Is Transparency in the Equity Lending Market Good News?

Is Transparency in the Equity Lending Market Good News? Zsuzsa R. Huszár Ruth S. K. Tan Weina Zhang 1 This draft: June 4, 2015 Abstract Using novel U.S. equity lending market data from 2007 to 2010, we find that stock lending fees predict future returns beyond shorting demand. We link this change in the predictability of fees to a regime shift in the stock lending market following the collapse of Lehman Brothers. We provide direct evidence of proactive pricing behavior in the lending market and show that active lenders price in the expected future demand in addition to realized demand before earnings announcements. These results suggest that while lenders may benefit from managing the lending fees proactively, they are also likely to create more binding short-sale constraints during news events. JEL classification: G10, G12, G14 1 All authors are from NUS Business School, at the National University of Singapore (NUS), Mochtar Riady Building, 15 Kent Ridge Drive, Singapore 119245. Huszár and Zhang are also affiliated with the Risk Management Institute (RMI) and the Institute of Real Estate Studies (IRES) at NUS. Contact author s email: bizhzr@nus.edu.sg, phone: +(65) 6516-8017, fax: +(65) 6779 2083. Huszár thanks Sungard for the invaluable Sungard Astec Analytics data and support from Sungard s sales and analytics team. The authors would like to thank Ekkehart Boehmer, Darrell Duffie, Truong X. Duong, Allaudeen Hameed, David Hirshleifer, Burton Hollifield, Charles Lee, Alexander Ljungqvist, Wenlan Qian, David M. Reeb, Matthew Ringgenberg (discussant), Pedro Saffi, and Hong Zhang (discussant) for comments and suggestions. We would also like to thank the participants at the 2014 China International Conference in Finance and at the 2014 Financial Management Association (FMA) meeting. 1 Selling a stock short without fulfilling the locate requirement is considered a violation of Reg. SHO except for shorts by market makers for bona fide market making. This locate requirement mandates that traders prime brokers must ensure in advance that the stock sold (where the short sellers does not initially own the security) will be available (can be borrowed) to fulfill the trade settlement.

Keywords: Institutional ownership; Securities lending market; Short sales; Short-sale constraints Is Transparency in Equity Lending Market Good News? Abstract Using novel U.S. equity lending market data from 2007 to 2010, we find that stock lending fees predict future returns beyond shorting demand. We link this change in the predictability of fees to a regime shift in the stock lending market following the collapse of Lehman Brothers. We provide direct evidence of proactive pricing behavior in the lending market and show that active lenders price in the expected future demand in addition to realized demand before earnings announcements. These results suggest that while lenders may benefit from managing the lending fees proactively, they are also likely to create more binding short-sale constraints during news events. JEL classification: G10, G12, G14 Keywords: Institutional ownership; Securities lending market; Short sales; Short-sale constraints

1. Introduction In recent years, the hedge fund industry has experienced unprecedented growth, with assets under management reaching $2 Trillion in 2011/2012 (Forbes, 2012). Hedge fund managers generally actively manage the funds and aggressively participate in short selling either to create market neutral portfolios and/or to exploit negative firm specific information. The recent regulations (e.g., Regulation SHO, 2004) and the 2008 naked short sale ban requiring stock borrowing prior to the short sale stimulated the demand in the securities lending (hereafter seclending) market. 1 On the supply side, institutional lenders (e.g., large institutional investors, such as mutual funds, trusts, and custodians) eagerly responded, looking for additional income from their passive portfolio (Evans, Ferreira, and Porras-Prado, 2014; SEC, 2014). Even pension funds pay close attention to their income from securities lending, as suggested by the recent lawsuit filed against Blackrock for systematically looting securities lending revenues from investors (Bloomberg, 2013; Financial Times, 2013). The failure of Lehman Brothers, a large prime broker and a key participant in sec-lending transactions was a wake-up call for many lenders. Institutional investors suddenly become more aware of the risks due to counterparty failure of trusted brokerages as well as the potential of returns that can be realized on well managed and indemnified assets. In the aftermath of the 1 Selling a stock short without fulfilling the locate requirement is considered a violation of Reg. SHO except for shorts by market makers for bona fide market making. This locate requirement mandates that traders prime brokers must ensure in advance that the stock sold (where the short sellers does not initially own the security) will be available (can be borrowed) to fulfill the trade settlement. 1

Lehman collapse, it became difficult for fund managers to realize good return on traditional long strategies and many turned to sec-lending to generate income from passive holdings. In this study, we examine stock lending fee dynamics to test whether increased involvement of lenders in sec-lending alters the formation of lending fees and thereby the information content in fees. Specifically, we test whether lending fees are informative about future returns and whether lending fees are lower with increased competition and transparency. Traditionally, the sec-lending market was viewed as a major impediment to shorting because of high search costs and the oligopolistic market setting (Duffie, Garleanu, and Pedersen, 2002; Kolasinski, Reed and Ringgenberg, 2013). Interestingly, Autore, Boulton, and Braga-Alves (2015) and Fotak, Raman and Yadav (2014) show that short sellers can circumvent high stock borrowing costs by exercising failures-to-delivers (FTD) and able to correct mispricing with active naked shorting. In response to the regulatory changes with the Dodd-Frank Act, Duffie, Dworczak, and Zhu (2014) propose a new model for the OTC market where by providing more transparency with single benchmark rate the borrowing costs can be reduced.the reduced fees manifest in the buyside market where borrowers may be in a better position to bargain down the fees with increased transparency. However, Duffie et al. (2014) s model may not be applicable to a segmented seclending market where lenders aim to raise income from lending. Since borrowers (e.g., short sellers) often trade on time sensitive information, they maybe not be in good bargaining position against informed lenders. In this case the increased transparency would aid the bargaining position of lender and result in higher fees. This latter behavior is consistent with findings in executive compensation literature, where greater disclosure leads to higher CEO pay (Balsam, et al. 2014; Hermalin and Weisbach, 2012; Lu and Shi, 2013). Thus, ex ante the effect of increased transparency on fees and how the changes in fees feed back to the stock market are unclear. 2

We have two main findings about the U.S. equity lending market from 2007 to 2010. First, our results show that the lending fees (i.e., the income lenders receive) are significantly related to future stock returns after controlling for realized demand. This result is different from prior studies which suggest that fees predicts negative returns because it captures high realized borrowing demand. We find that the return predictability of the lending fees is stronger after the Lehman collapse, suggesting a regime shift in the market. Secondly, to explain this return predictability of lending fees we link lending fee dynamics to active institution participation. For stocks where lenders are likely to be active institutions, the lending fees are more sensitive to demand and lenders are not only reactive but proactive in pricing. For example, before negative earnings news, the more informed lenders (e.g., insiders) may want to sell the stocks but are prohibited from doing so before the public earnings announcement. They may want to raise lending fees before the announcement to deter or delay shorting. Subsequently, they can legally liquidate their position at a better price after the announcement. This proactive behavior is likely more prevalent after the crisis as lenders explore new opportunities in sec-lending in the changing regulatory environment. Our study provides two important contributions to the literature. First, proactive pricing behavior of equity lenders has not been documented previously. Extant studies show that borrowing costs (i.e. loan fees) are insensitive to moderate changes in demand (Kolasinski, Reed, and Ringgenberg, 2013) and only respond to large demand shocks (Blocher, Reed, and Wesep, 2013). To disentangle the supply and demand effects on loan fees, Kaplan, Moskowitz, and Sensoy (2013) show that an exogenous shock to lending supply significantly raises the equity loan fees, but supply shifts convey no information about future returns. According to Cohen, Diether, and Malloy (2007), supply shocks are only informative about future returns when 3

combined with demand shocks. Taken together, these studies suggest that loan fees (cost to borrower) mainly capture past (and current) high shorting demand (when supply is constrained), while our findings suggest that lending fees (income to lender) provide additional information about future stock returns beyond the currently observable shorting demand. Second, our findings suggest that in the newly transparent lending market, short sale constraints are more binding when the lenders are likely active institutions around news events. While existing studies such as Saffi and Sturgess (2012) find that institutional ownership relaxes short sale constraints, our findings are more consistent with Porras-Prado, Saffi, and Sturgess (2014) who argue that concentrated institutional ownership drives short sale constraints. Augmenting prior evidence that short sellers tend to be active around corporate events (Berkman and McKenzie, 2012; Christophe, Ferri, and Angel, 2004; Boehmer and Wu, 2013; Engelberg, Reed, and Ringgenberg, 2012), we find proactive pricing behavior by active institutional lenders around negative earnings announcements and more so after the recent financial crisis. Our findings show that lenders expecting negative news will constraint short selling to reduce price impact before they can trade. This results in more binding short selling constraints when short selling is especially important to incorporate new information. The rest of the paper is organized as follows. Section 2 describes the recent development of the equity lending market and proposes testable hypotheses. Section 3 describes the data employed and research methodology. Section 4 presents the empirical findings and section 5 concludes. 2. Review of the Equity Lending Market and Hypothesis Development 2.1. Institutional Background of Equity Lending Market 4

In a traditional equity lending transaction, a stock borrower pays loan fees directly to the lender or to the agent/broker and post collateral of about 150% (100% proceeds + 50% margin). Although there are no fixed rules on the division of income, institutional lenders on average receive about 60-70% of the borrowing cost as lending fees (or loan fee). In this study, we define lending fees as the income that lenders directly from the borrower in bilateral transaction or receive after the brokers cut in triparty agreement (examples of lending fee establishments are included in Appendix I). 3 The potential income from securities lending is a non-negligible amount which is increasingly important as an alternative source of revenue for institutions. In our data sample of stocks which constitutes only a fraction of the U.S. market (after merging with institutional ownership and earnings news data), we find that on average the annual lending fee income is about $600 million during the period of 2007 to 2010. Fund managers of more passive funds, such as index funds and pension funds, realize that fees on their idle assets through fullycollateralized loans are almost universally profitable (used to cover administrative costs and complement portfolio returns) even during market contractions. Even ERISA funds, state and local treasuries, insurance companies, central banks and other institutional investors are also active in the sec-lending market globally (see for example, Australian Securities Lending Association, 2012; Vanguard, 2009; Citibank, 2013) In recent years, the global sec-lending market has become more transparent. It is estimated that the equity lending market encompasses more than $1.8 trillion securities worldwide in 2012, 3 Traditionally a 40/60 cut is applied between agent and lender on the fees received from borrower. In more recent years, the agents share has shrunk to 20/80 and in some cases even to a 10/90 with the increased transparency and sophistication of lenders (GFOA, 2009; Clearstream, 2014). 5

fuelled by increased institutional trading (FSB, 2012). Responding to industry demand, seclending data and solution providers, such as Markit (formerly Data Explorers) and SunGard have increased their coverage in terms of the number of products and frequency of information provision. Today, these sec-lending market data and solutions are essential not only for borrowers but also for lenders who want to actively manage passive holdings to maximize revenues. 2.2. Development of Research Hypotheses Our main research question focuses on the pricing behavior of equity lenders in the context of the recent development in sec-lending. Traditionally, sec-lending is a passive activity where pension funds and trusts don t influence fees because they auction off their holdings to large brokerages and securities firms who will lend those assets out and manage the collaterals. Such passive behavior can be explained by limited information on borrowing demand in a nontransparent OTC market. However, in recent years data vendors such as SunGard have made significant improvement in providing daily (since 2005) and intraday information (since 2011) on equity and debt securities worldwide. The timely and reliable information not only enable borrowers to locate securities faster but also allow lenders to observe demand and supply on a real time basis and charge more competitive prices. Theoretically, Duffie, Gârleanu, and Pedersen (2002) model endogenous short-sale constraints in the sec-lending market with a search model where lenders rationally set high loan fees for assets with high investor disagreement and/or uncertainty. Empirically, D Avolio (2002) and Geczy, Musto, and Reed (2002) show that on average sec-lending fees are relatively small compared to the abnormal returns arising from well-known trading strategies, such as momentum trading. These studies also find that lending fees can be extremely high for 6

small/illiquid stocks with high demand and suggest that high lending fees (or negative rebate rates) predict returns because of the high shorting demand driven by the private negative information from short sellers. In this study, we focus on the lenders role in the equity lending market, first by examining whether lending fees contain additional information beyond observable shorting demand. We do so by testing whether lending fees reflect only past and current shorting demand as suggested by earlier short sale studies. We posit that lending fees contain additional information because lenders become more active in managing their lending desks, thereby revealing new information. The Lehman collapse revealed the significant counterparty risk in sec-lending and more importantly the benefits of these transactions. After the initial shock of the prime broker failure, lenders have been able to fully cover their positions by liquidating collaterals. Many market participants have come to realize that sec-lending provides an important low-risk revenue stream even in crisis conditions because the contracts are fully securitized. This low risk low return opportunity is surprisingly attractive in the highly volatile market conditions after the Lehman collapse. We posit that lenders are more active and incorporate not only the past and current shorting demand, but also the expected future lending demand in their lending fees as suggested by Duffie et al. (2002). Such pricing behavior will naturally affect the stock valuation beyond its fundamental value (Porras-Prado, 2015). This leads to our first testable hypothesis: Hypothesis 1: Lending fees predict future stock movements even after controlling for the current shorting demand, especially after the Lehman collapse. If we find empirical support for Hypothesis 1, building on Duffie et al. (2002) s work, we conjecture that the fees return predictability can be attributable to the active pricing of 7

anticipated future shorting demand in addition to the current and past demand. Our conjecture is based on two observations. First, reliable timely information on sec-lending is now available; and second, institutions are increasingly driven to realize revenues from passive holdings in the aftermath of the financial crises. In the empirical analyses, we use total realized borrowing demand (i.e., total number of shares out on loan, combining all shares from all borrowing contracts that have not yet closed out) to measure past shorting demand and the newly borrowed shares (i.e., the number of shares newly borrowed) on the last trading day to measure new shorting demand. In the first step, we examine lending fee dynamics in relation to shorting demand and institutional ownership. In addition to aggregate institutional ownership, which is an accepted proxy for lending supply (Asquith, Pathak, and Ritter, 2005), we use another variable to proxy for the economic significance of active institutional investors. Specifically, we use the ratio of active institutional ownership such as mutual funds, hedge fund, and advisory firms (Type 3 and 4 in 13 filings) divided by total institutional ownership. At the stock level, this measure also captures the pricing behavior of specific equity lenders who are likely to be active institutions as well. If lending fees are positively related to the expected future demand and active equity lenders are more likely to incorporate such demand, lending fees are expected to be positively related to the participation of active institutional investors. This leads to our second testable hypothesis (H2A): Hypothesis 2A: Lending fees are more responsive to demand in the presence of active institutional investors, more so after the Lehman collapse. To confirm that the link between the lending fees and active institutional investors participation is due to the future demand, we adopt a natural experiment setting to proxy for 8

expected higher future demand around earnings announcement. This leads to the next testable hypothesis (H2B): Hypothesis 2B: Lending fees are significantly more related to shorting demand, especially around corporate events when higher shorting demand is anticipated. First, we directly test whether expected future demand (proxied by the realized future demand) is priced in by active institutions. However, institutions may not have perfect foresight and may price in only their expectations which is unobservable. Therefore, we adopt the earning announcements as a natural experience event, with 50-50 day trading window before and after the earnings news release. We focus on the earnings announcement window because prior studies show that short sellers are especially active around corporate events (Christophe, Ferri, and Angel, 2004; Boehmer and Wu, 2013; Boehmer, Jones, and Zhang, 2012; Engelberg, Reed, and Ringgenberg, 2012). Blau and Wade (2012) find that short sellers are actually more speculative rather than informed around events, such as analysts upgrade/downgrade decisions, which suggest that while short sellers may not be always informed, they are clearly more active around information events. We also differentiate between positive and negative announcements because large institutional investors may have been informed about the earnings information weeks in advance during the board meetings. While the insiders (i.e., large institutional investors who are the lenders normally) are prohibited from trading on news before the public announcement, they can try to hinder short selling by raising lending fees. 3. Data and Summary Statistics 3.1. Data 9

We use several data sources to construct our final sample. We obtain daily stock returns, daily trading volumes, prices, and number of shares outstanding for common U.S. stocks from the Center for Research in Security Prices (CRSP) daily files. This return dataset is supplemented by company-specific financial information, such as book value of equity from Compustat s annual industrial files and earnings announcement dates and earnings news from Institutional Brokers' Estimate System (I/B/E/S). The included stocks need to have at least one earning news announcement from I/B/E/S. Institutional ownership data are obtained from 13f files of the following five institution types namely (1) banks, (2) insurance companies, (3) investment companies, (4) investment advisors, and (5) all others including pension funds, university endowments, and foundations. We merge the ownership information at the quarterly frequency, based on quarterly reporting, and exclude all stocks without coverage from CRSP, or 13f or without sec-lending information from SunGard Astec Analytics. 4 While a number of recent studies (e.g., Saffi and Sigurdsson, 2011; Kolasinski, Reed, and Ringgenberg, 2013) use sec-lending market data, their primary data source is Markit s Securities Financing data. To our knowledge, we are the first to explore SunGard Astec Analytics data in an academic study. It is slightly different from Markit s sec-lending files which focuses on the supply side. SunGard Astec Analytics provides more comprehensive daily data on borrowing volume, lending fees, differentiating across the types of collaterals and the types of loans (overnight versus term loans), especially for U.S. firms. In Figure 1, we show that SunGard s U.S. market coverage is stable during our sample period. Since the dataset is newly explored in 4 We have to assume zero institutional ownership because of missing data less than 5% of the time. Excluding these observations from the sample do not change the economic implications of our results. 10

academic research, we implement filter screens to reduce data errors, such as lending in excess of supply, or misreported fees. Moreover, we manually validate all extreme fee observations, where the annual fee is in excess of 50% (fees reported in % of the daily close price). Our final sample consists of 2,347,779 stock-day observations for 3,766 unique stocks listed on major U.S. equity markets (Amex, NYSE, and NASDAQ) from January 2007 to December 2010. While we would like to explore the increased transparency, we are unable to examine a regime switch as SunGard Astec Analytics provides real time data only from 2013. 3.2. The Construction of Key Variables Our key variable is the value weighted average securities lending fee, Allfee, in percentage term. We define lending fees (income to lender) as the generic loan rate or the premium on non-cash loans, or the rebate rate on cash-collateralized loan. This rate is inferred from the broker-lender transaction from the wholesale market instead of the broker-short seller (borrower) transaction from the retail market (SunGard, 2009). It reflects both the supply and demand side. In the later analysis, we will disentangle the two sides by explicitly controlling for observable past and current shorting demand. In the regression analysis, we use the natural logarithm of the annualized fees, Logallfee, to reduce the skewness (Gagnon, 2014). To proxy for stock-level aggregate shorting demand in the past, we define AggSIR as the total number of shares out on loan on a trading day divided by the total shares outstanding. Although stocks are borrowed for a number of reasons, such as voting, dividend arbitrage, funding trade, and collateral swaps, the primary reason is for short selling (Clearstream, 2014), therefore our AggSIR measure is comparable to the SIR measures in extant short sale studies, for example (e.g., Asquith, Pathak, and Ritter, 2005; Boehmer et al. 2010). We also define a dummy variable, HighSIR, which equals 1 for stocks with AggSIR from the top decile of the distribution on a 11

trading day. We use a dummy variable for high shorting demand because earlier studies (e.g., Desai, Ramesh, Thiagarajan, and Balachandran, 2002; Asquith et al 2005) show that the information content in short selling is concentrated in the extremes. We also proxy for the current shorting demand, SIR new, which is defined as the new borrowings (i.e., recently lent out shares) divided by the total shares outstanding on a trading day. Similar to the aggregate shorting demand measure, we also define a dummy to capture high new shorting demand where the HighSIR new takes on the value of 1 for stocks SIR new from the top decile. To test the role of active institutional investors, we use 13f filings aggregate institutional ownership data (IOall) by combining all five types of institutions total shareholding. IOall captures aggregate institutional holdings relative to the total shares outstanding. Following Abarbanell, Bushe, and Raedy (2003) and Cheng, Hameed, Subrahmanyam, and Titman (2014), we combine two ownership types, namely investment companies and investment advisors ownership (this classification includes mutual funds and hedge funds) to create an active institutional ownership proxy, IOact, which is defined as the aggregate active institution holding relative to the total shares outstanding. To explicitly measure the presence of active institutions in the specific stock s loan supply market, we create a FracActIO measure which is the ratio of the number of shares held by active institutions relative to total number of shares held by institutions. The FracActIO can be interpreted as follows: if FracActIO is greater than 0.5 then more than 50% of the shares that are held by institutions are held by active institutions, and thus, the lenders are likely active institutions. Our control variables are constructed by following Boehmer, Jones, and Zhang (2008). We define the variables that are related to long-term and short-term return predictability. They include size (LogSize), book-to-market ratio (BtoM), turnover (DailyTurn -1m ), and past-month 12

return volatility (RetStd -1m ). We also use alternative liquidity and volatility controls such as the average high-low price spread (HLspread -1m ), and bid-ask spread (BAspread -1m ) for the previous month. The high-low price spread is the difference between the daily highest and lowest prices divided by the highest price of the day, while the bid-ask spread is the difference between the daily ask price and the bid price relative to the average bid-ask price. 3.3. Summary Statistics and Time-series Panel A of Figure 1 shows the time-series of average shorting demand, measured by the short interest ratio (SIR) and the number of stocks in the sample (on the right axis). On average we have about 3,000 stocks daily. Panel A shows that the average SIR, AggSIR (reported 2.98% in Table 1) is relatively stable during the sample period, ranging between 1.80% and 3.2%. This indicates that the realized shorting demand does not vary significantly over time. [Figure 1 is about here] In contrast, Panel B in Figure 1 shows that the aggregate lending fee income varies significantly over time, indicating that the fluctuation in lending fees may be driven by factors other than the observed shorting demand, which is relatively stable as shown in Panel A. We define the total lending fee income as the product of the number of shares out on loan and the share price and the relevant daily lending fees. Figure 1 Panel B shows that there is a peak in lending fee income around the Lehman collapse, possibly due to anticipated increase in the counterparty risk among prime brokers and sec-lending agents. Another peak in the lending income occurred following as the U.S. stock market touched bottom in March 2009. [Table 1 about here] We present the summary statistics of these key variables in Table 1. Panel A covers the entire daily sample from January 2007 to December 2010, while Panel B and Panel C report the 13

summary statistics before and after the Lehman collapse in September 2008. Panel A in Table 1 shows that on average the AggSIR is 2.98% (median 1.86%) which is comparable with that reported by Boehmer, Huszár, and Jordan (2010). The average SIRnew -1 of 0.15% indicates that about 0.15% of the total shares are borrowed each day, anew. In the regression setting we use the last day of information, reflected by the subscript -1. We note that the average SIVnew -1 is 0.29, suggesting that daily new borrowings are equivalent to about 29% of the daily turnover. This value is consistent with Diether, Lee, and Werner (2009) who report that 24% of NYSE trading volume is associated with short selling. The average aggregate institutional ownership (IOall) is 58.4%, while the active institutional ownership (IOact) is about 23.5%. Since institutional holding, proxy for lending supply, is over 50% of total shares while the borrowing is only 3%, proxied by AggSIR, short sale constraints on average are non-binding in our sample as was also noted by previous studies (D Avolio, 2002; Asquith, Pathak, and Ritter, 2005). In Panels B and C of Table 1, we present the summary statistics for the subsamples before and after the Lehman collapse, which span from January 2007 to August 2008 in Panel B and from October 2008 to December 2010 in Panel C respectively. We omit the event month, September 2008 in the subsample analysis. We observe a decrease in shorting demand over time (i.e., the average AggSIR declines from 3.42% in Panel B to 2.66% in Panel C) and an increase in lending fees, Allfee, from 0.85% in Panel B to 1.0% in Panel C. We also observe an increase in daily stock return volatility and decrease in liquidity (see increase in the BAspread measure) after the Lehman collapse. The unfavorable market conditions after the Lehman collapse, as the market declined for months, encourage equity lenders to look for safe income from fully collateralized sec-lending. The lenders can also exploit the change in short sale regulations, as the naked short sale ban led to more borrowing demand for stocks by short sellers. 14

4. Empirical Analysis In this section, we test our hypotheses stated in Section 2.2. Hypothesis 1 posits that lending fees, which reflect the pricing behavior of equity lenders, can predict future stock returns beyond the current observable shorting demand. Hypothesis 2A test whether the fees return predictability is attributable to active institutional investors role in the lending market through the expected demand channel. Lastly, Hypothesis 2B examines whether lenders price in future expected demand, higher than usual shorting demand, around earnings announcements. 4.1. Return Predictability of Lending Fees Following Diether, Lee, and Werner (2009) s research design to test short sale return predictability, we adopt Fama-MacBeth (1973) regression method. In addition to the short term return predictors used by Diether, Lee, and Werner (2009), we also control for stock return volatility as in Boehmer, Jones, and Zhang (2008). Table 2 presents the results. [Table 2 about here] Model 1 in Table 2 shows a significant negative relation between the future returns and the current shorting demand, HighSIR, at the 1% significance level, consistent with the prior literature (e.g., Boehmer, Jones, and Zhang, 2008; Diether, Lee, and Werner, 2009). A high aggregate shorting is associated with a negative future abnormal return of -69 basis points (bps) in a 20-day horizon. Even after controlling for aggregate past shorting demand, using the HighSIR measure, we find support for Hypothesis 1. There is a significant negative relation between lending fees, measured by Logallfee, and future returns at the 1% significance level in Models 2 through 5 in Table 2. Specifically, Model 2 shows that a 100% higher lending fees is associated with 9 bps 15

lower excess future returns, after accounting for stock characteristics. 5 This significant negative relation between lending fees and future returns is very robust across alternative specifications in Model 3 and Model 4 after controlling for new shorting demand that is observable on day t-1 with SIRnew -1 and HighSIRnew -1. The first variable, SIRnew -1, captures the continuous new shorting demand while the second variable, HighSIRnew -1, is a dummy that takes on the value of one for stocks with new shorting demand from the top decile of the distribution on day t-1, respectively. In Model 5, we also control for specialness of the stock, with the high fee dummy, and find that our result about the fee informativeness in the cross-section is not entirely driven by high fee stocks. In Panel B of Table 2, we show that the explanatory power of the lending fees is significantly stronger after the Lehman collapse that is during the sample period of October 2008 to December 2010. Now based on Model 2 results, we find that a 100% increase in lending fees is associated with 13 bps lower future returns (-0.426*(log1+%change in fees) = -0.426(log2) 0.13). Again, we show that the finding is not driven by stocks on special because in Model 5, we explicitly control for high lending fees. Thus, the return predictability of lending fees has become economically more important in recent years. Figure 2 depicts the time series coefficients on lending fees and clearly shows a regime shift after the Lehman collapse. [Figure 2 about here] Table 2 shows results for the control variables that are consistent with those in prior studies. For example, more volatile stocks underperform on average, consistent with the well 5 In the linear-log model of returns and lending fees, the coefficient estimate of -0.297 in Model 2 can be interpreted as follows: a 100% higher lending fees is associated with -0.297*log(1+%change in fees))= -0.297(log2) 0.09 lower future returns, after controlling for stock characteristics. 16

documented volatility anomaly (Amihud, 2002; Ang, Hodrick, Xing, and Zhang, 2006). Overall, we show that lending fees are informative about future stock prices beyond the past and current shorting demand, especially after the collapse of the Lehman Brother. In the next section, we explore in detail the key determinants of fees. We especially focus on the pricing of the realized and expected borrowing demand in relation to the participation of active and passive institutions (a form of supply dynamics). 4.2. Determinants of Lending Fees Given the supporting evidence for Hypothesis 1 from Table 2, we explore whether the return predictability of the lending fees have anything to do with the time-varying behavior of active institutional investors in the newly transparent and automated equity lending market. These are our Hypothesis 2A and 2B. Our dependent variable is the natural logarithm of annualized lending fees, Logallfee. We use panel regression to identify the determinants of lending fees with firm and time fixed effects. Table 3 reports the results. [Table 3 about here] Table 3 shows that the lending fee measure, Logallfee, is positively related to old and new shorting demand as expected. In general, institutional ownership relaxes short sale constraints by facilitating supply. However, when we consider shorting demand in conjunction with active institutional investors, we find that institutions create shorting constraint by charging higher fees. First, the significant positive coefficients on the HighSIR and HighSIRnew variables show that fees respond to demand. The interaction variable on HighSIRnew*FracActIO show that fees are even higher when lenders are active institutions. The coefficient estimate of 0.151 implies that a stock with 100% active institutional investors has 16.3% higher lending fees (i.e., 100(e 0.151-1) 16.3%) than a stock with 0% active institutional investors with a similar shorting demand. These 17

latter results provide support for our Hypothesis 2A that shorting demands are more actively priced in in the presence of active institutions. Panel B of Table 3 shows that the pricing implication of active institutions has become more important after the Lehman collapse. The coefficient estimate of 0.242 on the interaction variable HighSIRnew*FracActIO implies that a stock with 100% active institutional investors has 27.4% higher lending fees (i.e., 100(e 0.242-1) 27.4%) than a stock with 0% active institutional investors with a similar shorting demand. In general, we find that the binding short sale constraints arising from active institutions participation is only material for stocks with significant new shorting demand. In the absence of strong shorting demand or expected bad news, active institutions are less likely to engage in price setting because most stocks can be borrowed at the general collateral rate. 6 Overall, Table 3 suggests that active institutions play an important role in lending fees dynamics, supporting Hypothesis 2A that return predictability likely arises from the specific role of active institutional investors. The results show that lending fees may capture the proactive or dynamic pricing of active institutional investors in the lending market. To confirm this, we examine lending fee dynamics around earnings announcements to pin down the proactive behavior of active equity lenders, which is our Hypothesis 2B. 4.3. Determinants of Lending Fees around Earnings Announcements In this section, we use a natural setting, earnings announcement, to re-examine the pricing behavior of active lenders. Prior studies show that short selling is elevated around earnings announcements (Boehmer, Jones, and Zhang, 2012; Boehmer and Wu, 2013). 6 We present robustness analysis by controlling explicitly for loan supply in Appendix II through V. We do not incorporate supply in our main analyses because we do not have supply data for the entire sample period. 18

[Table 4 about here] Table 4 supports Hypothesis 2B that institutional investors play an important role in determining lending fees around earnings announcements. In the regression analyses, we examine lending fees with 50-50 calendar day window around earnings announcement. We find that lending fees are higher before announcements. With Models 1 and 2, we consider all announcements (both positive and negative announcements) and find that lending fees are higher on average before announcement after controlling for past shorting demand with aggregate shorting, and new shorting demand. With Models 3 and 4, we examine lending fees around negative news announcements and find that fees are significantly higher before these announcements. Finally with Models 5 and 6, we examine lending fees around nonnegative announcements and find no evidence that fees are higher before announcements. Apart from the earnings window effect on lending fees, the coefficient estimates on the active institutional ownership measures (FracActIO) also warrant attention. On average, the negative coefficient estimates on FracActIO suggest that active institutional ownership relaxes short sale constraints manifesting in lower fees. However the coefficient estimates in Models 3 and 4 are statistically insignificant, while in Models 5 and 6, the coefficient estimates of -0.336 and -0.338 are statistically significant at the 1 percent level. These results suggest that before negative news events, active institutional ownership does not relax short sale constraints but instead create constraints by not lowering fees. Overall, the combined effect that fees are raised before negative news and that active institutional ownership does not significantly reduce fees imply that lenders may raise fees before the negative announcement which will deter short selling. Still active institutions are willing to lend when there are no adverse news, as we find 19

that active institutional ownership is associated with lower fees in the subsample of nonnegative news announcements. Next in Panel B of Table 4, we reexamine lending fees in the same earnings announcement setting during the latter part of our sample period, after Lehman collapse. Our results show that lending fees are raised before negative announcements. In addition, we find that lending fees become more responsive to new shorting demand as suggested by the increased economic significance of the coefficient estimate on the SIRnew -1. Overall we find that lending fees are dynamic and respond to shorting demand especially in the presence of active institutional investors. We also show that lending fees may be raised if lenders price in new information about expected bad earnings or want to deter shorting. Our results are based on the sell side lending market (lenders market is analyzed here) which is separated from brokerages on the buy-side market (where short sellers borrow the stocks). However, the markets are connected. As lenders are increasingly active in pricing, the cost pressure may hinder short selling especially around earnings announcements where the incorporation of new information is imperative for facilitating pricing efficiency. We also examine the robustness of our results and explicitly control for loan supply (i.e., number of shares available for stock borrowing). We only include this supply in robustness tests because we do not have data for our entire sample period. In Appendix II through V, we present robustness results for our main analyses from Tables 3 and 4, including supply (fraction of total shares outstanding available for borrowing) as an additional control in the regression analyses. The results in these appendix tables are statistically and economically similar to those reported in the main tables suggesting that our results are robust and not driven by the omitted variable bias. 20

5. Conclusion In this study, we explore the pricing behavior of equity lenders in the U.S. equity lending market from 2007 to 2010. Traditionally, lenders are viewed as passive price takers in the OTC lending market where collaboration and timely pricing of demand is difficult in a non transparent setting. However, lenders, especially active institutional lenders have become more proactive in the newly transparent lending market where reliable daily demand, supply and pricing information is readily available. We provide new insights into the U.S. equity lending market dynamics. First, we find that lending fees predict future stock returns even after controlling for aggregate past and current demand, especially after the Lehman collapse. Second, we establish a strong positive relation between lending fees and active institutional investors participation. We show that active lenders timely price in new shorting demand. More importantly, we find that lending fees are likely strategically raised before negative earnings announcements and active institutional ownership does not relaxes constraints by providing supply. Taken together, we conclude that lending fees are closely related to future anticipated shorting demand by informed lenders especially around news events. This study provides new insights into a paradigm shift of the pricing behavior of equity lenders. Transparency in the lending market facilitates better information transmission, which allows the lenders to price in current as well as future or expected shorting demand. More importantly, our findings also imply that the increased transparency and participation of proactive equity lenders can potentially create new impediment to market efficiency by making short sale constraints more binding. 21

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Table 1. Summary Statistics The table presents the summary statistics of the key variables used in the analysis from January 1, 2007 to December 31, 2010. The sample includes NYSE, AMEX and NASDAQ stocks. The summary statistics are calculated based on pooled observations. AggSIR -1 (in %) and SIRnew -1 (in %) are the total number of shares out on loan as of the last trading day and the number of shares newly borrowed on the last trading day relative to shares outstanding, respectively. SIV -1 and SIVnew -1 are the total number of shares out on loan as of the last trading day and the number of shares newly borrowed on the last trading day relative to daily trading volume, respectively. Allfee (in %) is the value weighted average loan fee on all loans currently outstanding as of the last trading day, where the weights are the number of shares in the lending contract, irrespective of the loan age or loan term (whether overnight or term loan). IOall (IOact) is the aggregate institutional ownership (number of shares owned by asset management and advisory firms) relative to total shares from Factset. FracActIO is the number of shares held by active institutions (mutual funds, hedge fund, defined as Type 3 and 4 in 13 filings) IOact as a fraction of total institutional ownership IOall. LogSize is the natural logarithm of the number of common shares in millions times the month-end price. BtoM is the ratio of book value of equity to market value of equity following Fama and French s (1993) definition. BAspread -1m (in %) is the average bid-ask spread for last month, where the bid-ask spread is the difference of the daily ask price and bid price relative to the average of the daily bid and ask prices. HLspread -1m (in %) is the average daily price spread during last month, where the price spread is the difference between the highest and lowest price of the day relative to the highest price of the day. DailyTurn -1m (in %) is the average daily turnover in percentage during the past month. Retstd -1m (in %) is the standard deviation of daily return during the past month. CumRet 1,20 is the future 20-day holding period returns in percentage. Panel A presents the summary statistics for the entire sample period. Panel B shows the summary statistics before the Lehman collapse from January 1, 2007 to August 31, 2008. Panel C shows the summary statistics after the Lehman collapse, from October 1, 2008 till December 31, 2010. Panel A: Summary Statistics of the Entire Sample from January 1, 2007 to December 31, 2010 Variables Obs Mean 25th percentile Median 75th percentile AggSIR -1 (in %) 2,347,779 2.978 0.613 1.865 4.035 SIRnew -1 (in %) 2,347,779 0.155 0.000 0.043 0.157 SIV -1 2,347,779 7.466 1.207 3.152 6.858 SIVnew -1 2,347,779 0.293 0.000 0.067 0.216 Allfee (in %) 2,347,779 0.946 0.066 0.120 0.293 IOall (in %) 2,347,779 0.584 0.367 0.635 0.821 IOact (in %) 2,347,779 0.235 0.136 0.233 0.326 FracActIO 2,347,779 0.413 0.309 0.398 0.502 LogSize 2,347,779 6.212 4.941 6.015 7.389 BtoM 2,347,779 1.020 0.317 0.578 0.984 BAspread -1m (in %) 2,347,779 0.683 0.110 0.217 0.574 HLspread -1m (in %) 2,347,779 4.727 2.799 3.983 5.751 DailyTurn -1m (in %) 2,347,779 0.940 0.324 0.654 1.156 RetStd -1m (in %) 2,347,779 3.458 1.877 2.752 4.125 CumRet 1,20 (in %) 2,347,779 0.698-7.550 0.236 7.711 27

Panel B: Summary Statistics of Subsample from January 1, 2007 to August 31, 2008 Obs Mean 25th percentile Median 75th percentile AggSIR -1 (in %) 931,589 3.417 0.732 2.342 4.696 SIRnew -1 (in %) 931,589 0.171 0.000 0.040 0.161 SIV -1 931,589 9.237 1.359 3.595 7.604 SIVnew -1 931,589 0.332 0.000 0.056 0.201 Allfee (in %) 931,589 0.850 0.104 0.137 0.346 IOall (in %) 931,589 0.618 0.422 0.673 0.845 IOact (in %) 931,589 0.255 0.159 0.253 0.347 FracActIO 931,589 0.421 0.323 0.409 0.507 LogSize 931,589 6.585 5.311 6.285 7.743 BtoM 931,589 0.656 0.260 0.461 0.746 BAspread -1m (in %) 931,589 0.424 0.117 0.196 0.405 HLspread -1m (in %) 931,589 3.926 2.485 3.515 4.878 DailyTurn -1m (in %) 931,589 0.939 0.373 0.693 1.170 RetStd -1m (in %) 931,589 2.789 1.660 2.395 3.415 CumRet 1,20 (in %) 931,589-0.942-7.944-0.891 5.654 Panel C: Summary Statistics of Subsample from October 1, 2008 till December 31, 2010 Obs Mean 25th percentile Median 75th percentile AggSIR -1 (in %) 1,363,695 2.660 0.551 1.598 3.511 SIRnew -1 (in %) 1,363,695 0.143 0.003 0.044 0.152 SIV -1 1,363,695 6.204 1.118 2.873 6.324 SIVnew -1 1,363,695 0.263 0.006 0.075 0.225 Allfee (in %) 1,363,695 0.999 0.051 0.090 0.252 IOall (in %) 1,363,695 0.560 0.332 0.606 0.804 IOact (in %) 1,363,695 0.221 0.119 0.218 0.311 FracActIO 1,363,695 0.407 0.299 0.389 0.496 LogSize 1,363,695 5.956 4.664 5.801 7.147 BtoM 1,363,695 1.268 0.374 0.687 1.169 BAspread -1m (in %) 1,363,695 0.864 0.103 0.241 0.789 HLspread -1m (in %) 1,363,695 5.268 3.033 4.341 6.484 DailyTurn -1m (in %) 1,363,695 0.942 0.295 0.625 1.145 RetStd -1m (in %) 1,363,695 3.915 2.060 3.045 4.713 CumRet 1,20 (in %) 1,363,695 2.762-6.250 1.550 9.524 28

Table 2. The Return Predictability of Lending Fees The dependent variable is the future 20-day cumulative holding period returns in percent with a one day skipping from time t+1 to t+20. HighSIR is a dummy variable that takes the value of one for stocks that are in the top decile of the total short interest ratio, total number shares out on loan as percentage of the total shares defined in Table 1. Logallfee is the natural logarithm of the average value-weighted annualized lending fees on day t-1. SIRnew -1 is the number newly borrowed shares in the previous relative to the total share outstanding while HighSIRnew -1 is a dummy variable that take on the value of 1 for stocks from the top decile of the distribution of the SIRnew -1 and SIVnew -1. HighFee takes on the value of one for stocks from the top decile of the distribution based on the Logallfees. LogSize is the natural logarithm of the number of common shares in millions times the month-end price. BtoM is the ratio of book value of equity to market value of equity following Fama and French s (1993) definition. BAspread -1m (in %) is the average bid-ask spread during the past month, where the bid-ask spread is the difference of the daily ask price and bid price relative to the average of the daily bid and ask prices. HLspread -1m (in %) is the average daily price spread during the past month, where price spread is the difference between the highest and lowest price of the day relative to the highest price of the day. DailyTurn -1m (in %) and Retstd -1m (in %) are the average daily turnover and the daily standard deviation of returns in percentage during the past month. The coefficient estimates from Fama-MacBeth daily analyses based on the sample period of January 1, 2007 to December 31, 2010 are shown with corresponding t-stats in parentheses. ***, **, and * represent the statistical significance at the 10%, 5% and 1% level respectively. The standard errors are Newey-West adjusted errors with 5 lags. Panel A. Fama-MacBeth Return regression for full sample period CumRet 1,20 CumRet 1,20 CumRet 1,20 CumRet 1,20 CumRet 1,20 (1) (2) (3) (4) (5) HighSIR -0.692*** -0.397** -0.353** -0.373** -0.383** (-4.03) (-2.27) (-2.09) (-2.18) (-2.27) Logallfee -0.297*** -0.295*** -0.295*** -0.252*** (-7.97) (-7.93) (-7.94) (-5.77) SIRnew -1-0.191*** (-2.62) HighSIRnew -1-0.117-0.118 (-1.61) (-1.62) HighFee -0.270* (-1.91) LogSize -0.218** -0.310*** -0.307*** -0.309*** -0.296*** (-2.56) (-3.67) (-3.63) (-3.66) (-3.49) BtoM 0.030 0.013 0.013 0.014 0.014 (0.33) (0.14) (0.14) (0.15) (0.16) BAspread -1m 0.034 0.091 0.088 0.088 0.091 (0.21) (0.57) (0.55) (0.54) (0.57) HLspread -1m 0.199** 0.259*** 0.259*** 0.259*** 0.260*** (2.09) (2.71) (2.71) (2.71) (2.72) DailyTurn -1m 0.263*** 0.274*** 0.286*** 0.281*** 0.281*** (2.69) (2.82) (2.93) (2.91) (2.90) Retstd -1m -0.204*** -0.211*** -0.212*** -0.212*** -0.211*** (-4.61) (-4.81) (-4.83) (-4.82) (-4.83) Constant 0.982 0.655 0.659 0.661 0.690 (1.55) (1.03) (1.03) (1.04) (1.08) AdjR-squared 0.052 0.054 0.054 0.054 0.054 Obs 2347779 2347779 2347779 2347779 2347779 29

Table 2. continued Panel B. Fama-MacBeth Return regression for subsample, after Lehman collapse CumRet 1,20 CumRet 1,20 CumRet 1,20 CumRet 1,20 CumRet 1,20 (1) (2) (3) (4) (5) HighSIR -0.907*** -0.450* -0.380-0.443* -0.453* (-4.10) (-1.87) (-1.63) (-1.87) (-1.92) Logallfee -0.426*** -0.422*** -0.424*** -0.360*** (-9.18) (-9.09) (-9.11) (-8.54) SIRnew -1-0.251** (-2.20) HighSIRnew -1-0.050-0.049 (-0.46) (-0.45) HighFee -0.389** (-2.20) LogSize -0.496*** -0.640*** -0.637*** -0.641*** -0.627*** (-3.55) (-4.74) (-4.71) (-4.75) (-4.63) BtoM 0.227** 0.218** 0.217** 0.217** 0.217** (2.51) (2.40) (2.40) (2.39) (2.39) BAspread -1m -0.166-0.123-0.125-0.123-0.118 (-1.12) (-0.83) (-0.85) (-0.84) (-0.81) HLspread -1m 0.459*** 0.534*** 0.534*** 0.534*** 0.536*** (3.26) (3.80) (3.80) (3.80) (3.82) DailyTurn -1m 0.460*** 0.474*** 0.493*** 0.481*** 0.482*** (3.27) (3.42) (3.56) (3.50) (3.50) Retstd -1m -0.265*** -0.271*** -0.272*** -0.271*** -0.271*** (-5.22) (-5.39) (-5.42) (-5.41) (-5.42) Constant 3.241*** 2.821*** 2.835*** 2.833*** 2.905*** (3.50) (3.00) (3.01) (3.02) (3.06) AdjR-squared 0.063 0.066 0.066 0.066 0.066 Obs 1363695 1363695 1363695 1363695 1363695 30

Table 3. The Determinants of Lending Fees: The Role of Active Institutions The dependent variable is the natural logarithm of the future average lending fee in percentage on day t+1. IOall is the total number of shares held by institutions from 13f filings in percent of the total shares outstanding. FracActIO is the fraction of institutional ownership by active institutions calculated as IOact/IOall where IOact is the number of shares held by active institutions (mutual funds, hedge fund, defined as Type 3 and 4 in 13 filings). HighSIR -1 is the dummy variable that equals 1 for stocks that are in the top decile of the distribution of the SIR on day t-1, where SIR is the total number shares out on loan relative to the total number of shares. SIRnew -1 is the number newly borrowed shares in the previous relative to the total share outstanding while HighSIRnew -1 takes on the value of 1 for stocks from the top decile of the distribution of the SIRnew -1. HighSIRnew*FracActIO and SIRnew -1 *FracActIO are interaction variables of FracActIO with the HighSIRnew -1 and SIRnew -1 variables, respectively. Control variables, such as LogSize, BtoM, DailyTurn -1m, Retstd -1m, BAspread -1m, and HLspread -1m are defined in Table 1. The coefficient estimates are from Panel regressions with firm and day fixed effects, with robust standard errors based on the sample period of January 1, 2007 to December 31, 2010 in Panel A and the sample period of October 1, 2008 to December 31, 2010 in Panel B. The coefficient estimates with corresponding t-stats in parentheses. ***, **, and * represent the statistical significance at the 10%, 5% and 1% level respectively. Panel A. Panel Regression Result for Lending Fees for Full Sample Period Logallfee Logallfee Logallfee Logallfee Logallfee Logallfee (1) (2) (3) (4) (5) (6) HighSIR 0.627*** 0.656*** 0.615*** 0.632*** 0.644*** 0.636*** (19.71) (6.43) (6.07) (6.39) (6.33) (6.31) IOall -0.435*** -0.434*** -0.462*** -0.462*** -0.441*** -0.441*** (-3.99) (-3.98) (-4.25) (-4.25) (-4.05) (-4.05) FracActIO -0.214** -0.209** -0.210** -0.217** -0.209** -0.205** (-2.27) (-2.23) (-2.25) (-2.32) (-2.23) (-2.20) HighSIR*FracActIO -0.072-0.053-0.096-0.075-0.057 (-0.29) (-0.22) (-0.40) (-0.30) (-0.23) HighSIRnew 0.157*** 0.098*** (18.27) (3.01) HighSIRnew* FracActIO 0.151* (1.84) SIRnew -1 0.044*** 0.062** (6.23) (2.45) SIRnew -1 * FracActIO -0.044 (-0.84) LogSize -0.321*** -0.321*** -0.321*** -0.321*** -0.321*** -0.321*** (-11.53) (-11.53) (-11.55) (-11.55) (-11.54) (-11.54) BtoM 0.024*** 0.024*** 0.023*** 0.023*** 0.024*** 0.024*** (4.87) (4.87) (4.85) (4.85) (4.86) (4.86) BAspread -1m -0.023*** -0.023*** -0.023*** -0.023*** -0.023*** -0.023*** (-3.00) (-3.01) (-3.01) (-3.00) (-3.01) (-3.01) HLspread -1m 0.052*** 0.052*** 0.052*** 0.052*** 0.052*** 0.052*** (10.03) (10.03) (10.07) (10.07) (10.04) (10.04) DailyTurn -1m 0.071*** 0.071*** 0.067*** 0.067*** 0.070*** 0.070*** (8.76) (8.77) (8.44) (8.45) (8.67) (8.66) Retstd -1m -0.012*** -0.012*** -0.011*** -0.011*** -0.011*** -0.011*** (-3.50) (-3.50) (-3.47) (-3.47) (-3.50) (-3.50) Constant 0.746*** 0.743*** 0.754*** 0.758*** 0.745*** 0.743*** (4.04) (4.03) (4.09) (4.10) (4.03) (4.02) Observations 2,347,779 2,347,779 2,347,779 2,347,779 2,347,779 2,347,779 R-squared within 0.266 0.266 0.267 0.267 0.266 0.266 R-squared overall 0.371 0.371 0.375 0.375 0.3714 0.372 31

Table 3. continued Panel B. Panel Regression Result for Lending Fees for SubSample, after the Lehman collapse Logallfee Logallfee Logallfee Logallfee Logallfee Logallfee (1) (2) (3) (4) (5) (6) HighSIR 0.607*** 0.572*** 0.534*** 0.560*** 0.530*** 0.538*** (16.54) (5.00) (4.69) (5.05) (4.64) (4.79) IOall -0.759*** -0.760*** -0.788*** -0.789*** -0.785*** -0.786*** (-5.67) (-5.68) (-5.89) (-5.90) (-5.87) (-5.87) FracActIO -0.199* -0.205* -0.204* -0.215** -0.202* -0.207* (-1.81) (-1.87) (-1.86) (-1.96) (-1.85) (-1.87) HighSIR*FracActIO 0.091 0.097 0.029 0.083 0.062 (0.32) (0.34) (0.10) (0.29) (0.22) HighSIRnew 0.174*** 0.083** (17.98) (2.51) HighSIRnew* FracActIO 0.242*** (2.77) SIRnew -1 0.192*** 0.169*** (10.14) (3.63) SIRnew -1 * FracActIO 0.060 (0.48) LogSize -0.280*** -0.280*** -0.281*** -0.281*** -0.282*** -0.282*** (-8.25) (-8.24) (-8.29) (-8.29) (-8.32) (-8.31) BtoM 0.008* 0.008* 0.008 0.008 0.008 0.008 (1.67) (1.67) (1.63) (1.63) (1.59) (1.59) BAspread -1m -0.030*** -0.030*** -0.030*** -0.030*** -0.029*** -0.029*** (-3.52) (-3.52) (-3.48) (-3.47) (-3.43) (-3.42) HLspread -1m 0.036*** 0.036*** 0.036*** 0.036*** 0.036*** 0.036*** (6.58) (6.59) (6.61) (6.60) (6.59) (6.59) DailyTurn -1m 0.065*** 0.065*** 0.061*** 0.061*** 0.060*** 0.060*** (7.52) (7.53) (7.15) (7.17) (7.02) (7.03) Retstd -1m -0.008** -0.008** -0.007** -0.007** -0.007** -0.007** (-2.33) (-2.33) (-2.28) (-2.27) (-2.28) (-2.28) Constant 0.944*** 0.946*** 0.955*** 0.959*** 0.942*** 0.943*** (4.24) (4.25) (4.30) (4.32) (4.24) (4.25) Observations 1,363,695 1,363,695 1,363,695 1,363,695 1,363,695 1,363,695 R-squared within 0.218 0.218 0.220 0.221 0.221 0.221 R-squared overall 0.407 0.406 0.413 0.549 0.549 0.549 32

Table 4. The Determinants of Lending Fees: Earnings Announcements The dependent variable is the natural logarithm of the future average lending fee in percentage on day t+1. IOall is the total number of shares held by institutions from 13f filings in percent of the total shares outstanding. FracActIO is the fraction of institutional ownership by active institutions calculated as IOact/IOall where IOact is the number of shares held by active institutions (mutual funds, hedge fund, defined as Type 3 and 4 in 13 filings). HighSIR -1 is the dummy variable that equals 1 for stocks that are in the top decile of the distribution of the SIR on day t-1, where SIR is the total number shares out on loan relative to the total number of shares. SIRnew -1 is the number newly borrowed shares in the previous relative to the total share outstanding while HighSIRnew -1 takes on the value of 1 for stocks from the top decile of the distribution of the SIRnew -1. BefAN is a dummy variable that takes on the value of 1 for the 10 days prior to the earnings announcement. Control variables, such as LogSize, BtoM, DailyTurn -1m, Retstd -1m, BAspread -1m, and HLspread -1m are defined in Table 1The coefficient estimates are from Panel regressions with firm and day fixed effects, with robust standard errors based on the sample period of January 1, 2007 to December 31, 2010 in Panel A and the sample period of October 1, 2008 to December 31, 2010 in Panel B. The coefficient estimates with corresponding t-stats in parentheses. ***, **, and * represent the statistical significance at the 10%, 5% and 1% level respectively. Panel A. Panel regression result for lending fees around earnings announcements, full sample period Logallfee Logallfee Logallfee Logallfee Logallfee Logallfee (1) (2) (3) (4) (5) (6) All Announcements Negative Announcements Non-negative Announcements HighSIR 0.579*** 0.594*** 0.619*** 0.637*** 0.526*** 0.538***. (18.53) (18.65) (13.18) (13.39) (14.96) (14.98) IOall -0.380*** -0.360*** -0.403** -0.378** -0.335*** -0.319*** (-3.41) (-3.23) (-2.25) (-2.11) (-2.93) (-2.78) FracActIO -0.321*** -0.322*** -0.261-0.261-0.336*** -0.338*** (-3.05) (-3.05) (-1.38) (-1.38) (-2.99) (-3.00) BefAN 0.015*** 0.012** 0.041*** 0.038*** 0.004 0.002 (2.97) (2.43) (4.14) (3.74) (0.71) (0.34) HighSIRnew -1 0.146*** 0.134*** 0.148*** (15.05) (10.62) (13.18) SIRnew -1 0.053*** 0.033*** 0.065*** (5.99) (2.93) (5.57) LogSize -0.345*** -0.345*** -0.429*** -0.429*** -0.305*** -0.305*** (-11.34) (-11.36) (-9.43) (-9.44) (-9.36) (-9.36) BtoM 0.018*** 0.018*** 0.009* 0.009* 0.031*** 0.031*** (3.15) (3.15) (1.67) (1.66) (3.44) (3.44) BAspread -1m -0.006-0.006 0.000-0.000-0.002-0.002 (-0.52) (-0.53) (0.00) (-0.01) (-0.17) (-0.17) HLspread -1m 0.062*** 0.062*** 0.062*** 0.062*** 0.055*** 0.055*** (9.63) (9.62) (5.99) (5.99) (7.45) (7.46) DailyTurn -1m 0.062*** 0.065*** 0.055*** 0.058*** 0.064*** 0.067*** (6.24) (6.36) (3.72) (3.88) (4.94) (4.99) Retstd -1m -0.016*** -0.016*** -0.014* -0.015** -0.016*** -0.016*** (-3.65) (-3.70) (-1.96) (-1.97) (-2.82) (-2.86) Constant 0.809*** 0.801*** 1.224*** 1.217*** 0.614** 0.603** (3.73) (3.68) (3.79) (3.76) (2.53) (2.48) Observations 662,683 662,683 207,381 207,381 455,302 455,302 R-squared within 0.278 0.277 0.283 0.281 0.275 0.274 R-squared overall 0.363 0.36 0.333 0.33 0.363 0.36 33

Table 4. continued Panel B. Panel regression result for lending fees around earnings announcements, later sample period Logallfee Logallfee Logallfee Logallfee Logallfee Logallfee (1) (2) (3) (4) (5) (6) All Announcements Negative Announcements Non-negative Announcements HighSIR 0.558*** 0.545*** 0.570*** 0.559*** 0.515*** 0.503***. (15.51) (14.95) (8.82) (8.59) (12.36) (11.83) IOall -0.667*** -0.669*** -0.825*** -0.823*** -0.530*** -0.533*** (-4.59) (-4.61) (-3.25) (-3.25) (-3.30) (-3.32) FracActIO -0.262** -0.263** -0.236-0.245-0.229-0.228 (-2.05) (-2.05) (-0.86) (-0.90) (-1.55) (-1.54) BefAN 0.010 0.011* 0.028** 0.029** 0.002 0.002 (1.64) (1.75) (2.11) (2.16) (0.22) (0.33) HighSIRnew -1 0.156*** 0.140*** 0.148*** (14.22) (8.88) (12.15) SIRnew -1 0.189*** 0.169*** 0.182*** (7.80) (7.94) (6.44) LogSize -0.309*** -0.309*** -0.338*** -0.338*** -0.294*** -0.295*** (-7.83) (-7.84) (-5.41) (-5.42) (-6.34) (-6.35) BtoM 0.005 0.005 0.003 0.002 0.012 0.012 (0.89) (0.83) (0.40) (0.36) (1.35) (1.29) BAspread -1m -0.001 0.000-0.005-0.004-0.003-0.002 (-0.05) (0.03) (-0.24) (-0.19) (-0.18) (-0.13) HLspread -1m 0.043*** 0.043*** 0.045*** 0.045*** 0.041*** 0.041*** (5.94) (5.92) (3.28) (3.28) (4.98) (4.96) DailyTurn -1m 0.063*** 0.062*** 0.062*** 0.060*** 0.060*** 0.060*** (5.30) (5.21) (3.04) (2.94) (3.67) (3.64) Retstd -1m -0.011*** -0.011** -0.009-0.009-0.013** -0.013** (-2.58) (-2.57) (-1.00) (-1.02) (-2.39) (-2.37) Constant 0.951*** 0.934*** 1.549*** 1.537*** 0.647* 0.629* (3.35) (3.29) (3.31) (3.28) (1.94) (1.89) Observations 384,261 384,261 111,035 111,035 273,226 273,226 R-squared within 0.244 0.245 0.259 0.260 0.236 0.237 R-squared overall 0.396 0.396 0.367 0.367 0.392 0.393 34

Figure 1. Monthly Lending Income, Stock Borrowing Demand and Coverage This figure depicts the time series patterns of monthly lending income, stock borrowing demand and number of reporting firms from January 2007 to December 2010. Monthly lending income is the aggregated monthly lending fees across all stocks for all trading days in the month, where the lending income for one individual stock is the sum of shares on loan times share price times lending fee. The total value of stocks on loan is the product of the total number of shares out on loan and the share price. Stock borrowing demand is measured with the aggregate short interest ratio (AggSIR) which is the total number shares out on loan as percentage of the total shares outstanding. Number of reporting firms with stock lending information refers to the firms with stock return and sec-lending data. Panel A. Average Stock Borrowing Demand and Number of Reporting Firms 5.0 4.0 3.0 2.0 1.0 0.0 5000 4000 3000 2000 1000 0 Average number of stocks with stock lending information on right axis Average AggSIR (% of agg borrowed shares/total shares) Panel B. Aggregate Value of Stocks on Loan and Monthly Lending Income 250 200 150 100 50 0 200 160 120 80 40 0 Total monthly income from stock lending ($Mill) on right axis Total value of stocks currently borrowed (out on loan) in $Bill 35

Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Jan-07 Mar-07 May-07 Jul-07 Sep-07 Nov-07 Jan-08 Mar-08 May-08 Jul-08 Sep-08 Nov-08 Jan-09 Mar-09 May-09 Jul-09 Sep-09 Nov-09 Jan-10 Mar-10 May-10 Jul-10 Sep-10 Nov-10 Figure 2. Time-Series Betas of Lending Fees from Fama-MacBeth Return Regressions The graphs depict the 30-day moving average of Betas from the daily cross-sectional return regressions (Regression models 2 and 3 from Table 2 depicted in Panels A and B, respectively) in solid lines, while the lighter dashed line shows the average coefficient estimate during the sample period before (Jan. 2007 to Aug. 2008) and after the Lehman collapse (Oct. 2008 to Dec. 2010). The shaded regions conincide with September 2008 to depict the Lehman collapse. Panel A. Time-series betas of lending fees, without control for shorting demand from daily cross-sectional return regressions 1.5 1.0 0.5 0.0-0.5-1.0-1.5 Time-series of Betas on lending fees from daily cross-sectional regressions Average Beta Before and After Lehman's collapse Panel B. Time-series betas of lending fees, with control for shorting demand from daily cross-sectional return regressions 1.5 1.0 0.5 0.0-0.5-1.0-1.5 Time-series Betas on Lending fees from Return regression Average Betas before and after the Lehman's collapse 36

Appendix I. Illustration of Establishment of Lending Fee and Loan Fees in the Stock Lending Market Example A illustrates establishment of fees in the buy-side (short sellers) and sell side (lenders) markets where a broker acts as intermediary (tri-party agreement). Examples B illustrates the establishment of fees in bilateral setting. For simplicity, we do not display the stock being passed from the lender to borrower via the broker (in example A) or directly (in example B). A) Traditionally, lenders are uninformed in the absence of information about the stock lending market. Stock lending transaction where the lender is not in a good bargaining position (potentially the lender does not have access to the data and therefore he/she is uninformed about the current market rate and the demand (for simplicity, the graph demonstrate only the opening of the transaction) Uninformed Lender receives 1.2% lending fee (after the broker s 40% cut from the 2%) after the cut from the broker. Or Informed Lender receives 1.6% lending fee (after the broker s 20% cut from the 2%) after the cut from the broker. Fee (1.2%) Fee (1.6%) Broker takes a cut when passing on fees, where the cut depends on the lender Collateral invested and managed by broker according to lender s guidelines (the generated income will be shared across borrower and lender) Fee (2%) Cash Collateral Stock Borrower (most commonly short seller) Pays 2% loan fee + post 150% collateral *2% is the current benchmark rate, the average borrowing cost for this stock B) With automated lending platforms, lenders and borrowers can directly connect without intermediator (however often a financial institution or infrastructure provider provide the platform and implicit or explicit guarantee). In this case, the informed lenders may demand higher fees. The borrower may agree to the higher fee if the borrower is restricted in terms of time (e.g., the trading opportunity is very time sensitive) and prefer to make deal with lenders they already know from past relationship. Fee (1%) Uninformed lender receives significantly below market rate fee, say 1%, as the short seller wants to reduce costs Or Collateral Stock Borrower (most commonly short seller) Wants to pay less than the current benchmark rate Informed lender considers the benchmark rate of 2% and wants to get a rate close to that, say 1.6%. If this lender has existing relation with the specific borrower, the lender may accept more flexible collateral, or more flexible loan terms. 37 Fee (1.6%) Flexible Collateral *2% is the current benchmark rate, the average borrowing cost for this stock