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Short-selling constraints and ‘quantitative’investment strategiesPanagiotis Andrikopoulos a , James Clunie b & Antonios Siganos ca Department of Accounting and Finance, Leicester Business School , DeMontfort University , The Gateway, Leicester , LE2 7BQ , UKb Scottish Widows Investment Partnership , Edinburgh One, 60 MorrisonStreet, Edinburgh , EH3 8BE , UKc Accounting and Finance Division, Business School , Glasgow University ,Glasgow , UKPublished online: 31 Jan 2012.

To cite this article: Panagiotis Andrikopoulos , James Clunie & Antonios Siganos (2013) Short-sellingconstraints and ‘quantitative’ investment strategies, The European Journal of Finance, 19:1, 19-35, DOI:10.1080/1351847X.2011.634426

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The European Journal of FinanceVol. 19, No. 1, January 2013, 19–35

Short-selling constraints and ‘quantitative’ investment strategies

Panagiotis Andrikopoulosa∗, James Clunieb and Antonios Siganosc

aDepartment of Accounting and Finance, Leicester Business School, De Montfort University, The Gateway, LeicesterLE2 7BQ, UK; bScottish Widows Investment Partnership, Edinburgh One, 60 Morrison Street, Edinburgh EH3 8BE,UK; cAccounting and Finance Division, Business School, Glasgow University, Glasgow, UK

(Received 13 January 2011; final version received 18 October 2011)

This study uses stock lending data from Data Explorers to assess the impact of short-selling constraintson the profitability of eight investment strategies. Returns from unconstrained long–short portfolios arecompared with those from ‘feasible’ portfolios, constrained to short-selling only those shares that can beborrowed. We find that only a small percentage of the firms identified by Datastream for short-selling areavailable for lending, but our results suggest that differences in profitability between unconstrained andfeasible strategies are statistically insignificant. We also find that the stock borrowing fee for the majorityof the strategies is normally less than 1% per annum, showing that prior UK studies, which assumedthat the short-selling fee is flat at 1.50% per annum, have overestimated such cost. Overall, these resultsindicate that stock loan unavailability and stock borrowing fees do not explain the persistence of returnsfrom anomaly-exploiting quantitative investment strategies in the UK stock market.

Keywords: stock market anomalies; stock-lending fee; short-selling constraints; Data Explorers

JEL Classification: G14; G32

1. Introduction

A number of studies (Fama and French 2008) show that long–short portfolios that seek to exploitstock market anomalies tend to be concentrated in smaller capitalisation or hard-to-short compa-nies. Such stocks would generally be characterised by poorer trading liquidity, higher transactioncosts, poorer loan availability and higher stock-lending fees. A limited number of studies inves-tigate the extent to which this tendency explains why anomalies have persisted, despite beingknown for many years. Geczy, Musto, and Reed (2002) use private data based on a large uniden-tified lender of US stocks from November 1998 to October 1999 and investigate the profitabilityof size, book/market and momentum strategies. They find that where strategies are restricted toshares that investors can short-sell, they generate lower profitability than unconstrained portfoliosfound in the academic literature, but remain economically and statistically significant. D’Avolio(2002) uses US data between April 2000 and September 2001 and demonstrates that ‘growth’ and‘low-momentum’ stocks are relatively more likely to experience high short-selling fees, leadingto practical difficulties and costs in creating the long/short factor portfolios found in the financeliterature.

This study builds on the existing literature by using data from Data Explorers for UK companiesbetween February 2007 and February 2010. Data Explorers offers stock-lending data aggregatedfrom a number of market participants, including broker-dealers, lenders and custodian banks,

∗Corresponding author. Email: Pandrikopoulos@dmu.ac.ukISSN 1351-847X print/ISSN 1466-4364 online© 2013 Taylor & Francishttp://dx.doi.org/10.1080/1351847X.2011.634426http://www.tandfonline.com

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offering a reliable estimation of which companies can be sold-short and at what lending fee.This differs from Geczy, Musto, and Reed (2002) who use only a single lender and are thussusceptible to substitution effects. Data Explorers also offers unique data on the quantity of stockborrowed relative to the total amount available for borrowing, known as the active utilisation rate.The focus of this study is to explore the profitability and the short-selling fee of the followingeight widely published long/short strategies: return/assets, size, size reverse, earnings/price, assetgrowth, book/market, accruals and momentum. The selected strategies include some of the seminalstrategies (e.g. size) that have been reported in the academic literature and some recently reportedinvestment strategies (e.g. return/assets).

We first form unconstrained long/short deciles to explore strategies’ profitability and comparethe results with the profitability of strategies that narrow the subset of short positions to allow onlythose that are available for borrowing. We find that for most strategies, only a limited percentageof the firms that should be shorted can be borrowed – mostly less than 30% of the firms. As withGeczy, Musto, and Reed (2002), we find that the differences in profitability between unconstrainedand constrained strategies are not statistically significant, although the profitability of the strategiestends to be higher when only firms available for borrowing are sold short.

We also explore the magnitude of the fee required to follow the short positions in the strategies.Previous UK studies (Agyei-Ampomah 2007; Li, Brooks, and Miffre 2009; Siganos 2010; Opongand Siganos 2011), which have explored the post-cost profitability of investment strategies, havehad no access to short-selling data and assumed that the short-selling fee is flat for all shares at1.50% per annum. Soares and Stark (2009, 330) even exclude the short-selling cost, since ‘in theUK, data availability for the direct estimation of shorting costs … is limited, implying that thesecosts cannot be easily determined’. Our study has access to short-selling data and shows that priorstudies seem to have overestimated the lending fee, since the fee for the strategies’ short positionis mostly less than 1% per annum.

Overall, these results indicate that stock loan unavailability and stock borrowing fees do notexplain the persistence of returns from anomaly-exploiting quantitative investment strategies onthe London Stock Exchange.

This paper is organised as follows. The next section introduces the stock-lending processand reviews prior to empirical evidence on lending constraints. Section 3 presents the data andmethodology. Section 4 discusses the empirical findings and Section 5 concludes the study.

2. Literature review

A typical stock-lending transaction starts with a trader, who intends to short-sell, requesting fromhis broker a ‘locate’ on a given quantity of a stock. Since a formal securities lending exchangedoes not currently exist, brokers may locate stock from their own inventory, from an institutionalinvestor with whom they have either a relationship or a stock-lending agreement, or via the use ofan intermediary such as a custodian or investment bank. Electronic platforms such as EquiLendand SecFinex allow firms to share information on stock available for lending and to negotiatestock loans. Such platforms cover only part of the total transactions and do not effectively serveas regulated exchanges. Stock borrowing typically takes place the same day. The lending feeis negotiated between the parties involved and is determined by supply and demand for thesecurities to be lent, collateral flexibility and the term associated with the transaction. Stock loansare normally made on a ‘call’ basis: that is, they are open-ended in nature, renewed daily and withcollateral adjusted according to daily market moves in the underlying stock. Normally, stocksmay be ‘recalled’ at any time.

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A small number of studies have explored the impact of short-selling constraints on strategiesprofitability. Geczy, Musto, and Reed (2002) is probably the study closest to the current paper.They use private data based on a large unidentified lender of US stocks from November 1998to October 1999 and investigate the profitability of the following investment strategies: size,book/market and momentum. They find that if strategies are followed within shares that investorsare allowed to short-sell, investment strategies generate lower profitability than that found inthe academic literature without any constraint, but strategies remain economically profitable andstatistically significant. Interestingly, Bettman, Sault, and Reibnitz (2010) use Australian dataand find that the profitability of the 52-week momentum strategy is weak after adjusting foravailability of firms to short-sell and liquidity constraints. These findings raise a query to theextent short-selling constraints may have a different impact in the profitability of the investmentstrategies within different markets. To the best of our knowledge, this is the only study withinthe UK context to explore the particular issue. Stated differently, due to differences in regulatoryand institutional frameworks, evidence from studies of US data is not necessarily representativeof behaviour outside US markets. For example, in the UK, the Financial Services Authority doesnot ordinarily impose specific restrictions or controls on short-selling. Instead, short-sellers aresubject to general market and regulatory arrangements, including market-abuse principles.

D’Avolio (2002) is the key study that has explored the level of short-selling fee by examiningan 18-month period of data (April 2000 to September 2001) from one US stock lender. He findsthat whereas stock-lending fees for most stocks (known as ‘general collateral’) are very low, alimited number of stocks (known as ‘specials’) can only be borrowed at higher fee rates. 91%of stocks lent out cost less than 1% per annum to borrow, while the value-weighted mean feefor such ‘general collateral’ stocks is 17 basis points per annum. For the remaining 9% of stocks(‘specials’), the mean fee is 4.3% per annum. The largest fee observed in his sample is 79% perannum. D’Avolio (2002) demonstrates that ‘growth’ and ‘low-momentum’ stocks are relativelymore likely to be ‘special’, leading to practical difficulties and costs in creating the long/shortfactor portfolios found in the finance literature.

Other studies have used proxies for short-sale constraints. Nagel (2005) uses institutional own-ership as a proxy and finds that short-sale constraints help explain cross-sectional return anomalies,such as the underperformance of stocks with a high market-to-book ratio, analysts’ forecast dis-persion, turnover or volatility. Ali and Trombley (2006) use the following five firm characteristicsto proxy stock loan fees: size, trading volume, cash flow, IPO status and book/market. They con-clude that short-sale constraints appear to be an important factor behind the persistence of themomentum effect, years after its initial discovery. Zhang, Philipatos, and Daves (2010) constructa more complete proxy of short-selling constraints incorporating demand and supply of shortingactivity. Using such proxy, they find that the profitability of the loser portfolio in the momentumstrategy can be explained, since firms with the highest shorting constraints experience very poorperformance.

This study builds on the existing literature by using a direct measure of market-wide stock loancosts, as opposed to a market-wide proxy for stock loan fees (as in Nagel 2005; Ali and Trombley2006; Zhang, Philipatos, and Daves 2010) or a direct measure based on a single lender (as inD’Avolio 2002; Geczy, Musto, and Reed 2002). This paper examines 3 years of stock loan data.The two other studies that use direct measures of fees (D’Avolio 2002; Geczy, Musto, and Reed2002) examine 1 year and 18 months of data, respectively. A limited number of studies investigateshort-selling and its impact on stock prices outside the USA (Aitken et al. 1998; Biais, Bisiere,and Decamps 1999; Poitras 2002; Ackert and Athanassakos 2005; Chang, Chen, and Yu 2007;Au, Doukas, and Onayev 2009).

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3. Data and methodology

3.1 Data

This study employs data from two databases. Weekly information (every Thursday) on short-selling costs, active utilisation and the percentage of shares outstanding on loan are providedby Data Explorers. Data Explorers offers unique data on stock-lending provided from marketparticipants such as broker-dealers, lenders and custodian banks and is the most complete dataset regarding the lending market in the London Stock Exchange.1 Data Explorers is estimatedto cover around 80% of short transactions and an increasing number of recent academic papershave employed Data Explorers to access short-selling data (Prado, Brounen, and Verbeek 2009).All other operating/financial and market data are obtained from DataStream. Companies’SEDOLcodes offer the linkage between the two databases. The selected data set covers the period from22 February 2007 to 18 February 2010. The selected period is driven by the availability ofstock-lending and active utilisation data.

To determine the sample from Data Explorers, the study excludes non-UK firms, trusts, funds,Exchange Traded Funds (ETFs), preference shares and non-equities. To determine the total sampleavailable from DataStream, all dead and actively listed UK companies are employed. This studyfinds that Data Explorers offers data for 812 companies out of the total 2212 firms available fromDataStream showing that around 37% of the companies listed in the UK stock market can besold short. The mean (median) market capitalisation of companies available from Data Explorersis £1996 million (£174 million) and those with no information in Data Explorers £109 million(£12 million). Stocks that can be sold-short represent 95.58% of the market value in the UKstock market. In line with the US literature (D’Avolio 2002), this study finds that most largecapitalisation UK firms can be sold short, but a large number of small capitalisation firms areunavailable for short-selling.

This study collects the following data information from Data Explorers:

• The short-selling fee uses the fee for non-cash collateral transactions or using the risk-free rateminus the rebate rate for cash collateral transactions. The fee for a company in day t is theweighted average of the fees in day t adjusted on the size of transactions. Using the value-weighted average, small transactions that tend to be expensive have a smaller impact on the feeestimations.

• The active utilisation for a company in day t is estimated by dividing the amount that is actuallylent by the amount available to borrow. This is a measure of supply and is positively associatedwith the lending fee. Difficult (easy) to find shares for borrowing tend to experience high (low)fees. Firms with 100% active utilisation are not available for borrowing.

• The percentage of shares outstanding on loan for a company in day t shows the value of sharessupplied for borrowing firm i in relation to the firm’s market capitalisation. This shows thepopularity of each company to be short-sold in relation to its size.

Panel 1 of Table 1 reports some descriptive statistics for the variables available from Data Explor-ers; while Figure 1 shows the variables’ median values during the sample period. The activeutilisation column shows that 20% of companies available in Data Explorers have been sold shortto the maximum possible extent (100%) and, in unreported results, this study finds that around23% of companies experienced maximum active utilisation during the financial crisis from late-2008 to early-2009. The ‘percentage of shares outstanding on loan’ column also shows that asmall percentage (around 1–2%) of the total capitalisation of companies at any time is short-sold.

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Table 1. Descriptive statistics for variables from Data Explorers (%).

1% 5% 10% 20% 30% 40% 50% 60% 70% 80% 90% 95% 99% Average

Panel 1: Data distributionActive utilisation 0.00 0.00 0.89 3.24 5.74 9.11 13.94 22.42 43.86 100.00 100.00 100.00 100.00 35.24Short-selling fee 0.07 0.09 0.10 0.14 0.21 0.30 0.44 0.67 1.01 1.86 3.50 4.16 6.00 1.11% of shares on loan 0.00 0.01 0.04 0.15 0.32 0.61 1.03 1.58 2.26 3.29 5.20 7.65 14.27 2.06

Panel 2: Firms’ change in short-selling fee/refinancing riskWeekly −64.14 −27.36 −15.15 −5.89 −1.95 −0.17 0.00 0.31 2.35 6.83 18.58 38.42 184.01 8.72Monthly −79.70 −47.45 −30.56 −15.22 −6.49 −1.20 0.00 2.30 8.62 20.51 50.10 98.22 402.20 18.08Yearly −92.22 −76.65 −60.44 −37.51 −19.93 −4.56 6.42 27.73 64.56 127.94 267.03 470.69 1559.48 113.48

Notes: This table shows the distribution of variables from Data Explorers. Active utilisation shows the amount that is actually lent for a company compared to the amount availableto borrow. The short-selling fee for a company in a day is the weighted average of the fees during the day adjusted on the size of transactions. The % of shares outstanding onloan for a company shows the value of shares supplied for borrowing a firm in relation to that firm’s market capitalisation. For example, 70% of firms face fee less than 1.01%per annum. Panel 2 shows the distribution of changes in short-selling fee within alternative frequencies (e.g. weekly). For example, there was a reduction in fee by −15.15% onaverage in the extreme 10% of the firms.

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Active Utilisation

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2007 2008 2009

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2007 2008 20095.00

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0.70

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Figure 1. Data Explorers’ variables during the sample period (median values).

Panel 1 of Table 1 also shows that it is relatively cheap to short-sell the majority of companies,with 69% of companies borrowed at a cost of less than 1% per annum. The average (median) feeis 1.11 (0.44)% per annum. However, for a small number of companies, the fee can be notablyhigher. For example, 10% of the firms exhibit a stock borrowing cost of over 3.5% per annum.Figure 1 also shows that median fee values varied considerably during the sample period withthe level of fee almost doubling from late-2008 to early-2009 and then reversing back to normallevels at the end of 2009. As an example, Table 2 presents a selection of companies during thesample period that have experienced very high fees. The maximum fee is noted as being 40% perannum for Instore Plc during the sample period from 25 June 2009 to 17 December 2009. TwoUK banks that became financially distressed, Bradford and Bingley Plc and Northern Rock Plc,are unsurprisingly among the companies with high lending fees – around 15% per annum duringthe banking crisis. Panel 2 of Table 1 also provides key descriptive statistics for the changes infirms’ short-selling fees on a weekly, monthly and annual basis. We find that stock-borrowingfees can vary considerably even within a week, with longer frequencies exhibiting greater fee

Table 2. Selected firms with high short-selling fees.

Company Fee (date)

Instore Plc 40% (25 June 2009 until 17 December 2009)Sibir Energy Plc 32.90% (23 July 2009)Cosalt Plc 26.61% (22 November 2007), 17% (29 November 2007), 17.07% (6

December 2007)Bradford and Bingley Plc 11.30% (17 July 2008), 13.17% (24 July 2008), 14.21% (31 July 2008),

14.49% (7 July 2008), 14.53% (14 August 2008), 14.40% (21 August2008), 13.94% (28 August 2008), 13.57% (4 September 2008), 13.10%(11 September 2008), 13.03% (18 September 2008)

Northern Rock Plc 11.27% (22 November 2007), 12.62% (29 November 2007), 12.25% (6December 2007), 13.92% (13 December 2007), 14.53% (20 December2007), 14.93% (27 December 2007), 15.66% (3 January 2008), 17.01%(10 January 2008), 18.30% (17 January 2008), 20.20% (24 January2008), 18.11% (31 January 2008), 18.11% (7 February 2008), 17.76% (14February 2008), 16.88% (21 February 2008), 18.34% (28 February 2008),13.90% (6 March 2008)

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variation. These provide a measure of the refinancing risk faced by investors. Overall, in line withfindings within the US literature (D’Avolio 2002), we find that the level of short-selling fee mayvary significantly between different firms and/or through time for the same companies.

3.2 Investment strategies

The strategies can be distinguished between (i) those that classify stocks, using various marketcharacteristics to exploit known return anomalies, with the most commonly known being pricemomentum (Jegadeesh and Titman 1993) and market size (Banz 1981); and, (ii) strategies thataim to exploit the erroneous adjustment of prices to fundamental indicators and/or other variouscompanies’micro-characteristics. The most widely known of these are value/contrarian strategies,where firm selection is based on characteristics such as return/assets (Chen and Zhang 2010),book/market (Rosenberg, Reid, and Lanstein 1985), earnings/price (Basu 1977), asset growth(Cooper, Gulen, and Schill 2008) and accruals (Sloan 1996).

The strategies under examination and their computation details are as follows:

• Return/assets: This strategy buys companies with high return/assets ratios and short-sells thecounterpart firms with low ratios. Companies with missing return/assets data and financial firms(icbic = 8000) are excluded.2

• Size and size reverse: In line with Banz (1981), this strategy buys the portfolio comprising smallcapitalisation stocks (mv) and short-sells that of the counterpart large capitalisation stocks.Within the UK literature (Dimson and Marsh 1999), it is documented that the size effect hadreversed in the post-1990 period (i.e. large capitalisation companies tend to outperform smallcapitalisation firms), and therefore this study also examines the ‘size reverse strategy’. Sincesize (reverse size) strategy sells-short large (small) capitalisation firms, it is interesting to findthe impact of lending constraints in the largest/smallest capitalisation firms.

• Value: this study ranks firms on their book/market [(305-344)/mv] and earnings/price (eps/up).The strategy buys high book-to-market value of equity (B/M) stocks, and low earnings-to-priceratio (E/P) stocks. It short-sells companies with the exact opposite characteristics. The proposedearnings/price strategy is the reverse from that used within early US data (Basu 1977).Within theUK literature, Siganos (2011) finds that low earnings/price companies outperform counterparthigh earnings/price firms after 1988. Companies with missing required firm characteristics foreach strategy are excluded. Stocks with negative B/M and zero EPS are also excluded fromthe analysis of the book/market and earnings/price strategies, respectively.3 Financial firms areexcluded for the B/M strategy.

• Asset growth: this study ranks companies based on their total asset growth from fiscal the yeart − 2 to fiscal year t − 1 before the portfolio formation. The strategy buys short-sells companieswith low (high) growth in total assets (wc02999). Consistent with Cooper, Gulen, and Schill(2008), financial firms, companies with missing return/assets data and firms with zero totalassets have been excluded.

• Accruals: in line with Soares and Stark (2009), we estimate accruals as follows:

Accrualsi,t = (�CAi,t − �Cashi,t)

− (�CLi,t − �STDebti,t − �Divi,t − �Inti,t − �Taxi,t) − DEPi,t , (1)

where �CA shows change in total current assets (wc02201), �Cash shows change in cash andequivalents (wc02001), �CL shows change in total current liabilities (wc03101), �STDebt

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shows change in total short-term debt (wc03051), �Div shows change in dividend payable(wc03061), �Int shows change in interest payable (wc03062), �Tax shows change in taxespayable (wc03063) and DEP shows depreciation, depletion and amortisation (wc01151). Inline with Sloan (1996), variables are adjusted in line with the average of the beginning and endbook value of total assets (wc02999). The strategy buys companies with low operating accrualsand short-sells firms with high operating accruals. Financial firms are excluded. This study alsoexcludes firms that have missing current assets, current liabilities and depreciation, depletionand amortisation data. In line with Soares and Stark (2009), this study does not require allcharacteristics to be available, due to the large amount of missing data in some variables.4

• Momentum: this study ranks stocks on their performance over the previous year.5 Companiesthat have a missing daily return performance during the rank period have been excluded fromthe sample. The strategy buys stocks that have performed the best and short-sells stocks thathave performed poorly.6

Portfolio returns are calculated, using the compound method following the approach of Liu andStrong (2008), according to which equal-weighted portfolio returns are calculated after adjustingfor the daily performance of each individual firm over time in comparison to that of the portfolio.The following formula is employed to estimate portfolio returns:

Rp,t =n∑

i=1

∏t−1t=1 (1 + Ri,t)∑n

j=1

∏t−1t=1 (1 + Rj,t)

Ri,t , (2)

where Rp,t is the return for portfolio p at day t, Ri,t is the return for share i in portfolio p at day t,Rj,t shows the performance of the portfolio j that shares i is included and nis the number of sharesin the portfolio. The estimation of portfolio returns on the first day of the construction is simplyan average of the individual share returns.

Market value data (market capitalisation and price) reflect the data at the time of portfolioformation. Academic studies leave typically a 6-month gap between financial-year-end values(return/assets, book value, earnings, total assets and all variables used in the estimation of accruals)and portfolio selection to ensure that information has been incorporated into share prices. However,UK companies with a financial-year end on or after twentieth January 2007 are allowed a maximum4-month period in which to make their financial statements public7 and, therefore, this study formsportfolios in the beginning of July 2007, May 2008 and May 2009. The frequency of short-sellingdata used by Data Explorers in the investment strategies is annual at the portfolio formation date.If companies are delisted during the period subsequent to the portfolio formation, we use thebankruptcy announcements made by HM Revenue and Customs to determine which stocks havebecome valueless and set the equivalent return to a 100% loss.8

This study also uses the top/bottom deciles to define the number of companies that arebought/sold-short. All long/short deciles are defined, using three alternative methodologies: first,in line with conventional academic literature, we form unconstrained long/short deciles, assumingthat all firms can be sold-short. Second, we narrow the number of firms identified previously in theshort portfolio to include only those that are available to borrow. We refer to this stock selectionmethodology as ‘Constrained I’, offering a reliable estimation of profits in the short position.Third, the study explores the profitability of the so-called long-short ‘zero-cost portfolios’ thatassume that the short seller is allowed to use the proceeds from the short position to finance thelong position.9 We refer to this methodology as ‘Constrained II’. This methodological approach

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to form long/short deciles incorporates only firms that could be sold short in the short-portfolios;but also sets both long and short portfolios to include an equal number of firms.

4. Empirical results

We first explore the impact of stock-borrowing constraints on the profitability of investmentstrategies. In line with conventional academic literature (the appendix reviews the empirical find-ings of prior literature), we initially investigate the profitability of the unconstrained type ofstrategies that allow short-selling of the full list of companies identified by Datastream. Table 3shows the compound/average/median/risk-adjusted returns for the strategies.10 Results are rela-tively robust to alternative return estimations. The return/assets (Panel 1), size-reverse (Panel 2)and earnings/price (Panel 4) strategies appear to generate significant compound profitability atthe level of 1.80 (−0.15 + 1.95), 2.56% and 3.92% per month respectively, driven by the shortpositions.11 These results are consistent with prior UK studies (Dimson and Marsh 1999; Siganos2011) and with those found by Chen and Zhang (2010) in US data regarding the return/assetsstrategy.

On the contrary, the results for all other strategies give a different picture. For example, thecompounded monthly returns of the hedge position for the asset growth (Panel 5) and book/market(Panel 6) strategies generate large economically and statistically significant losses with −1.88%and −2.88%; while, the accruals and momentum strategies (Panels 7 and 8) fail to generate anysignificant profitability (−0.39% and 0.00%, respectively). This result on the accrual strategydiffers considerably from earlier findings in USA (Sloan 1996), but within the UK literature(Soares and Stark 2009), the profitability of the accruals strategy has always been relativelyweaker. On the contrary, the failure of the momentum strategy to generate any economicallyand statistically significant profitability appears contrary to all prior studies in most countries,including those on the UK stock market (Liu, Strong, and Xu 1999; Griffin, Ji, and Martin 2003).This may be an early sign that momentum returns are about to disappear/reverse due to changes inthe amount (and nature) of capital pursuing this strategy.12 Although theoretically such strategyis market neutral, generating profits both in bull and bear markets, another plausible explanationfor this result might be the period under examination. The extreme volatility swings in the marketduring the period of September 2008 to April 2009 caused by the high levels of uncertainty overthe viability of the banking sector and the financial system in general has resulted in the majorityof stocks to perform in a similar manner; hence, destroying the familiar pattern of the winner/losereffect. Over the following years, empirical findings will show whether the momentum strategyhas become a victim of its own popularity or not.

Next we compare the profitability of unconstrained-type portfolios with that of portfolios con-strained to short-selling only stocks that can be borrowed.13 Despite the unprofitable performanceof some of these strategies, we explore the impact of borrowing constraints on each of the strate-gies’ profitability. An alternative could have been to focus only on those strategies (return/assets,size reverse and earnings/price) that are significantly profitable. Estimating the impact of borrow-ing constraints for all strategies offers a clearer view of the impact of borrowing constraints onstrategies’ profitability.

We first investigate the profitability of each strategy when shorting is limited to only thosestocks that are available for borrowing. Results are shown in the ‘Constrained I’ column ofTable 3. Note that the profitability of the long position for the strategies remains unchangedbetween Unconstrained and Constrained type I strategies by construction. Interestingly, we findthat the profitability of the constrained portfolios tends to be greater than that of the unconstrained

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Table 3. Unconstrained and constrained I and II strategies’ profitability (%).

Unconstrained Constrained I Constrained II Unconstrained Constrained I Constrained II(1) (2) (3) (1) versus (2) (1) versus (3) (4) (5) (6) (4) versus (5) (4) versus (6)

Panel 1: Return/assets Panel 2: Size reverseLong −0.15 −0.15 −0.32 −0.17 −0.17 0.00Short −1.95 −3.40 −2.90 −2.73 −10.18 −2.68Hedge 1.80* 3.25* 2.58*** 1.45 0.78 2.56** 10.01** 2.68** 7.45 0.12Mean (median) 1.73 (2.56) 2.89 (5.37) 2.60 (1.65) 2.76 (0.92) 8.17 (3.60) 2.88 (0.03)Risk-adjusted 1.48* 2.83* 2.40*** 2.50*** 7.84** 2.87***Stdev 1.23 2.05 1.23 1.80 5.22 1.65

Panel 3: Size Panel 4: Earnings/priceLong −2.73 −2.73 −2.68 0.06 0.06 0.77Short −0.17 −0.20 0.00 −3.86 −4.22 −3.79Hedge −2.56** −2.53** −2.68** 0.03 −0.12 3.92*** 4.28*** 4.56*** 0.36 0.64Mean (median) −2.75 (−0.92) −2.73 (−1.23) −2.88 (−0.03) 3.92 (3.43) 4.21 (3.48) 4.52 (4.61)Risk-adjusted −2.50*** −2.47*** −2.83*** 3.92*** 4.13*** 4.53***Stdev 1.80 1.81 1.65 0.89 1.31 1.23

Panel 5: Asset growth Panel 6: Book/marketLong −2.14 −2.14 −2.14 −2.24 −2.24 −2.59Short −0.27 −1.10 −0.83 0.63 −0.23 −0.15Hedge −1.88*** −1.05 −1.32* 0.83 0.56 −2.88*** −2.01** −2.44*** 0.87 0.45Mean (median) −1.92 (−0.65) −1.26 (−2.09) −1.42 (−0.75) −2.98 (−0.49) −2.14 (−0.76) −2.44 (−1.88)Risk-adjusted −1.77** −0.95 −1.28* −2.78*** −1.73*** −2.18***Stdev 0.98 1.47 1.10 1.39 1.30 1.11

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Panel 7: Accruals Panel 8: MomentumLong −1.40 −1.40 −0.78 −1.25 −1.25 −1.56Short −1.01 −2.53 −1.57 −1.25 −0.71 −1.81Hedge −0.39 1.13 0.79 1.52 1.18 0.00 −0.54 0.25 −0.54 0.25Mean (median) −0.41 (0.00) 0.95 (0.00) 0.70 (0.17) 0.00 (0.99) −0.96 (0.14) 0.23 (0.38)Risk-adjusted −0.41 1.18 0.74 −0.14 −0.98 0.08Stdev 1.08 1.53 0.88 1.01 1.95 1.36

Notes: This table reports the profitability on the short and long position of the different strategies. Notice that the profitability of the strategies is measured on a monthly basis. %>0 shows the percentage of positivedaily hedge returns and Stdev shows the standard deviation of daily hedge returns. Constrained I strategies short-sell only firms that are available for borrowing and Constrained II strategies use only firms availablefor borrowing and constructs long/short portfolios to incorporate an equal number of firms. A Return/Assets strategy buys companies with high return/assets ratios and sells-short the counterpart firms with lowratios. A Size Reverse strategy buys large capitalisation firms and short-sells small capitalisation firms. A Size strategy buys small capitalisation firms and sells-short the counterpart large capitalisation firms. AnEarnings/Price strategy buys low earnings/price firms and sells short high earnings/price firms. An Asset Growth strategy buys (sells-short) companies with low (high) growth in total assets. A Book/Market strategybuys high book/market firms and sells short low book/market firms. An Accruals strategy buys companies with low operating accruals and sells-short firms with high operating accruals (as measured by Equation(1)). A Momentum strategy buys firms that have performed the best and sells-short companies that have performed the worst over the previous 12 months.We proxy risk-adjusted returns using the three-factor model (Fama and French 1993) and estimate the following OLS regression: Ri,t = a + bRmt + cSMBt + dHMLt + ut where Ri,t is the daily returns of strategy iat time t, Rmt is the market return (FTSE-all share) at time t, SMBt is the difference in daily returns between portfolios with small and large capitalisation at time t and HMLt is the difference in daily returns betweenportfolios with high and low book/market at time t.*Significant at the 10% level.**Significant at the 5% level.***Significant at the 1% level.

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portfolios. The largest difference in profitability between the two types of portfolios is documentedfor the ‘size reverse’ strategy with a difference of 7.45% (hedged returns of 2.56 and 10.01%,respectively). One possible explanation for this difference is the increased risk of the constrainedmethodology with a reported standard deviation of 5.22%. Nonetheless, the reported performancefor both methodologies on a risk-adjusted basis is very similar, 2.50 and 7.84% (in contrastto 2.56% and 10.01%, respectively), suggesting that a risk explanation is not very convincing.This pattern of return difference between the first and second stock selection methodologiesremains similar for almost all strategies under consideration. In terms of statistical significance,even though the level of profitability tends to increase in constrained portfolios, the level ofstatistical significance remains rather similar to that reported for unconstrained portfolios, and thisis attributed to the increase in the standard deviation of hedge returns. Overall, in line with Geczy,Musto, and Reed (2002) who use US data, we find that the difference in profitability betweenunconstrained- and constrained-type methodologies is not statistically significant, showing thatborrowing constraints do not impact significantly on the profitability of the alternative investmentstrategies.

To understand why the volatility of hedge returns for the Constrained I strategies has increased,Table 4 explores the percentage and the number of companies (shown in parentheses) that wereavailable for borrowing in the short portfolio. We find that for most strategies, only a smallpercentage of the firms identified by Datastream are available for lending. For example, only4.14% (6 firms) of the firms identified for lending could be borrowed during the first period forthe Return/Asset strategy. To highlight the significance of market capitalisation on whether acompany is offered for lending, we compare the results found in the Size Reverse strategy incomparison to those found in the Size strategy and find that at maximum (at minimum) 1.58(91.22)% of the smallest (largest) capitalisation decile of firms could be borrowed.

Since the Constrained I type of strategy covers only a small number of firms in the short portfolio,we also explore the profitability of the Constrained II-type strategies when both long and shortportfolios include an equal number of firms to achieve a zero-cost portfolio. The ‘ConstrainedII’ column of Table 3 shows the results. We find that the profitability of investment strategiesis relatively similar among Constrained I and Constrained II-type portfolios, while the standarddeviation of returns for the Constrained II-type portfolios is lower. The differences between thereturns from unconstrained and Constrained II-type portfolios are not statistically significanteither. The reported results indicate that stock borrowing constraints do not alter to a large extentthe picture on the performance of various investment strategies when examined in the UK equitymarket.

These findings are further corroborated by the examination of the precise level of stock- lendingfees for each of the strategies under consideration. The summary statistics for the full period andfor each of the three sample periods under examination are reported in Table 5. As the resultssuggest, the stock borrow fee for the majority of the strategies is less than 1% per annum. Theaverage (median) fee for the ‘accrual strategy’, for example, is 0.78 (0.36)% per annum. Only the‘size reverse strategy’, which requires short positions in the smallest capitalisation firms, exhibitsa stock borrowing fee in excess 1.5% per annum. This result shows that previous UK studies(Agyei-Ampomah 2007), which have explored the post-cost profitability of investment strategiesand assumed that the short-selling fee is flat for all shares at 1.50% per annum, have overestimatedthe actual cost.

Overall, these results indicate that stock loan unavailability and stock-borrowing fees do notexplain the persistence of returns from anomaly-exploiting quantitative investment strategies onthe London Stock Exchange.

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Table 4. Stock loan availability for each strategy.

Return/assets Size reverse Size Earnings/price Asset growth Book/market Accruals Momentum

First period 4.14 (#6) 0.00 (#0) 91.22 (#135) 22.97 (#17) 12.41 (#18) 31.87 (#29) 17.48 (#25) 5.41 (#10)Second period 6.62 (#9) 1.58 (#2) 98.43 (#125) 36.25 (#29) 18.25 (#25) 37.83 (#28) 16.55 (#24) 8.29 (#15)Third period 4.55 (#6) 0.78 (#1) 98.43 (#125) 22.51 (#18) 17.42 (#23) 34.67 (#26) 10.69 (#14) 6.71 (#11)

Note: This table shows the percentage and the number of firms (shown in parentheses) that are available for borrowing.

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Table 5. Estimation of short-selling costs (%).

Return/assets Size reverse Size Earnings/price Asset growth Book/market Accruals Momentum

Panel 1: Full periodConstrained I

Av/Median 1.47/0.75 0.61/0.52 0.19/0.11 1.34/0.67 0.93/0.51 0.53/0.27 0.78/0.36 1.11/0.49Min/Max 0.10/5.00 0.25/1.06 0.01/2.74 0.09/8.21 0.08/5.00 0.04/5.00 0.09/6.00 0.09/5.00

Constrained IIAv/Median 1.32/0.75 2.54/2.07 0.14/0.09 1.50/0.61 0.75/0.42 0.50/0.26 0.73/0.37 1.12/0.60Min/Max 0.08/5.00 0.10/11.20 0.01/2.28 0.04/32.63 0.08/5.00 0.04/5.00 0.06/6.00 0.09/5.00

Panel 2: First periodConstrained I

Av/Min/Max 1.93/0.10/4.13 n/a 0.12/0.02/3.22 0.90/0.10/3.75 0.74/0.08/1.97 0.56/0.08/2.10 0.55/0.09/1.37 0.89/0.13/3.60Constrained II

Av/Min/Max 1.16/0.08/4.13 2.21/0.10/5.00 0.09/0.05/0.21 0.66/0.04/3.75 0.68/0.08/4.25 0.55/0.08/2.10 0.54/0.08/2.10 0.72/0.10/3.60

Panel 3: Second periodConstrained I

Av/Min/Max 0.83/0.10/3.16 0.79/0.52/1.06 0.16/0.03/2.74 0.96/0.09/5.00 0.92/0.09/4.25 0.33/0.09/1.50 0.45/0.09/3.00 0.68/0.09/5.00Constrained II

Av/Min/Max 1.08/0.09/4.94 2.12/0.10/6.00 0.13/0.03/1.99 1.18/0.09/6.00 0.66/0.09/4.25 0.33/0.09/1.50 0.59/0.09/3.49 1.00/0.09/5.00

Panel 4: Third periodConstrained I

Av/Min/Max 1.99/0.01/6.37 0.25/0.25/0.25 0.32/0.01/2.28 2.04/0.17/8.21 1.11/0.08/5.00 0.72/0.04/5.00 1.73/0.11/6.00 1.90/0.20/5.00Constrained II

Av/Min/Max 1.99/0.17/5.00 3.39/0.20/11.20 0.22/0.01/2.28 2.68/0.17/32.63 0.95/0.08/5.00 0.67/0.04/5.00 1.18/0.06/6.00 1.80/0.20/5.00

Note: This table shows the level of the short-selling fee for the full period and for each period separately.Dow

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5. Conclusions

This study contributes to the extant literature in two ways. First, this study offers a descriptionof the UK share-lending market. In line with the US share-lending market, this study shows thatborrowing of shares is common practice in the UK stock market, with most large capitalisationcompanies being easy to borrow. There are, though, still a large number of small capitalisationfirms that cannot be borrowed. Firms that can be borrowed represent 95.58% of the market valueof the UK stock market. The short-selling fee for the majority of firms is relatively low: 69% ofcompanies can be borrowed at a cost of less than 1% per annum. This study finds that previousstudies (Agyei-Ampomah 2007) have over-estimated the level of the lending fee since the annualfee for these strategies’ short positions is mostly less than 1% per annum.

Second, this study investigates the profitability of eight long/short investment strategies. Thisstudy calculates precisely the short-selling fee and, based on information from Data Explorers,incorporates only those short positions that are feasible. This differs from previous UK studiesthat assume that all available firms from DataStream can be sold-short. Interestingly, we find thatonly a small percentage of the firms identified by Datastream are available for lending – normallyless than 30% of firms. When we restrict short portfolios in firms that are available to short-sellor/and generate long-short portfolios in shares that are available for lending, the profitability of thestrategies generally increases, but the differences in profitability are not statistically significant.These results indicate that lending constraints are not behind the perseverance of stock returnanomalies in the London Stock Exchange.

Notes

1. CREST is another database that offers UK short-selling fee data, but its coverage compared to Data Explorers is limited.Data Explorers incorporates data covered by CREST, plus additional data made available by market participants. The‘active utilisation’ data is proprietary to Data Explorers and is used extensively in this study.

2. The Returns Index (RI) datatype determines daily share returns and is adjusted for dividend payments. Due to the useof a relatively short sample period, this study employs daily returns rather than the conventional monthly returns. Thisoffers a more accurate estimation of portfolio returns and boosts the number of observations used when estimatingstatistically significant levels. Notice that this study uses weekly short-selling observations since higher frequency ofsuch data is not required for the estimation of the relevant portfolios. When investment portfolios are constructed onthe basis of short-selling activity, portfolios are formed and rebalanced annually. Weekly frequency in short-sellingdata is used to offer a description of the short-selling activity in the UK market.

3. Datastream records zero earnings per share for companies that have announced negative earnings.4. For example, Datastream offers interest payable data only in 6.96% of firms in the sample.5. The first ranking period is extended from July 2006 to June 2007 in order to enable all strategies in the study to be

tested in the same performance period.6. Note that the estimation of returns among alternative strategies is based on alternative companies. If companies were

required to fulfil all conditions to be included in the sample, we would have reduced the sample size significantly andconsidered only very large capitalisation companies. The profitability of the strategies would also have been differentfrom that investors would have generated by following a particular strategy.

7. http://fsahandbook.info/FSA/html/handbook/DTR/4/1 (accessed August 2010).8. Note that the initiation of new short-sale positions in 29 financial companies was prohibited by the Financial Services

Authority between 19 September 2008 and 16 January 2009. The strategies followed in this study do not require newshort-positions in the particular period. Existing short positions in financial companies could be maintained.

9. Note that in practice the proceeds from short-selling are normally not available to the short-seller, but are used ascollateral with the lender to provide security for the borrowed shares.

10. We employ the most commonly used measurement within the academic literature to proxy risk – the three-factormodel (Fama and French 1993). Briefly, companies with negative book values and financial companies are excludedfrom the sample and rebalancing of portfolios occurs yearly. All eligible companies that arrive from Datastream areranked into three book/market portfolios (L-30%, M-40% and H-30%) and two size portfolios (S and L, based on

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the median market values) independently and we construct six portfolios LS, LL, MS, ML, HS and HL. The SMBt

factor reflects the daily return difference on the average three small size portfolios (LS, MS and HS) and the threelarge size portfolios (LL, ML and HL) and accordingly, the HMLt factor reflects the daily return difference betweenthe average of the two high-book/market portfolios (HS and HL) and the two low-book/market portfolios (LS, LL).The risk-adjusted returns are shown by the alpha of an OLS regression that uses the returns of an investment strategyas the dependent variable and market, SMBt and HMLt as independent variables.

11. Note that by construction, portfolio long/short returns in the size strategy are the same with those reported in theshort/long portfolios respectively in the size reverse strategy.

12. For example, see recent Media coverage on the profitability of momentum strategy (Johnson 2008) and the recentintroduction of momentum funds by AQR Capital Management.

13. Notice that brokers could locate a stock from their own inventory to undertake short-selling activity. Given that thisform of activity is proprietary and to the best of our knowledge there is no database currently available that incorporatessuch information, there is little we can do to get access to such confidential data. Nonetheless, as the use of DataExplorers is estimated to cover around 80% of the short-selling activity in the UK market (Prado, Brounen, andVerbeek 2009), we have no reason to believe that our inferences would have been different if such private inventoryinformation were available.

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Appendix. Findings of prior literature

MonthlyStudy Strategy Market Sample period return (%)

Chen and Zhang (2010) Return/assets US 1972–2006 0.96Banz (1981) Size US 1926–1975 1.52Dimson and Marsh (1999) Size reverse UK 1989–1997 0.54Siganos (2010) Size reverse UK 1988–2009 1.55Basu (1977) Earnings/price US 1956–1971 0.58Siganos (2010) Earnings/price UK 1988–2009 2.53Cooper, Gulen, and Schill (2008) Asset growth US 1963–2003 1.73Rosenberg, Reid, and Lanstein (1985) Book/market US 1973–1984 0.36Sloan (1996) Accruals US 1962–1991 0.87Soares and Stark (2009) Accruals UK 1989–2004 0.60Jegadeesh and Titman (1993) Momentum 12 × 12 US 1965–1989 0.68Liu, Strong, and Xu (1999) Momentum 12 × 12 UK 1977–1996 0.85

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