssP Ss,t + βothersrelssotherss,t + ηs,t
Figure 7 presents the results. The rst histogram presents the 1,000 estimates of βall.
Most of the estimated coe cients are negative (895), con rming the well-documented evi-dence of aggregate shorting predicting lower future returns. The second and third histograms reveal that the predictability of aggregate shorting comes from deals by P S-short-sellers. From a total of 1,000 regressions, βP S estimates are negative in 993 of them. In contrast, in
only 196 out of the 1,000 regressions, the estimates of βothers are negative.
[Figure 7 about here]
3.3 Yes, there is short-selling skill
In Section 3.1 we de ne three conditions for a short-seller to be classi ed as potentially skilled: she must have placed at least ten distinct short-selling deals, she must present sta-tistically positive average risk-adjusted return across her deals, and she must present positive monetary pro t. We then show that P S-short-sellers, beside presenting good performance, also display higher shorting activity if compared to others in terms of number and size of deals, and number of stocks traded. Additionally, in Section 3.2 we perform time- and stock-dimension out-of-sample exercises and con rm that the good performance of P S-short-sellers is persistent out-of-sample. Taken together, these ndings support the existence of shorting skill in the stock market. In the next section, we examine the trading behavior of P S-short-sellers. We present three empirical exercises, two related to the opening of their short
positions and one related to the covering.
4 A closer look at P S-short-sellers trading activity
In this section we take a closer look at P S-short-sellers trading activity. In the rst analysis, we decompose the P S-short-sellers' total pro t. A well-documented important source of short-sellers superior performance is their ability to anticipate rm-speci c news. Christophe, Ferri, and Angel (2004) show that aggregate short-selling increases few days prior to negative earnings announcements. Engelberg, Reed, and Ringgenberg (2012) nd that aggregate short-selling is particularly strong around days when rm-speci c news is disclosed. The economic importance of such events to short-sellers' realized pro t is an open empirical question which can be addressed with our detailed data set. By how much does the realized pro t of P S-short-sellers depend on the anticipation of rm-speci c events? Since we observe both the initial and terminal dates of each loan deal, we can determine whether a rm-speci c event did occur during the deal life span. From that, we can directly analyze the importance of rm-speci c news to the realized pro t of P S-short-sellers.
In the second analysis, we relate P S-short-sellers trading activity with past movements of stock prices. If informed short-sellers sell short following short-term stock price increases (thus acting as contrarian investors), they would contribute to the price discovery process by avoiding short-term price overshooting. On the other hand, if informed short-sellers sell short following short-term stock price falls (thus acting as short-term momentum investors), they would contribute to the price discovery process by accelerating the incorporation of negative information into stock prices.
In the third analysis, we study the covering of a short position. One of the most robust features of unsophisticated investors behavior is the so-called disposition eect, i.e., the tendency of riding losses and realizing gains (Barberis and Xiong, 2009). Accordingly, we expect P S-sellers to be less susceptible to the disposition eect than the other
short-sellers. We test this by studying the decision of short-sellers to anticipate the covering of a shorting deal.
4.1 Decomposing P S-short-sellers pro t
By how much does the realized pro t of P S-short-sellers depend on the anticipation of rm-speci c events? To answer this question, we focus on two types events: (i) negative earnings announcements and (ii) negative material facts announced by rms17 (e.g. other corporate
news announcements not directly related to earnings announcements).18 These types of
events dier from each other in some important aspects; it follows that anticipating them requires dierent skills. Earnings announcements occur periodically and are scheduled; their content is known in advance only by company insiders but can be potentially predicted using public information. In contrast, material facts occur sporadically, are not scheduled, and in principle should be unpredictable for company outsiders.
We calculate the share of short-sellers total pro t that can be attributed to each event. To do so, we rst compute the pro t of each short-selling deal. We then determine whether an event occurred during the life span of each deal.19 Each short-selling deal can fall in one
of the following four groups: i) disclosure of one or more negative earnings have occurred during the deal, ii) disclosure of one or more negative material facts have occurred, iii) disclosures of both types of events have occurred, and iv) no disclosures have occurred. Finally, we aggregate pro ts within each group to calculate the share of short-sellers total pro t attributed to each event.
17According to Brazilian regulation, important corporate news announcements must the disclosed
imme-diately by the rm (usually referred to as material facts), and any company insider endowed with this information is prohibited to disclose it privately or to trade upon it. Because of their nature largely unan-ticipated and containing important information about the rm not yet impounded into prices , material facts are important piece of news that aects prices signi cantly. Examples of material facts include proposal of mergers, changes of CEOs, and announcements of secondary public oering of shares.
18To classify these announcements as negative, we follow Engelberg, Reed, and Ringgenberg (2012) and
look at what happens with the stock price on the day the information is made public. We say that an announcement is negative if on the day they are disclosed the stock price declines by more than one ( rm-speci c) standard deviation.
Figure 8 shows the share of short-sellers total pro t attributed to each event. The chart on the left shows the shares for P S-institutions and the chart on the right for P S-individuals. For P S-institutions, we nd that 19.9% of their total shorting pro t (their total pro t in the 3-year period was US$ 2.2 billion) came from deals during which a negative earning an-nouncement occurred, 17.1% from deals during which a negative material fact anan-nouncement occurred, and 3.2% from deals during which both events occurred. For P S-individuals, we nd that 12.8% of their total shorting pro t (their total pro t in the 3-year period was US$ 50 million) came from deals during which a negative earning announcement occurred, 6.9% from deals during which a negative material fact announcement occurred, and 3.4% from deals during which both events occurred.
[Figure 8 about here]
Christophe, Ferri, and Angel (2004) and Engelberg, Reed, and Ringgenberg (2012) show that aggregate short-selling increases prior to negative rm-speci c events. Consistent with these results, we nd that a signi cant portion of the short-sellers pro t can be indeed at-tributed to negative earnings announcements and negative material fact announcements. However, it is important to emphasize that the greater portion of short-sellers pro t is not dependent on the anticipation of these two types of rm-speci c events (for institutions, 59.8%; for individuals, 76.9%). In particular, if total realized pro t were heavily dependent on negative material fact announcements (which are not scheduled, and should be unpre-dictable for company outsiders), the practice of insider trading could potentially be the most important source of short-sellers pro t as opposed to genuine shorting skill.20
20See Berkman, McKenzie, and Verwijmeren (2017) for recent evidence on how short-sellers may exploit
4.2 Do P S-short-sellers follow short-term contrarian or momentum
strategies?
To answer this, we run stock-day panel regressions of the number of investors selling short stock s on day t (Ns,t) on the past performance of stock prices. We measure stock s past
performance in two dierent ways: (i) its 5-day past return, rets,t−5→t−1; and (ii) dummy
variables indicating the quintile stock s belongs to on day t with respect to rets,t−5→t−1.
Be-cause the number of investors can vary across stocks for other reasons than past performance, such as trading volume, number of shares outstanding, and lending market conditions, we include in all regressions stocks xed-eects.
The results presented in Table 9 show that P S-short-sellers follow short-term momentum strategies. In contrast, other short-sellers follow short-term contrarian strategies. Columns (1) and (2) show that P S-institutions step up on days and on stocks that have experienced recent price falls. In contrast, Column (4) shows that other institutions tend to short stocks that are in the top two quintiles of rets,t−5→t−1. A similar pattern arises for individuals.
Column (5) shows that P S-individuals tend to short stocks that have experienced recent price falls. In contrast, Column (7) indicates that other individuals tend to trade in stocks after recent price increases and Column (8) shows that they avoid stocks in the lower quintiles of rets,t−5→t−1 and that they prefer stocks in the top quintiles of rets,t−5→t−1.
[Table 9 about here]
In a market with momentum stocks, a short-seller that follows short-term momentum strategies is likely to pro t. This makes the in-sample result that P S-short-sellers follow short-term momentum strategies less interesting. To evaluate whether P S-short-sellers are intrinsically short-term momentum traders, we run regressions with the same 1,000 cross-sectional sample splits of Section 3.2. As before, each out-of-sample exercise is based on the
construction of two groups of stocks randomly selected, groups A and B. To assign stocks to either group, we rst divide all stocks in our sample into four quantiles according to their total shorting volume during the full sample period. We then randomly assign one half of the stocks in each quantile to group A and the other half to group B. By doing so, we end up with two random groups of stocks with similar shorting volume. Using only deals on stocks from group B, we then run stock-day panel regressions of the number of investors selling short stock s on day t (Ns,t) on rets,t−5→t−1. Short-sellers are classi ed as P S-short-sellers
using stocks from group A.
Figure 9 shows four histograms of the 1,000 estimated coe cients of the regression. The top-left histogram refers to P S-institutions, the bottom-left to other institutions, the top-right to P S-individuals, and the bottom-right to other individuals. The out-of-sample results are consistent with the in-sample results for all groups, except for other institutions. The out-of-sample estimates strongly indicate that other institutions also follow short-term momentum strategies. Only other individuals are short-term contrarians: 701 out of the 1,000 estimates are positive, what suggests that these investors increase shorting activity following higher rets,t−5→t−1.
[Figure 9 about here]
We conclude that both P S-institutions and P S-individuals sell short stocks that have been experiencing recent price falls. This suggests that, as informed short-sellers, they contribute to the price discovery process by accelerating the incorporation of negative in-formation into stock prices, instead of by avoiding short-term price overshooting.21 This is
consistent with the evidence by Yan and Zhang (2009) who show that skilled institutionsi.e.
21There is robust evidence that short-sellers contribute to price e ciency. Sa and Sigurdsson (2011)
nd that stocks with higher short-sale constraints, characterized by low lending supply, display lower price e ciency. Engelberg, Reed, and Ringgenberg (2013) nd that stocks with more short-selling risk have less price e ciency. Bris, Goetzmann, and Zhu (2007), Boehmer and Wu (2013), and Purnanandam and Seyhun (2017) relate short-selling with price discovery.
institutions whose trading activity predicts future returnstend to trade at short-term hori-zons and follow momentum strategies.
4.3 The disposition eect
The disposition eect refers to the tendency of investors to ride losses and realize gains. This asymmetric behavior across gains and losses is consistent with investors having loss-averse type of preferences (see for instance Tversky and Kahneman, 1991, and Barberis and Xiong, 2009). At least since Shefrin and Statman (1985), many papers have documented the dis-position eect in nancial markets. Using proprietary data from a large discount brokerage house, Odean (1998) nds that individual investors are strongly aected by the disposition eect. Using an extensive data set from Finnish households, Grinblatt and Keloharju (2001) also document the disposition eect among individual investors. Using data from the Trea-sury Bond futures contract at the Chicago Board of Trade, Coval and Shumway (2005) nds that professional investors are also subject to the disposition eect and that are highly loss-averse. Locke and Mann (2005) show that futures traders tend to ride losses, although the authors argue this behavior does not aect their overall trading performance.
Regarding short-sellers, Beschwitz, Bastian, and Massa (2015) use a data set on equity lending on US stocks to show that short sellers are more likely to close a position when capital gains are higher, indicating the presence of disposition eect. Beschwitz, Bastian, and Massa (2015) do not dierentiate skilled short-sellers from other short-sellers, however. We use our deal-by-deal data set to test the presence of the disposition eect among short-sellers. We run deal-level regressions where the dependent variable is a dummy that equals one if deal i was terminated by the short-seller earlier than the original expiration date (T erminatei). In 99.2% of the loan contracts in our data set the borrower has the
option to return the stock to the lender earlier than the original expiration date in the loan contract; the early-return process is automatic and costless.
a deal if the stock price falls during the rst few days after the short sale. Analogously, she will be tempted to hold the short position on if the stock price rises. Accordingly, to test the existence of disposition eect among short-sellers, we regress T erminatei on the stock
return during the rst ve days after the opening of the deal (reti,5).22 That is, for each
group of short-sellers, we run the following deal-level regression:23
T erminatei = β0+ β1× reti,5+ i (1)
Table 10 reports the results. As expected, P S-short-sellers do not display disposition eect. After initially unfavorable returns, both P S-individual and P S-institutions actually tend to terminate the shorting deal earlier, contrary to what the disposition eect would induce. Columns (1) and (3) show that an increase in 10% in prices during the rst ve days of a deal increased by 0.1% the probability of an early termination for P S-institutions and by 2.6% for P S-individuals. In contrast, other short-sellers behave according to what is predicted by the disposition eect. Columns (2) and (4) show that an increase in 10% in prices during the rst ve days of a deal decreases by 0.8% the probability of an early termination for P S-institutions and by 1.6% for P S-individuals.
[Table 10 about here]
22To avoid polluting our analysis with deals that were originally schedule to last only few days, we include
in our regressions only deals that were originally set to expire in 10 days or more. Additionally, we also exclude deals with atypical long horizons (35 days or more) which are likely to be terminated earlier for other reasons. Deals with less than 10 days account for 1.6% of all deals, and deals with more than 35 days account for 8.2% of all deals.
23To estimate the coe cients in regression 1, we include only deals with the early return option to the
borrower. Additionally, to ensure that changes in the original duration of the deal are made by short-sellers, we also exclude deals that allow the lender to recall the stocks earlier. Of all 3,341,213 deals in our data set, 67.8% allow for both, short-sellers and lenders, to terminated the lending contract earlier, 31.4% allow for only short-sellers to return the stocks earlier, and 0.8% does not allow for neither short-sellers and lenders to terminate the lending contract earlier. There are no deals that allow only the lender to recall the stocks earlier.
Once again, out-of-sample results are important. In a market with momentum stocks, the disposition eect can be directly related to poor performance. The existence of momentum implies that price increases (decreases) tend to be followed by a sustained period of price increases (decreases). If a short-seller initiates a short position in a momentum stock, the disposition eect would induce her to continue (terminate) a losing (winning) position and to realize larger losses (smaller gains).
Hence, to address concerns that P S-investors performance is mechanically related to the disposition eect, we reproduce our analysis out-of-sample. As in Section 3.2, we run the disposition eect regression in each one of the 1,000 samples randomly constructed. The histograms in Figure 10 present the empirical distribution of the estimated coe cients. As expected, individuals that are not potentially skilled are the most susceptible ones to the disposition eect. Out of the 1,000 estimates of β1, 940 are negative. In contrast, only
372 estimates of β1 are negative for P S-individuals. With respect to institutions, most of
the estimates are close to zero for both P S-institutions and other institutions, although a slightly higher mass of negative β1 estimates can be observed for other institutions.
[Figure 10 about here]
5 Conclusion
We document the existence of short-selling skill among both institutions and individuals. This result contributes to the broad literature that searches for investment skills among market participants and, more directly, to the speci c literature on short-selling. Very few is known at the short-seller level. Boehmer, Jones, and Zhang (2008) nd that the average institution is more informed than the average individual. Jank and Smajlbegovic (2015) nd that hedge funds outperform other short-sellers. Kelley and Tetlock (2017) documents that
aggregate short-selling by individuals predicts lower returns. Wang, Lee, and Woo (2017) nd that individuals who sell short more rms make higher pro ts and that their pro tability is persistent over time.
We are the rst to use market-wide investor-level data to provide comprehensive evidence on short-selling skill. We nd that from a total of 4,107 institutions, 367 (8.9%) consistently pro t from shorting. They are responsible for 31.5% of the total shorting volume and 26.7% of the total number of shorting deals. From a total of 35,338 individuals, 1,484 (4.2%) con-sistently pro t from shorting. They are responsible for 0.3% of the total shorting volume and 2.5% of the total number of shorting deals. Importantly, these investors present robust out-of-sample performance persistence. To show this, we explore the fact that our performance analysis is at the deal-level and study persistence across stocks.
We examine in more detail the trading activity of the short-sellers who consistently pro t. We rst show that the greater portion of their realized pro t (for institutions, 59.8%; for individuals, 76.9%) is not dependent on the anticipation of negative earnings and material facts announcements. This is relevant. In particular, if total realized pro t were heavily dependent on negative material fact announcements (which are not scheduled, and should be unpredictable for company outsiders), the practice of insider trading could potentially be the most important source of short-sellers pro t as opposed to genuine shorting skill. We then show that skilled short-sellers follow short-term momentum strategies. This suggests that they contribute to the price discovery process by accelerating the incorporation of negative information into stock prices, instead of by avoiding short-term price overshooting. We nally show that, dierently from the average short-seller, skilled short-sellers do not display the disposition eect.
The Brazilian stock market, such as other modern but not so big stock markets, oers a good laboratory for empirical studies in nance. From a computational perspective, the size of the Brazilian market facilitates market-wide empirical analyses at the deal-level. Impor-tantly, standard empirical facts for the equity lending market and short-selling documented
for the US and Europe also hold in Brazil, ensuring external validity of our results. For instance, the short-selling share in total volume is similar (see Section 2), aggregate shorting also predicts future returns (see the aggregate predictability evidence in Section 3.2) and the loan market is also opaque and works in a similar way (Chague, De-Losso, Genaro, and Giovannetti, 2017).
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