The geography of hedge funds melvyn teo

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Article by Singapore-based professor Melvyn Teo in which he demonstrates that local hedge funds outperform internationally-based ones by some 3.7 percent. This is a logical result, because local participants in financial markets can get relevant information quicker. It is especially an advantage when dealing with non-listed securities and in Emerging or Frontier Markets where the level of information gathering and the quality of information is lacking.

However, international hedge funds, especially those in the US and UK seem to be better equipped when it comes to attracting capital.

Almost to the extent that Teo suggests that they might be willing to sacrifice performance for the sake of trying to get bigger: quantity instead of quality!

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The geography of hedge funds melvyn teo

  1. 1. The Geography of Hedge Funds* Melvyn Teo** AbstractThis paper analyzes the relationship between the risk-adjusted performance of hedge funds andtheir proximity to investments using data on Asian hedge funds. We find, relative to anaugmented Fung and Hsieh (2004) factor model, that hedge funds with a physical presence (heador research office) in their investment region outperform other hedge funds by 5.28 percent peryear. The local information advantage is pervasive across all major geographical regions andinvestment styles, but is strongest for Emerging Market and Event Driven funds. These resultsare robust to adjustments for fund fees, serial correlation, backfill bias, and incubation bias.JEL classifications: G11; G12; G23Keywords: hedge fund performance; alpha; factor models; geography______________________* We are indebted to Vikas Agarwal, George Aragon, William Fung, Mila Getmansky, RobertKosowski, and Bing Liang for many helpful discussions and to AsiaHedge and EurekaHedge forgenerously providing us with the data. This research is inspired by suggestions from WilliamFung. We alone are responsible for all errors.** Address: Lee Kong Chian School of Business, Singapore Management University, 50Stamford Road, #04-01, S(178899), Singapore. E-mail: melvynteo@smu.edu.sg, Tel: +65-6828-0735, and Fax: +65-6828-0777.
  2. 2. Does distance affect hedge fund performance? If not, why do some hedge funds locateclose to their investments while others prefer to monitor their investments from afar and from thecomfort of home? Anecdotal evidence1 suggests that proximity to investments may be helpful forhedge funds. Being near their investments or target firms allows hedge funds to maintain closecontact with senior management and stay abreast of the latest developments. Fund managers canlearn valuable information from other local firms along the supply chain by being on theground.2 Nearby funds, who are also substantial stakeholders, can more easily engage inconstructive shareholder activism and exert pressure on management to make shareholderfriendly improvements.3 In this paper, we investigate the link between funds’ proximity to their primaryinvestment markets and hedge fund risk-adjusted returns.4 We show that funds that have aphysical presence (head office or research office) in their main investment region outperformother funds on a risk-adjusted basis by on average 5.28 percent per year (t-statistic = 4.44). Thesuperior performance of funds with investment region presence cannot be attributed to lowerfees, greater backfill and incubation bias (Fung and Hsieh, 2000; Liang, 2000; Brown,Goetzmann, and Ibbotson, 1999), or illiquidity (Getmansky, Lo, and Makarov, 2004). We obtainsimilar results with fund alpha derived from pre-fee returns, from backfill and incubation biasadjusted returns, and from unsmoothed returns adjusted for serial correlation. The hypotheticalstrategy of buying nearby funds and shorting distant funds yields risk-adjusted returns of about4.54 percent per year (t-statistic = 3.09). This spread alpha is robust across all major fundinvestment styles and investment regions, and is substantially higher for styles/regions where thelocal informational advantage is likely to be stronger or more relevant. For instance, the nearbyEmerging Markets fund portfolio outperforms the distant Emerging Markets fund portfolio by1 In a Wall Street Journal report, Christophe Lee, chairman of the Alternative Investment Management Associationchapter in Hong Kong, remarked that a distinct advantage for Hong Kong vis à vis Singapore as a hedge fund hub isits proximity to China, which is home to many investment opportunities (Raagas De Ramos, 2006).2 For instance, Cohen and Frazzini (2006) show that a long/short strategy that takes advantage of information inconsumer/supplier links can earn substantial risk-adjusted returns.3 See Wighton (2006) for recent examples of investor activism by hedge funds.4 To clarify, we do not endeavor to explain why hedge funds locate where they do, but rather we show how hedgefund geography affects hedge fund performance. To explain hedge fund location, one would have to take intoaccount differences in air quality, income and corporate taxes, proximity to hedge fund clients, and the supply ofprime brokerage services as well. Also, we note that differences in income and corporate tax regimes are not centralto the arguments in the paper since income and corporate taxes are levied on manager income and fund profits,respectively, and not on the underlying portfolios themselves. Hence, the effects of income and corporate taxesshould not show up in fund risk-adjusted performance. 1
  3. 3. 32.06 percent per year. These results suggest that nearby hedge funds enjoy an informationaladvantage. In addition, the results are particularly relevant to hedge fund managers, Fund ofFunds, and other fund investors. Our study focuses on hedge funds that invest primarily in Asian financial markets. Theadvantage of Asian hedge fund data is that we can derive a simple yet meaningful distancemeasure by using fund location and fund investment region information. For instance, on onehand, we classify funds investing in the Asia including Japan region but located in the UnitedStates or United Kingdom as distant funds or funds without investment region presence. On theother hand, we classify funds investing in the Asia including Japan region and located inSingapore, Hong Kong, or Tokyo as nearby funds or funds with investment region presence. Toconduct the same analysis with the typical U.S.-centric fund database5 we will need informationon the location of fund holdings. Unfortunately, hedge fund holdings (including short positions)are highly confidential and rarely, if at all, available to researchers. To adjust for risk in Asian hedge funds, we employ an augmented Fung and Hsieh (2004)factor model. Fung and Hsieh (1999, 2000, 2001), Mitchell and Pulvino (2001), and Agarwaland Naik (2004) show that hedge fund returns relate to conventional asset class returns andoption-based strategy returns. Building on this pioneering work, Fung and Hsieh (2004) showthat their parsimonious asset-based style factor model can explain up to 80 percent of thevariation in fund portfolio returns. We augment the Fung and Hsieh (2004) factor model withadditional equity factors, which we derive from a principal components analysis approach[following Fung and Hsieh (1997)]. The equity factors we use are the excess return on the Asiaex Japan equity index and the excess return on the Japan equity index. The augmented factormodel explains up to 70 percent of the variation in Asian portfolio fund returns and allows us toabstract from risk in our analysis of geography and hedge fund performance. As a robustnesscheck and in response to concerns that fund returns may resemble the payoffs from writingoptions on the equity market (Agarwal and Naik, 2004), we also include in the factor modeladditional factors derived from call and put options on the Japan Nikkei 225 index, but findqualitatively similar results.65 Examples of such hedge fund databases include TASS, HFR, MSCI, and CISDM.6 The results are also robust to the inclusion of an Asian size factor in the performance evaluation model. 2
  4. 4. This paper presents novel results linking distance to hedge fund alpha. In doing so, webuild on several themes. Fung and Hsieh (2004) and Agarwal and Naik (2004) show that fundrisk can be explained by loadings on option-like factors. By leveraging on these results,Kosowski, Naik, and Teo (2006) show that top fund risk-adjusted performance cannot beexplained by luck, or sample variation. Consistent with this, they find that fund alpha persistsover annual horizons. Their results indicate that the top fund managers possess asset selectionskills. Our results suggest that part of this skill stems from the ability to take advantage of localinformation. Funds that invest in nearby markets tend to exhibit greater skill (i.e., alpha) thanfunds that invest in distant markets. French and Poterba (1991), Cooper and Kaplanis (1994),and Tesar and Werner (1995) show that investors severely overweight their portfolios towarddomestic assets. Our results shed light on this international home bias puzzle. Related to this, wealso contribute to a growing literature on distance and investment performance.7 Coval andMoskowitz (2001) demonstrate that U.S. mutual fund managers are better at picking stocks ofnearby firms than stocks of distant firms. Ivković and Weisbenner (2005) witness a similarpattern with U.S. retail investors. At the same time, Malloy (2005) finds that U.S. equity analystsissue more accurate earnings forecasts for nearby firms than for distant firms. On theinternational front, Hau (2001) reports, using data on professional traders, that local tradersoutperform foreign traders in the German market. Choe, Kho, and Stulz (2005) and Dvorak(2005) provide evidence that foreigners underperform other investors in Korea and Indonesia,respectively. Bae, Stulz, and Tan (2006) document, in their sample of 32 countries, that localequity analysts issue more accurate earnings forecasts than do foreign analysts. In particular,they find that the accuracy differential is stronger in Emerging Markets, where less informationis disclosed by firms.8 However, none of the aforementioned studies investigate the effects of distance on hedgefund performance. By focusing on hedge funds instead of mutual funds, we can convenientlyabstract from diversification issues that affect mutual funds, since hedge funds are typically pure7 See also Krugman (1991), Lucas (1993), Audretsch and Feldman (1996), Loughran and Schultz (2005), andBecker (2006) for related studies on the economic importance of geography.8 The international evidence is mixed however. Seasholes (2000) and Grinblatt and Keloharju (2000) argue thatforeign investors have access to better resources and expertise than local investors, which may allow them toovercome any local informational advantage. Consistent with this, they show that foreigners outperform locals inTaiwan and Finland, respectively. To the extent that this is true in our sample (i.e., foreign funds are moresophisticated than local funds), it biases our results downwards since we compare the performance of nearby localand foreign hedge funds with the performance of distant foreign hedge funds. 3
  5. 5. alpha bets. Further, unlike mutual funds, hedge funds can short sell and take advantage ofnegative information. Hence, an analysis of hedge fund performance allows for a more powerfultest of the local information advantage hypothesis. Consistent with this, at the fund level, mutualfunds that invest more in local stocks do not significantly outperform mutual funds that investmore in distant stocks [see pg. 823 of Coval and Moskowitz (2001)]. In contrast, our resultsindicate that one can earn significant economic rents by investing in hedge funds that operatenear their investment markets. Moreover, in line with the view that hedge funds are better able totake advantage of local information via short selling and the use of derivatives, the risk-adjustedspread between nearby and distant hedge fund portfolios (4.54 percent per year) is more thanthree times the risk-adjusted spread between the local and non-local stock portfolios of mutualfunds (1.18 percent per year) and of households (1.08 percent per year).9 The rest of this paper is as follows. Section 1 describes the data while Section 2 lays outthe framework for analyzing fund risk-adjusted performance. Section 3 presents the resultslinking distance to fund risk-adjusted performance. Section 4 concludes.1. Data We hand collect data on Asian hedge funds from two hedge fund databases:EurekaHedge and AsiaHedge. EurekaHedge is maintained by EurekaHedge Advisors Pte Ltdwhile AsiaHedge is maintained by Hedge Fund Intelligence. Both databases include funds whichinvest a significant portion of their assets in Asian markets. The sample consists of monthly fundreturn data from January 1999 to December 2003. While AsiaHedge started collecting fundreturn data from 1999, EurekaHedge only started collecting fund return data from 2000 (butincludes fund returns since inception). Hence, we focus on the period from 2000 onwards toameliorate survivorship bias. Our sample also includes data on a host of fund characteristics including managementfee, performance fee, redemption period, investment style, investment geographical region, fundlocation, fund size, fund family size, and fund minimum investment. These fund characteristicsare recorded in year 2003 and, for our purposes, we take these characteristics as constant over the9 See the alpha spreads in Table 1 of Coval and Moskowitz (2001) and Table IX of Ivković and Weisbenner (2005). 4
  6. 6. sample period.10 Since minimum investments are sometimes quoted in currencies other than USdollar, we convert all minimum investments to US dollars using exchange rates on Dec 31st 2003so as to facilitate meaningful comparison. AsiaHedge records fund and family size in distinctranges. Hence, we convert fund and family size values to size categories, which condense sizeinformation into a number from one to ten. Details on the size categories are available inAppendix A. Naturally, the hedge fund sample includes both dead and live funds. Of the 325 fundswith at least one month of return data in the sample period, 23 are dead funds. As such, our deadfunds to total funds ratio of 23 to 325 is higher than the dead funds to total funds ratio of 27 to746 for the Hedge Fund Research (henceforth HFR) database used by Agarwal and Naik (2000).To the extent that the true survival rates are comparable across different databases, this suggeststhat survivorship issues11 (Brown, Goetzmann, and Ibbotson, 1999) are mitigated in our dataset. To verify the accuracy of the return data in our dataset, we collect data on hedge fundreturns from TASS, a popular database used in hedge fund research. Next, we match by handfunds from the combined AsiaHedge and EurekaHedge database with those from TASS usingfund name. We are able to obtain matches for 74 funds that have at least 24 months of commonreturn data in the January 2000 to December 2002 period (we are only able to obtain TASSreturn data prior to 2003). Then, we calculate the mean correlation in returns for these funds. Wefind that the mean correlation in fund returns from the two databases is 0.97, adding comfort toour use of the return data. As mentioned, the data contains information on fund investment region and fundlocation. In addition, some funds also provide information on the location of their researchoffices. This allows us to determine whether funds have a physical presence (head or researchoffice) within their investment region. In Table 1, we breakdown the funds in our sample byinvestment style and investment region, and report the following summary statistics: the total10 This assumption seems reasonable for all the fund characteristics except fund size and fund family size. Our useof broad size categories in place of size values ameliorates the problem. But nonetheless, we will avoid making anyinferences on fund and family size. We shall revisit this issue later in the paper.11 One caveat is that any residual survivorship bias is still likely to be greater in our dataset than in the latest versionsof the HFR, TASS, or CISDM datasets where more dead funds have been added [see Kosowski, Naik, and Teo(2006)]. The underlying assumption we make is that survivorship bias does not systematically differ between fundswith investment region presence and funds without investment region presence. Such an assumption seemsreasonable given that the ratio of dead funds to the total number of funds is 7.1% both for funds with investmentregion presence and for funds without investment region presence. 5
  7. 7. number of funds, the number of dead funds, the number of return months, as well as the numberof funds with and without investment region presence. [Please insert Table 1 here] From the summary statistics, we can see that the sample spans a broad range ofinvestment styles and investment regions. While it is not surprising that the majority of fundsengage in the Equity Long/Short strategy, we also have Relative Value, Event Driven, Macro,Directional, Fixed Income, and Managed Futures funds in our sample. Further, the funds investin several geographical regions including Korea, Greater China, Taiwan, India, Asia excludingJapan, Asia including Japan, Australia/New Zealand, Emerging Markets, Global, and Japan only.More importantly, for all the major investment styles and regions with at least 20 funds (exceptfor the Global12 and Australia/New Zealand regions) there are both funds with investment regionpresence and funds without investment region presence. The variation in investment regionpresence within styles and regions facilitates our tests of hedge fund geography. We recognize that hedge fund data are susceptible to many biases (Fung and Hsieh,2000). These biases stem from the fact that, due to the lack of regulation amongst hedge funds,inclusion in hedge fund databases is voluntary. As a result, there is a self-selection bias. Forinstance, funds often undergo an incubation period where they rely on internal funding beforeseeking capital from outside investors. Incubated funds with successful track records then go onto list in various hedge fund databases while the unsuccessful funds do not, resulting in anincubation bias. Separate from this, when a fund is listed on a database, it often includes dataprior to the listing date. Again, since successful funds have a strong incentive to list and attractcapital inflows, these backfilled returns tend to be higher than the non-backfilled returns. In theanalysis that follows, we will repeat the tests after dropping the first 12 months of return datafrom each fund so as to ensure that the results are robust to backfill and incubation bias.2. Asset based style factors for hedge fund returns Fung and Hsieh (1999, 2000, 2001), Mitchell and Pulvino (2001), and Agarwal and Naik(2004) show that hedge fund returns relate to conventional asset class returns and option-based12 This necessarily holds by construction for funds investing in the Global region. 6
  8. 8. strategy returns. Building on this pioneering work, Fung and Hsieh (2004) propose an assetbased style (henceforth ABS) factor model that can explain up to 80 percent of the monthlyvariation in hedge fund portfolios. Their ABS model avoids using a broad based index of hedgefunds to model hedge fund risk since a fund index can inherit errors that were inherent in hedgefund databases. These problems have been noted by Brown, Goetzmann, and Ibbotson (1999),Fung and Hsieh (2000), and Liang (2000), and they include survivorship bias, backfill or instanthistory bias, selection bias, sampling differences across fund databases, lack of transparency inthe construction of fund indexes, and the choice of index weights.13 Fung and Hsieh (2004)choose their ABS factors by extracting common return components from sub-groups of hedgefunds that were classified by data vendors as having similar styles. Then, they link thosecommon sources of risk in hedge fund returns to observable market prices. In this section, we adopt a similar methodology to augment the Fung and Hsieh (2004)model so as to better explain risk in Asian hedge funds. By measuring fund performance relativeto the augmented Fung and Hsieh (2004) model, we can abstract from risk when analyzing therelationship between fund performance and fund proximity to investments. To identify additionalABS factors in Asian hedge fund space, we first perform principal components analysis of hedgefunds grouped by investment style and investment region. We do so for all style/regionintersections with at least five funds. Table 2 reports the R-squares from the regression of fundstyle/region returns on each of the top ten principal components as well as the percentage of thetotal variation explained by each principal component. It indicates that the first principalcomponent alone can account for about 51 percent of the variation in fund style/region returns.This compares favorably with Fung and Hsieh (1997) who find that the top five principalcomponents can account for 43 percent of the variation in offshore and onshore US fund returns. [Please insert Table 2 here] The R-squares in Table 2 reveal that the first principal component is likely to be an Asianequity factor since it is explains much of the return variation for funds in the Equity Long/Shortstyle (although it is also explains the returns of Event Driven funds in the Asia excluding Japanregion and Fixed income funds that invest in Emerging Markets). The second principalcomponent is most likely a Global Macro factor given the high R-square with Global Macrofunds (R-square = 0.71). Since the second principal component explains only 16 percent of the13 We refer the reader to Fung and Hsieh (2004) for an excellent review. 7
  9. 9. variation in fund style/region returns, we focus on linking the first principal component toobservable market prices. In this effort, we compute the correlation between the first principal component andvarious Asian equity market indices from Datastream, including the Asia Ex Japan, Asia, PacificBasin, and Japan market indices. We find that the correlation with the Asia Ex Japan marketindex is the highest at 0.82. In contrast, the correlations with the Asia, Pacific Basin, and Japanmarket indices are 0.64, 0.65, and 0.43, respectively. Hence, we augment the Fung and Hsieh(2004) model with two additional factors:14 the excess return on the Datastream Asia Ex Japanequity market index (henceforth ASIAMRF) and the excess return on the Datastream Japanequity market index15 (henceforth JAPMRF), to better explain variation in Asian hedge fundreturns. We evaluate the efficacy of the augmented factor model by regressing the Asian hedgefund portfolio on the factors from the augmented and non-augmented models, and comparing theadjusted R-squares. The augmented factor model is as follows: rim = a iM + biM SNPMRFm + c iM SCMLC m + d iM BD10 RETm + eiM BAAMTSYm + f iM PTFSBDm + g iM PTFSFX m + hiM PTFSCOM m + j iM ASIAMRFm (1) + k iM JAPMRFm + ε imwhere m = 1,…,M, rim is the monthly return on portfolio i in excess of the one-month T-billreturn, SNPMRF is the S&P 500 return minus risk free rate, SCMLC is the Wilshire small capminus large cap return, BD10RET is the change in the constant maturity yield of the 10 yeartreasury, BAAMTSY is the change in the spread of Moodys Baa - 10 year treasury, PTFSBD isthe bond PTFS, PTFSFX currency PTFS, PTFSCOM is the commodities PTFS, where PTFS isprimitive trend following strategy [see Fung and Hsieh (2004)]. [Please insert Table 3 here] The results in Panel A of Table 3 indicate that the augmented model well explainsvariation in Asian hedge fund returns. The adjusted R-square for the augmented model is 63percent or a meaningful 11 percent more than that for the non-augmented model. Also, it is not14 We deliberately avoid adding even more factors so as to maintain the parsimony of the factor model and keep thedegrees of freedom high in the regressions.15 The Japan equity factor (JAPMRF) is included to help explain the returns of Japan only funds. 8
  10. 10. surprising that the factor loading on ASIAMRF is statistically significant at the 1 percent level (t-statistic = 2.88), given the high correlation of ASIAMRF with the important first principalcomponent. Moreover, when we estimate similar regressions for fund portfolios stratified byinvestment style and investment region (for styles and regions with at least 20 funds), we findthat the augmented factor model can explain more than 30 percent of the variation in portfolioreturns for all styles and regions except for Relative Value funds. Consistent with this, we findthat for two of the three styles and for four of the six regions, the loadings on ASIAMRF arestatistically significant at the 10 percent level. As a comparison, the loadings on SNPMRF arestatistically significant at the 10 percent level for only three regions. Similarly, the loadings onSCMLC are statistically significant at the 10 percent level for only two regions. None of the otherfactors are statistically significant for more than one region or style. Clearly the ASIAMRF factordoes a fairly decent job of explaining Asian hedge fund returns. Not surprisingly, the Japan onlyfund portfolio loads significantly on the JAPMRF factor. In results not reported, we find that theR-square with the augmented factor model for the all funds portfolio is 70 percent. Thiscompares favorably with the R-squares in Table 2 of Fung and Hsieh (2004) where the returns onfund indices are evaluated relative to their factor model. As a prelude to analyzing the effects of hedge fund location on fund alpha, we investigatethe statistical significance of the top hedge fund alphas. If we find that the risk-adjustedperformance of the top hedge funds cannot be explained by luck or sampling variation, then itmakes the analysis that follows particularly relevant for investors since location may hold thekey to distinguishing genuinely good funds from the pack. If on the other hand, we find that theperformance of the top hedge funds can be explained by luck, then analyzing how locationaffects the cross-section of fund alpha will be of little interest to fund investors since even the topfunds are just riding on a lucky streak. To evaluate the significance of hedge fund alpha, while taking into account the cross-sectional distribution of hedge fund alpha as well as non-normalities in hedge fund returns, weemploy the bootstrap methodology used in Kosowski, Timmermann, Wermers, and White(2006). Details of the bootstrap procedure are available in Kosowski, Timmermann, Wermers,and White (2006). The basic idea is that by re-scrambling fund residuals across time and sortingfund alphas (and alpha t-statistics) across funds, we can empirically bootstrap the distributions ofthe top and bottom hedge fund alphas (and alpha t-statistics). The bootstrap p-values of the top 9
  11. 11. alpha and alpha t-statistic funds are displayed in Panel A of Table 4 and indicate that theperformance of the top funds cannot be attributed to luck alone.16 Of the 198 funds in ourbootstrap sample, by chance we expect ten funds to attain alphas greater than 12 percent peryear. In reality, 69 funds achieve annual alphas greater than 12 percent. One may quibble that these results are driven by backfill bias, incubation bias, orilliquidity. Hence, we remove the first 12 months of return data for each fund to mitigate backfilland incubation bias, and redo the bootstrap on the remaining sample of fund returns. We alsounsmooth the returns of the hedge funds using the Getmansky, Lo, and Makarov (2004)correction for serial correlation. In particular, we map the fund categories in Table 8 ofGetmansky, Lo, and Makarov (2004) to our fund categories (see Appendix B) and use the θ 0 , θ1 ,and θ 2 estimates for each fund category from their Table 8 to unsmooth fund returns. Then, were-do the bootstrap on the unsmoothed sample of fund returns. The bootstrapped p-values inPanels B and C of Table 4 indicate that none of our inferences change with these adjustments. [Please insert Table 4 here]3. Geography and hedge fund risk-adjusted performance The discussion thus far lays out the framework for analyzing fund alpha. Also, accordingto our bootstrap estimates, managers of the top alpha funds seem to possess skill. But are thesetop alpha funds also funds with investment region presence? Does part of their “skill” stem froma local informational advantage? It remains to investigate the relationship between hedge fundgeography and risk-adjusted return or alpha.3.1. The cross-section of hedge fund alpha To this end, we estimate cross-sectional regressions of hedge fund alpha, measuredrelative to the augmented Fung and Hsieh (2004) model, on an indicator variable for investmentregion presence. We set investment region presence equal to one for hedge funds with a headoffice or research office in their respective investment regions. Otherwise, we set investment16 We perform the bootstrap analysis on funds with at least 24 months of return information. Similar results obtainfor funds with at least 36 months of returns. 10
  12. 12. region presence equal to zero. We estimate both a univariate regression, and a multivariateregression, where we control for hedge fund characteristics that may affect fund performance. To be precise, following Carhart (1997), we first calculate monthly fund abnormal returnor alpha as fund excess returns minus the factor realizations times loadings estimated over theentire sample period [see Eq 5. of Carhart (1997)]. Hence, we have ALPHAim ≡ rim − (biM SNPMRFm + ciM SCMLCm + d iM BD10 RETm + eiM BAAMTSYm + f iM PTFSBDm + g iM PTFSFX m + hiM PTFSCOM m + jiM ASIAMRFm (2) + kiM JAPMRFm )where i = 1, …, nfunds, m = 1,…,M, ALPHAim is the abnormal return of fund i for month m, rimis fund return in excess of the riskfree rate, and the other variables are as defined in Section 2. Then, we estimate the following pooled OLS regressions: S −1 Y −1 ALPHAim = a + bPRESENCE i + ∑ s =1 c s STYLEDUM is + ∑d y =1 y YRDUM m + ε im , and y (3) ALPHAim = a + bPRESENCEi + cPERFFEEi + dMGTFEEi + eREDEMP + fMININVi i S −1 Y −1 (4) + gFUNDSIZEi + hFAMSIZEi + ∑ s =1 j s STYLEDUM is + ∑ k YRDUM y =1 y y m + ε imwhere PRESENCE is investment region presence, PERFFEE is fund performance fee, MGTFEEis fund management fee, REDEMP is fund redemption period, MININV is minimum investmentamount, FUNDSIZE is fund size category, FAMSIZE is fund family size category, STYLEDUMis fund investment style dummy, and YRDUM is year dummy. Fund fees and minimuminvestments may proxy for managerial ability since anecdotal evidence suggests that skilledmanagers often demand high fees and minimum investments17 so as to extract rents frominvestors. Fund and family size may proxy for the level of resources available to managers.However, the decreasing returns to scale argument advanced in Goetzmann, Ingersoll, and Ross(2003), Berk and Green (2004), and Fung, Hsieh, Naik, and Ramadorai (2006) may apply17 For instance, James Simons’ extremely successful Renaissance Technologies Medallion fund charges amanagement fee of 5 percent and a performance fee of 44 percent (“Really Big Bucks” Alpha Magazine, May2006). 11
  13. 13. instead. Finally, as in Aragon (2006) funds with longer redemption periods may take on greaterliquidity risk and achieve higher expected returns.18 To facilitate the estimation of fund alpha, weonly include results for funds with at least 24 months of return data.19 [Please insert Table 5 here] The results presented in columns one and two of Panel A, Table 5 are striking. Thecoefficient estimate on the investment region presence variable in the univariate regression(leftmost column of Panel A, Table 5) suggests that funds with a physical or research presence intheir investment region outperform other funds by 44 basis points per month or 5.28 percent peryear. Even after adjusting for the other hedge fund characteristics, funds with investment regionpresence still outperform funds without investment region presence by 4.68 percent per year.Both these effects are statistically significant at the 1 percent level (t-statistics = 4.44 and 3.59,respectively). In contrast, the other fund characteristics, save the size category variables, do notseem to explain fund alpha. There are concerns that the results may be due to differences in the way backfill andincubation bias affects funds with and without investment region presence. If funds withinvestment region presence backfill or incubate their returns more often than funds withoutinvestment region presence, it may explain the overperformance of the former group of funds.Also, there are concerns that funds with investment region presence may trade more illiquidsecurities. As a result, for these funds, reported returns tend to be smoother than true economicreturns, which understates volatility and overstates the statistical significance of risk-adjustedmeasures like alpha. To cater for such concerns, we re-estimate the regressions with backfill andincubation bias adjusted alpha and with alpha derived from unsmoothed returns using theGetmansky, Lo, and Makarov (2004) correction. As in the previous section, to adjust for backfilland incubation bias, we simply remove the first 12 months of return data from each fund andredo the alpha estimation for funds with at least 24 months of remaining return data. Lastly, toensure that the overperformance of funds with investment region presence is not simply due tolower fees, we also re-do the analysis on pre-fee20 alpha. The results from this robustnessanalysis are presented in columns three to eight of Panel A, Table 5 and suggest that the18 Nonetheless, this may not translate to higher alphas since the factor model takes into account liquidity risk as well.19 Similar results obtain for funds with at least 36 months of return data. Results are available upon request.20 Pre-fee alphas are computed from pre-fee returns. We derive pre-fee returns by taking the high watermark andhurdle rate as the T-bill, and assuming that the returns accrue to a first-year investor in the fund. 12
  14. 14. overperformance of hedge funds with investment region presence is not simply an artifact ofbackfill bias, incubation bias, illiquidity, or lower fund fees. The coefficient estimates on theinvestment region presence variable are statistically and economically significant (greater than4.30 percent per year) for all specifications and for both the univariate and multivariateregressions.21 Yet another concern is that the pooled OLS regressions do not take into account cross-correlation in residuals. To alleviate this problem, we also report as robustness checksregressions run using the Fama and MacBeth (1973) method. Specifically, we first run cross-sectional regressions for each month. Then, we report the time series averages of the coefficientestimates, and use the time-series standard errors of the average slopes to draw inferences. TheFama-MacBeth methodology is a convenient and conservative way of accounting for potentialcross-correlation in residuals. According to Fama and French (2002), Fama-MacBeth standarderrors are often two to five times the OLS standard errors from pooled panel regressions thatignore cross-correlation. The coefficient estimates and t-statistics from the Fama and MacBeth(1973) regressions reported in Panel B of Table 5 strongly corroborate our findings with thepooled OLS regressions.3.2. Sorts on hedge fund geography To further gauge the economic relevance of the apparent local informational advantage,we construct portfolios of funds based on fund investment region presence and compare theirrisk-adjusted performance. Specifically, we form an equal-weighted portfolio of funds withinvestment region presence (portfolio A) and an equal-weighted portfolio of funds withoutinvestment region presence (portfolio B). Then, we measure performance of the spread betweenportfolio A and B relative to the augmented Fung and Hsieh (2004) model. The alpha of thespread portfolio represents the value added to hedge fund investors (including Fund of Funds) of21 While most of the other fund characteristics do not explain the cross-sectional variation in fund alpha for allregression specifications, there seems to be a positive relationship between fund size and alpha. However, wecaution against making any inferences from this relationship as the fund size variables are taken at the end of thesample period and may simply reflect the fact that successful funds garner more inflows (Agarwal, Daniel, andNaik, 2006). 13
  15. 15. investing in funds with investment region presence and avoiding funds without investmentregion presence. The results from this exercise are displayed in Panel A of Table 6 and show that thestrategy of buying funds with investment region presence and avoiding funds without investmentregion presence yields a risk-adjusted return of 4.54 percent per year (t-statistic = 3.09). Thissuggests that astute hedge fund investors can benefit from the apparent local informationaladvantage of funds with investment region presence. Consistent with the cross-sectionalregression results, our findings are robust to adjustments for backfill and incubation bias, serialcorrelation in returns, and fund fees (see Panels B, C, and D of Table 6). All the spread alphas inPanels B, C, and D are comfortably above 4 percent per year and statistically significant at the 5percent level. It is reassuring to note that the portfolios returns (portfolio A and B) are wellexplained by the augmented Fung and Hsieh (2004) factor model. All but one of the adjusted R-squares for portfolios A and B are greater than or equal to 50 percent. In addition, the loadingson the Asia ex Japan equity market factor are economically and statistically significant (at the 5percent level)22 for all A and B portfolios attesting to the explanatory power of the Asia ex Japanequity market factor. [Please insert Table 6 here] Figure 1 complements the results from Table 6. It illustrates the monthly cumulativeaverage residuals (henceforth CARs) from the portfolio of funds with investment region presence(portfolio A) and the portfolio of funds without investment region presence (portfolio B). CAR isthe cumulative difference between a portfolios excess return and its factor loadings (estimatedover the entire sample period) multiplied by the augmented Fung and Hsieh (2004) risk factors.The CARs in Figure 1 indicate that portfolio A consistently outperforms portfolio B over theentire sample period and suggest that the effects of investment region presence are not peculiarto a particular year. [Please insert Figure 1 here] One concern is that the option-based factors of Fung and Hsieh (2004) may not capturethe return payoffs of funds engaged in option-based strategies on Asian equity market indices.For instance, Agarwal and Naik (2004) show that the payoffs of many equity hedge fundsresemble that from writing naked out-of-the-money (henceforth OTM) put options on the equity22 The t-statistics on the Asian equity factor loadings are omitted for brevity and are available upon request. 14
  16. 16. market. Yet another concern is that the SCMLC factor may not adequately capture the riskpremium of small versus large stocks in Asia. If, in addition, nearby funds load more on smallstocks than do distant funds, then this may explain the apparent overperformance of the former. In response to these concerns, we replicate the analysis in Table 5 after supplementingthe augmented model with two option-based factors on the Japan Nikkei 225 index and an Asiansize factor. Following Agarwal and Naik (2004), we use OTM European call and put options onthe Nikkei 225 traded on the Singapore Stock Exchange. The OTM call option strategy is asfollows. On the first day of January, buy an OTM call option on the Nikkei 225 that expires onFebruary. On the first day of February, sell the option bought a month ago (i.e., in January) andbuy another OTM call option on the Nikkei 225 that expires in March. Repeat this process everymonth and record the returns from this strategy. The payoffs for the OTM put option-basedstrategy are derived using an analogous procedure. We select the OTM call (put) option to be theone with the next higher (lower) strike price relative to the at-the-money call (put) option whosepresent value of strike price is closest to the current index value. For the Asian size factor, wetake the return difference between the Dow Jones Asia Small Cap Index and the Dow Jones AsiaLarge Cap Index. The results from this analysis are virtually identical to those from Table 6 andindicate that our inferences are robust to controlling for return covariation with the Nikkei 225index option-based factors and the Asian size factor.23 Another issue with our results is that they may be driven by pseudo cross-region effects.Suppose funds investing in region A consistently outperform, on a risk-adjusted basis, fundsinvesting in region B. In addition, funds investing in region A all have offices in region A whilefunds investing in region B do not set up local offices. Then, this could generate the results inTables 5 and 6 even though no local informational advantage exists. Related to this, Table 1indicates that all funds investing in the Global and Australia/New Zealand regions also have apresence in those markets. Instead of a local informational advantage, our results may well becapturing the overperformance of funds investing in the Global and Australia/New Zealandregions, and the underperformance of funds investing elsewhere.23 We also replicate the cross-sectional regression analysis in Table 5 after supplementing the augmented factormodel with the two option-based factors (derived from OTM options on the Nikkei 225) and the Asian size factor.To conserve the degrees of freedom (since some funds only have 24 months of data) we adopt the stepwiseregression approach of Agarwal and Naik (2004). We find that the results are virtually identical with this alternativespecification. 15
  17. 17. Moreover, if the overperformance of funds with investment region presence stems from alocal informational advantage, then it must be that the overperformance is greater for regionswhere firms disclose less public information and where the local informational advantage islarge. Extant research has shown that the local/foreign informational asymmetry is particularlysevere in Emerging Markets [see Dovrak (2005), Choe, Kho, and Stulz (2005), and Bae, Stulz,and Tan (2006)]. Hence, we expect the overperformance of funds with investment regionpresence to be greater for Emerging Markets than for other regions. To address these issues, we break down the analysis in Table 6 by geographical regionfor regions (excluding the Global and Australia/New Zealand regions) where we have at least 20funds. The results in Table 7 clearly indicate that the overperformance of funds with investmentregion presence is not driven by pseudo cross-region effects. For each major geographicalregion, funds with investment region presence outperform funds without investment presence.The alpha for the spread portfolio is consistently above 4 percent per year for all four investmentregions. Naturally with the reduced sample size, some of the within region spread alphas are nolonger statistically significant. Nonetheless, the spread alpha for the Asia including Japan regionand for Emerging Markets are statistically significant at the 1 percent level. Indeed, the spreadalpha is highest and most reliably different from zero for Emerging Market funds (t-statistic =2.77). This dovetails with our a priori intuition that the local informational advantage should bestrongest in Emerging Markets. [Please insert Table 7 here] As a final robustness check, we also break down the analysis in Table 6 by investmentstyle for styles with at least 20 funds. The results displayed in Table 8 complement those ofTable 7. They demonstrate that the overperformance of funds with investment region presenceholds within each of the three major investment styles. The alpha for the within style spread isabove 5 percent per year for all three styles. It is comforting to note that the alpha spread isstatistically significant at the 5 percent level (t-statistic = 2.47) for the most popular style: EquityLong/Short. Out of the three styles, the spread alpha is largest (8.19 percent per year) and moststatistically significant (t-statistic = 3.32) for Event Driven funds. This is not surprising sinceEvent Driven funds employ strategies involving investments in opportunities created bysignificant transactional events such as spinoffs, mergers and acquisitions, bankruptcyreorganizations, recapitalizations, and share buybacks. The informational demands of such an 16
  18. 18. investing strategy are likely to be onerous. Hence, relative to other funds, Event Driven fundshave much to gain from a local presence. [Please insert Table 8 here]4. Conclusion In summary, the results in this paper tell a consistent story. Hedge funds with a physicalpresence in their investment region outperform funds without a physical presence on a risk-adjusted basis. The difference in performance manifests in the cross-section of fund alpha and infund portfolio sorts. The superior performance of nearby funds is not simply a by-product offund fees, backfill bias, incubation bias, or illiquidity. This overperformance is also not due tocross-region effects. Within each major investment region and investment style, funds with alocal presence outperform other funds. Moreover, the overperformance is exceptionally strongfor investment regions (e.g., Emerging Markets) where the local/foreign information asymmetryis likely to be acute and for investment styles (e.g., Event Driven) where an informationaladvantage is likely to matter most. Collectively, these results suggest that nearby hedge fundsenjoy a local informational advantage. These results also contribute to our understanding ofhedge fund alpha, and are particularly relevant to fund managers and to fund investors like Fundof Funds.ReferencesAudretsch, D.B., Feldman, M.P., 1996. R & D spillovers and the geography of innovation and production. American Economic Review 86, 630-640.Agarwal, V., Daniel, N., Naik, N.Y., 2006. Flows, performance, and managerial incentives in the hedge fund industry. Unpublished working paper. Georgia State University.Agarwal, V., Naik, N.Y., 2000. Multi-period performance persistence analysis of hedge funds. Journal of Financial and Quantitative Analysis 35, 327-342.Agarwal, V., Naik, N.Y., 2004. Risk and portfolio decisions involving hedge funds. Review of Financial Studies 17, 63-98. 17
  19. 19. Aragon, G., 2006. Share restrictions and asset pricing: evidence from the hedge fund industry. Journal of Financial Economics, forthcoming.Bae, K.H., Stulz, R., Tan, H., 2006. Do local analysts know more? A cross-country study of the performance of local analysts and foreign analysts. Unpublished working paper. Ohio State University.Becker, Bo., 2006. Geographical segmentation of U.S. capital markets. Journal of Financial Economics, forthcoming.Berk, J., Green, R., 2004. Mutual fund flows and performance in rational markets. Journal of Political Economy 112, 1269 -1295.Brown, S., Goetzmann, W., Ibbotson, R., 1999. Offshore hedge funds: survival and performance, 1989-95. Journal of Business 72, 91-117.Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance 52, 57-82.Choe, H., Kho, B.C., Stulz, R., 2005. Do domestic investors have an edge? The trading experience of foreign investors in Korea. Review of Financial Studies 18, 795-829.Cohen, L., Frazzini, A., 2006. Economic links and predictable returns. Unpublished working paper. University of Chicago.Cooper, I., Kaplanis, E., 1994. Home bias in equity portfolios, inflation hedging, and international capital market equilibrium. Review of Financial Studies 7, 45-60.Coval, J., Moskowitz, T., 2001. The geography of investment: informed trading and asset prices. Journal of Political Economy 4, 811-841.Dvorak, T., 2005. Do domestic investors have an information advantage? Evidence from Indonesia. Journal of Finance 60, 817-839.Fama, E., French, K., 2002. Testing trade-off and pecking predictions about dividends and debt. Review of Financial Studies 15, 1-33.Fama, E., MacBeth, J.D., 1973. Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607-636.French, K., Poterba, J., 1991. Investor diversification and international equity markets. American Economic Review 81, 222-226.Fung, W., Hsieh, D., 1997. Empirical characteristics of dynamic trading strategies: the case of hedge funds. Review of Financial Studies 10, 275-302.Fung, W., Hsieh, D., 1999. A primer on hedge funds. Journal of Empirical Finance 6, 309-331. 18
  20. 20. Fung, W., Hsieh, D., 2000. Performance characteristics of hedge funds and CTA funds: natural versus spurious biases. Journal of Financial and Quantitative Analysis 35, 291-307.Fung, W., Hsieh, D., 2001. The risk in hedge fund strategies: theory and evidence from trend followers. Review of Financial Studies 14, 313-341.Fung, W., Hsieh, D., 2004. Hedge fund benchmarks: a risk based approach. Financial Analyst Journal 60, 65-80.Fung, W., Hsieh, D., Naik, N., Ramadorai, T., 2006. Hedge funds: performance, risk, and capital formation. Unpublished working paper. London Business School.Getmansky, M., Lo, A., Makarov, I., 2004. An econometric model of serial correlation and illiquidity of hedge fund returns. Journal of Financial Economics 74, 529-610.Goetzmann, W., Ingersoll, J., Ross, S., 2003. High water marks and hedge fund management contracts. Journal of Finance 58, 1685-1717.Grinblatt, M., Keloharju, M., 2000. The investment behaviour and performance of various investor types: a study of Finland’s unique data set. Journal of Financial Economics 55, 43- 47.Hau, H., 2001. Location matters: an examination of trading profits. Journal of Finance 56, 1959- 1983.Ivković, Z., Weisbenner, S., 2005. Local does as local is: Informational content of the geography of individual investors’ common stock investments. Journal of Finance 60, 267-306.Kosowski, R., Timmermann, A., Wermers, R., White, H., 2006. Can mutual fund “stars” really pick stocks? New evidence from a bootstrap analysis. Journal of Finance, forthcoming.Kosowski, R., Naik, N., Teo, M., 2006. Do hedge funds deliver alpha? A Bayesian and bootstrap analysis. Journal of Financial Economics, forthcoming.Krugman, P., 1991. Increasing returns and economic geography. Journal of Political Economy 99, 483-499.Liang, B., 2000. Hedge funds: the living and the dead. Journal of Financial and Quantitative Analysis 35, 309-327.Loughran, T., Schultz, P., 2005. Liquidity: urban versus rural firms. Journal of Financial Economics 78, 341-374.Lucas, R.E. Jr., 1993. Making a miracle. Econometrica 61, 251-272. 19
  21. 21. Malloy, C., 2005. The geography of equity analysis. Journal of Finance 60, 719-755.Mitchell, M., Pulvino, T., 2001. Characteristics of risk in risk arbitrage. Journal of Finance 56, 2135-2175.Ragaas De Ramos, R., 2006. Hong Kong outpaces Singapore in snaring hedge fund assets. The Wall Street Journal, May 15 2006.Seasholes, M., 2000. Smart foreign traders in emerging markets. Unpublished working paper. Harvard University.Tesar, L., Werner, I., 1995. Home bias and high turnover. Journal of International Money and Finance 14, 467-492.Wighton, D., 2006. Runners and raiders. Financial Times. June 10 2006.Appendix A: Fund size category definitionsFund size category Fund size (in millions US$)1 0-252 26-1003 101-2504 251-5005 501-7506 751-1,0007 1,001-2,5008 2,501-5,0009 5,001-10,00010 10,001+Appendix B: Fund investment style mappingAsian investment styles Getmansky, Lo, and Makarov (2004) categoriesRelative value Non-directional/Relative valueManaged futures Pure managed futuresDirectional Pure emerging marketEvent driven Event drivenFixed income Fixed income directionalEquity long/short Asian equity hedgeMacro Global macroUnknown Not categorized 20
  22. 22. 0.5 portfolioA (with presence in investment region) 0.45 portfolio B (without presence in investment region) 0.4 0.35 cumulative average residuals 0.3 0.25 0.2 0.15 0.1 0.05 0 2000 2001 2002 2003 yearFigure 1: Cumulative average residuals of hedge funds with presence in investment region versus hedge funds without presence in investment region. Equal-weighted portfolios of hedge funds are constructed by sorting funds on whether they have a physical / research presence in the geographical region they invest in.Cumulative average residual (CAR) is the difference between a portfolios excess return and its factor loadings multiplied by the augmented Fung and Hsieh (2004)risk factors. Factor loadings are estimated over the entire sample period. The sample period is from January 2000 to December 2003.
  23. 23. Table 1 Summary StatisticsThe sample period is from January 2000 to December 2003. Funds are grouped according to their primary investmentstrategy (Panel A) or according to their primary investment geographical region (Panel B). Funds without investmentstrategy information are classfied as "unknown." Funds with presence are funds with a physical presence (head office orresearch office) in their investment geographical region. The total number of funds is 325. The total number of deadfunds is 23. And the total number of fund months with return information is 9722. Panel A: By investment strategyInvestment strategy Total funds Dead funds With presence Without presence Return monthsEquity Long/Short 214 16 108 106 6264Relative Value 26 5 12 13 820Event Driven 38 0 29 9 1158Macro 16 1 16 0 464Directional 1 0 0 1 39Fixed Income 12 0 6 6 407Managed Futures 10 1 10 0 311Unknown 8 0 3 5 259 Panel B: By investment regionInvestment region Total funds Dead funds With presence Without presence Return monthsAsia excluding Japan 38 0 27 11 1183Asia including Japan 63 8 38 25 1991Australia/New Zealand 23 0 23 0 622Emerging Markets 33 0 8 25 1121Global 52 7 52 0 1479Japan only 108 8 30 77 3046Korea 2 0 1 1 96Greater China 2 0 2 0 42Taiwan 2 0 1 1 88India 2 0 2 0 54
  24. 24. Table 2 Explaining hedge fund returns: A principal components analysisR-squares from regressions of hedge fund style/region returns on their principal components. For each style and region intersection with at least 5 funds, the equal-weighted portfolio of the style/region returns is constructed. Principal components analysis is used to break the returns of the style/region portfolios intoorthogonal principal components. The returns of each style/region portfolio are then regressed on each principal component, and the r-squares from theregressions recorded. PC1 denotes the top principal component, PC2 denotes the second principal component, etc. For brevity, only the r-squares with the top tenprincipal components are presented. The sample period is from January 2000 to December 2003. Number Top ten principal componentsInvestment style Investment region of funds PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10Equity Long/Short Asia excl Japan 25 0.84 0.04 0.02 0.01 0.02 0.01 0.01 0.02 0.01 0.01Equity Long/Short Asia incl Japan 44 0.78 0.02 0.01 0.01 0.10 0.01 0.02 0.00 0.02 0.00Equity Long/Short Australia/NZ 15 0.58 0.17 0.04 0.11 0.01 0.06 0.00 0.00 0.00 0.01Equity Long/Short Emerging Markets 16 0.86 0.01 0.01 0.06 0.00 0.00 0.00 0.03 0.01 0.00Equity Long/Short Global 19 0.44 0.14 0.01 0.03 0.23 0.13 0.01 0.01 0.00 0.00Equity Long/Short Japan only 92 0.22 0.05 0.06 0.00 0.14 0.00 0.33 0.00 0.00 0.01Relative Value Asia incl Japan 6 0.01 0.05 0.02 0.05 0.00 0.07 0.04 0.01 0.00 0.20Relative Value Japan only 11 0.01 0.00 0.00 0.01 0.00 0.06 0.48 0.03 0.06 0.24Event Driven Asia excl Japan 8 0.45 0.07 0.07 0.13 0.00 0.08 0.00 0.00 0.09 0.03Event Driven Asia incl Japan 8 0.05 0.04 0.00 0.00 0.00 0.17 0.19 0.34 0.00 0.08Event Driven Australia/NZ 6 0.30 0.02 0.07 0.05 0.08 0.08 0.00 0.00 0.23 0.00Event Driven Global 6 0.17 0.01 0.01 0.34 0.09 0.00 0.01 0.00 0.03 0.03Managed Futures Global 9 0.01 0.21 0.63 0.09 0.02 0.01 0.01 0.00 0.01 0.00Fixed Income Emerging Markets 7 0.42 0.04 0.05 0.08 0.15 0.12 0.00 0.09 0.02 0.01Macro Global 13 0.18 0.71 0.06 0.03 0.00 0.00 0.00 0.00 0.01 0.00Percentage of total variance explained 50.54 16.40 7.77 5.74 4.49 3.53 2.58 2.22 1.87 1.34
  25. 25. Table 3 Explaining hedge fund returns: An augmented Fung and Hsieh (2004) factor modelHedge fund portfolio performance is estimated relative to the augmented Fung and Hsieh (2004) factors. The augmentedFung and and Hsieh (2004) factors are S&P 500 return minus risk free rate (SNPMRF), Wilshire small cap minus largecap return (SCMLC), change in the constant maturity yield of the 10 year treasury (BD10RET), change in the spread ofMoodys Baa - 10 year treasury (BAAMTSY), bond PTFS (PTFSBD), currency PTFS (PTFSFX), commodities PTFS(PTFSCOM), Asia Ex Japan index return minus risk free rate (ASIAMRF), and Japan index return minus risk free rate(JAPMRF), where PTFS is primitive trend following strategy. The sample period is from January 2000 - December 2003. BAAMTSY PTFSCOM Alpha (pct/ BD10RET ASIAMRF SNPMRF PTFSBD PTFSFX JAPMRF SCMLC year)Portfolio Adj. R2 Panel A: All fundsAll funds 8.96 0.21 0.12 0.08 0.10 0.00 0.00 0.05 0.52 (3.96) (5.40) (2.72) (0.96) (0.77) (0.20) (-0.05) (2.88)All funds 9.78 0.10 0.09 0.05 -0.05 0.00 0.00 0.03 0.13 0.04 0.63 (4.81) (2.01) (2.11) (0.74) (-0.36) (-0.06) (-0.36) (1.90) (2.88) (1.23) Panel B: By investment styleEquity Long/Short 9.50 0.09 0.06 0.02 -0.03 0.00 -0.01 0.04 0.15 0.04 0.62 (4.03) (1.65) (1.34) (0.29) (-0.20) (-0.09) (-1.14) (2.09) (2.99) (1.23)Relative Value 7.62 0.10 0.05 -0.08 -0.22 0.00 0.03 -0.03 -0.05 0.06 0.00 (2.49) (1.36) (0.86) (-0.76) (-1.18) (-0.07) (1.99) (-1.00) (-0.77) (1.36)Event Driven 11.88 0.03 0.05 -0.01 0.06 -0.01 0.00 0.04 0.12 0.01 0.38 (4.9) (0.55) (1.07) (-0.15) (0.39) (-0.61) (0.09) (1.68) (2.25) (0.35) Panel C: By investment regionAsia excluding Japan 13.27 0.03 -0.02 -0.06 0.17 0.00 0.02 0.02 0.29 0.03 0.47 (3.27) (0.32) (-0.30) (-0.43) (0.70) (0.23) (1.03) (0.48) (3.23) (0.54)Asia including Japan 8.72 0.02 0.09 0.09 -0.05 0.00 -0.01 0.04 0.24 0.04 0.51 (2.72) (0.22) (1.36) (0.81) (-0.27) (-0.31) (-0.82) (1.41) (3.40) (0.99)Australia/New Zealand 11.07 0.17 0.05 0.05 0.08 0.02 -0.02 -0.02 0.11 0.01 0.53 (3.76) (2.40) (0.77) (0.46) (0.46) (1.26) (-1.45) (-0.78) (1.73) (0.14)Emerging Markets 15.03 0.27 0.13 0.18 0.02 0.00 0.00 0.05 0.18 -0.01 0.54 (3.76) (2.86) (1.60) (1.28) (0.07) (-0.21) (-0.12) (1.55) (2.07) (-0.12)Global 9.43 0.18 0.09 0.30 0.15 0.00 0.01 0.04 0.10 -0.04 0.49 (3.45) (2.84) (1.71) (3.05) (0.89) (-0.16) (0.73) (1.54) (1.63) (-0.92)Japan only 8.13 0.01 0.11 -0.13 -0.21 0.00 -0.01 0.03 -0.02 0.10 0.31 (3.59) (0.27) (2.33) (-1.62) (-1.52) (-0.01) (-0.74) (1.52) (-0.33) (3.04)
  26. 26. Table 4 Statistical significance of the best and worst hedge funds performancePanel A reports the statistical significance of performance measures for all funds. Panel B reports the statistical significance of performance measures after correcting forincubation and backfill bias by removing the first 12 months of data for each fund. Panel C reports analogous estimates after correcting for serial correlation using theGetmansky, Lo and Makarov (2004) correction. In each panel, the first and second rows reports the ranked OLS estimate of alpha in percent per month and thebootstrapped p -value of alpha. Alpha is estimated relative to the augmented Fung and Hsieh (2004) factors. The third and fourth rows report the ranked t -statistic ofalpha based on heteroskedasticity and autocorrelation consistent standard errors as well as the bootstrapped p -value of the t -statistic. The first column on the left (right)reports funds with the lowest (highest) alpha and t -statistic followed by results for the funds with the second lowest (highest) alpha and t -statistic and marginal funds atdifferent percentiles in the left (right) tail of the distribution. The p -value is based on the distribution of the best (worst) funds in 1000 bootstrap resamples. The augment-ed Fung and Hsieh (2004) factors are S&P 500 return minus risk free rate (SNPMRF), Wilshire small cap minus large cap return (SCMLC), change in the constantmaturity yield of the 10 year treasury (BD10RET), change in the spread of Moodys Baa - 10 year treasury (BAAMTSY), bond PTFS (PTFSBD), currency PTFS(PTFSFX), and commodities PTFS (PTFSCOM), Asia Ex Japan index return minus risk free rate (ASIAMRF), and Japan index return minus risk free rate (JAPMRF),where PTFS is primitive trend following strategy. The sample period is from January 2000 to December 2003. Bottom 2. 3. 4. 5. 1% 3% 5% 10% 10% 5% 3% 1% 5. 4. 3. 2. Top Panel A: Full sample of fundsmonthly alpha (%) -1.64 -1.48 -1.34 -1.17 -1.17 -1.48 -0.97 -0.79 -0.47 1.71 2.67 2.99 4.06 3.52 3.71 4.06 4.39 4.59p -value (bootstrapped) 0.95 0.90 0.90 0.94 0.86 0.90 0.97 0.96 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02t -alpha -3.72 -3.25 -2.93 -2.75 -2.35 -3.25 -2.29 -1.74 -1.04 3.89 4.76 5.68 8.19 6.96 7.58 8.19 8.63 9.69p -value (bootstrapped) 0.77 0.77 0.80 0.79 0.95 0.77 0.93 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Panel B: Adjusting for incubation and backfill biasmonthly alpha (%) -2.00 -1.97 -1.21 -1.09 -1.08 -2.00 -1.09 -0.79 -0.39 1.67 2.38 3.35 4.66 3.35 3.49 4.46 4.66 4.92p -value (bootstrapped) 0.65 0.27 0.87 0.85 0.71 0.65 0.85 0.94 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00t -alpha -2.84 -2.47 -2.40 -2.36 -2.36 -2.84 -2.36 -1.59 -0.95 4.06 4.90 5.23 7.08 5.23 5.31 6.91 7.08 8.25p -value (bootstrapped) 0.99 0.99 0.97 0.95 0.89 0.99 0.95 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 Panel C: With unsmoothed returns using the Getmansky, Lo and Makarov (2004) correctionmonthly alpha (%) -1.72 -1.53 -1.36 -1.31 -1.14 -1.53 -1.08 -0.85 -0.50 1.74 2.54 2.95 4.13 3.60 3.75 4.13 4.15 4.72p -value (bootstrapped) 0.96 0.93 0.91 0.88 0.93 0.93 0.92 0.96 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02t -alpha -3.77 -3.05 -2.86 -2.76 -2.43 -3.05 -2.21 -1.62 -0.97 3.38 4.15 4.72 7.04 6.03 6.14 7.04 7.71 8.86p -value (bootstrapped) 0.73 0.87 0.83 0.76 0.90 0.87 0.95 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
  27. 27. Table 5 Cross-sectional regressions on hedge fund risk-adjusted performancePooled OLS regressions and Fama MacBeth (1973) regressions on the cross-section of hedge fund alphas. The dependentvariable is hedge fund monthly alpha estimated relative to the augmented Fung and Hsieh (2004) factors. The augmentedFung and and Hsieh (2004) factors are S&P 500 return minus risk free rate (SNPMRF), Wilshire small cap minus large capreturn (SCMLC), change in the constant maturity yield of the 10 year treasury (BD10RET), change in the spread ofMoodys Baa - 10 year treasury (BAAMTSY), bond PTFS (PTFSBD), currency PTFS (PTFSFX), and commodities PTFS(PTFSCOM), Asia Ex Japan index return minus risk free rate (ASIAMRF), and Japan index return minus risk free rate(JAPMRF), where PTFS is primitive trend following strategy. The independent variables are hedge fund characteristicssuch as management fee, performance fee, redemption notification period, minimum investment, fund size category, familysize category, investment region presence, and style dummies. Investment region presence is an indicator variable thatequals 1 when the fund has a physical or research presence in the geographical region it invests in, and equals 0 otherwise.The t -statistics derived from White (1980) standard errors, are in parentheses. The OLS regressions (Panel A) also includeyear dummies. The coefficient estimates on the year and style dummies are omitted for brevity. The sample period is fromJanuary 2000 to December 2003. * Significant at the 5% level; ** Significant at the 1% level. Dependent variableIndependent variables monthly alpha backfill and incubation alpha from pre-fee alpha bias-adjusted alpha unsmoothed returns Panel A: Pooled OLS regressionsinvestment region presence 0.44** 0.39** 0.43** 0.36** 0.43** 0.38** 0.54** 0.49** (4.44) (3.59) (3.98) (3.07) (3.99) (3.22) (5.23) (4.40)management fee (%) 0.07 -0.28* 0.09 0.16 (0.64) (-2.50) (0.81) (1.49)performance fee (%) 0.00 -0.03 0.01 0.02 (0.31) (-1.39) (0.43) (1.51)redemption period (days) 0.00 0.01 0.00 0.00 (0.44) (1.72) (0.23) (0.69)minimum investment (US$m) -0.06 -0.05 -0.07 -0.07 (-1.67) (-1.23) (-1.91) (-2.00)fund size category 0.13* 0.23** 0.12* 0.13* (2.26) (3.60) (2.13) (2.32)family size category -0.05* -0.06 -0.05* -0.07** (-2.12) (-1.91) (-2.03) (-2.64) Panel B: Fama-MacBeth (1973) cross-sectional regressionsinvestment region presence 0.47** 0.39** 0.45** 0.42** 0.46** 0.38** 0.57** 0.50** (3.83) (3.65) (3.94) (3.88) (3.41) (3.30) (4.60) (4.55)management fee (%) 0.09 -0.30* 0.11 0.18 (0.64) (-2.24) (0.76) (1.31)performance fee (%) 0.01 -0.03 0.01 0.03 (0.58) (-1.60) (0.68) (1.49)redemption period (days) 0.00 0.01* 0.00 0.00 (1.00) (2.01) (0.76) (1.30)minimum investment (US$m) -0.07* -0.04 -0.09* -0.09* (-2.09) (-1.16) (-2.46) (-2.58)fund size category 0.13* 0.22** 0.13* 0.13* (2.34) (3.09) (2.24) (2.48)family size category -0.06* -0.04 -0.06* -0.07* (-2.08) (-1.27) (-2.00) (-2.55)
  28. 28. Table 6 Sorts on hedge fund investment region presenceHedge funds are sorted based on whether they have a presence in the geographical region they invest in. Portfolio A is the equal-weighted portfolio of funds withinvestment region presence. Portfolio B is the equal-weighted portfolio of funds without investment region presence. Alpha is estimated relative to the augmented Fungand Hsieh (2004) factors. The augmented Fung and and Hsieh (2004) factors are S&P 500 return minus risk free rate (SNPMRF), Wilshire small cap minus large capreturn (SCMLC), change in the constant maturity yield of the 10 year treasury (BD10RET), change in the spread of Moodys Baa - 10 year treasury (BAAMTSY), bondPTFS (PTFSBD), currency PTFS (PTFSFX), and commodities PTFS (PTFSCOM), Asia Ex Japan index return minus risk free rate (ASIAMRF), and Japan index returnminus risk free rate (JAPMRF), where PTFS is primitive trend following strategy. The sample period is from January 2000 - December 2003. BAAMTSY PTFSCOM BD10RET ASIAMRF SNPMRF PTFSBD PTFSFX JAPMRF SCMLC Alpha Mean Ret. (pct/ t -stat ofPortfolio (pct/ year) Std. Dev. year) alpha Adj. R2 Panel A: Raw returnsPortfolio A (with presence) 13.33 6.10 11.88 5.75 0.09 0.09 0.06 0.05 0.00 0.00 0.02 0.15 0.03 0.67Portfolio B (no presence) 7.14 5.58 7.34 3.20 0.10 0.08 0.04 -0.17 0.00 -0.01 0.04 0.10 0.04 0.51Spread (A-B) 6.19 3.01 4.54 3.09 -0.01 0.00 0.02 0.22 0.00 0.01 -0.02 0.05 -0.01 0.27 Panel B: Adjusted for backfill and incubation biasPortfolio A (with presence) 13.38 6.93 11.45 5.09 0.10 0.11 0.07 0.08 0.01 0.00 0.02 0.18 0.03 0.70Portfolio B (no presence) 6.43 6.14 7.25 2.76 0.10 0.07 0.03 -0.16 -0.01 -0.01 0.05 0.12 0.03 0.48Spread (A-B) 6.95 4.29 4.20 1.98 0.00 0.04 0.03 0.24 0.01 0.01 -0.04 0.06 0.00 0.25 Panel C: Adjusted for serial correlationPortfolio A (with presence) 13.36 6.50 11.91 5.41 0.11 0.10 0.08 0.05 0.00 0.00 0.03 0.16 0.03 0.67Portfolio B (no presence) 6.98 6.14 7.27 2.85 0.12 0.09 0.08 -0.17 0.00 -0.01 0.05 0.11 0.03 0.50Spread (A-B) 6.38 3.30 4.64 2.78 -0.02 0.01 -0.01 0.22 0.00 0.01 -0.02 0.04 0.00 0.22 Panel D: Pre-fee returnsPortfolio A (with presence) 17.59 6.30 16.33 7.72 0.09 0.09 0.05 0.07 0.00 0.00 0.03 0.15 0.04 0.68Portfolio B (no presence) 10.08 5.74 10.46 4.49 0.10 0.08 0.04 -0.16 0.00 -0.01 0.05 0.11 0.04 0.53Spread (A-B) 7.51 3.01 5.87 3.98 -0.01 0.00 0.02 0.23 0.00 0.01 -0.02 0.05 -0.01 0.27
  29. 29. Table 7 Sorts on hedge fund investment region presence by investment regionHedge funds are sorted based on whether they have a presence in the geographical region they invest in. Portfolio A is the equal-weighted portfolio of funds withinvestment region presence. Portfolio B is the equal-weighted portfolio of funds without investment region presence. Alpha is estimated relative to the augmented Fungand Hsieh (2004) factors. The augmented Fung and and Hsieh (2004) factors are S&P 500 return minus risk free rate (SNPMRF), Wilshire small cap minus large capreturn (SCMLC), change in the constant maturity yield of the 10 year treasury (BD10RET), change in the spread of Moodys Baa - 10 year treasury (BAAMTSY), bondPTFS (PTFSBD), currency PTFS (PTFSFX), and commodities PTFS (PTFSCOM), Asia Ex Japan index return minus risk free rate (ASIAMRF), and Japan index returnminus risk free rate (JAPMRF), where PTFS is primitive trend following strategy. The sample period is from January 2000 - December 2003. BAAMTSY PTFSCOM BD10RET ASIAMRF SNPMRF PTFSBD PTFSFX JAPMRF SCMLC Alpha Mean Ret. (pct/ t -stat ofPortfolio (pct/ year) Std. Dev. year) alpha Adj. R2 Panel A: Asia excluding JapanPortfolio A (with presence) 15.61 9.62 15.72 3.46 0.05 -0.03 -0.11 0.19 0.01 0.03 0.01 0.21 0.06 0.35Portfolio B (no presence) 7.60 11.92 7.58 1.65 -0.02 -0.02 0.09 0.16 -0.01 -0.01 0.03 0.49 -0.06 0.56Spread (A-B) 8.01 8.87 8.15 1.72 0.07 -0.01 -0.20 0.03 0.02 0.04 -0.02 -0.28 0.12 0.14 Panel B: Asia including JapanPortfolio A (with presence) 11.78 8.14 11.53 3.45 -0.01 0.09 0.09 -0.01 -0.01 -0.02 0.03 0.25 0.07 0.51Portfolio B (no presence) 4.93 7.70 4.81 1.37 0.05 0.09 0.08 -0.12 0.00 -0.01 0.05 0.22 0.00 0.41Spread (A-B) 6.86 4.30 6.72 2.71 -0.06 0.00 0.01 0.11 -0.01 -0.01 -0.02 0.03 0.07 -0.01 Panel C: Japan onlyPortfolio A (with presence) 12.80 6.91 11.38 3.19 -0.01 0.16 -0.21 -0.18 0.01 -0.01 0.00 0.03 0.12 0.19Portfolio B (no presence) 6.37 4.73 6.92 3.09 0.03 0.08 -0.10 -0.23 -0.01 -0.01 0.05 -0.04 0.09 0.32Spread (A-B) 6.43 5.80 4.46 1.42 -0.04 0.08 -0.11 0.05 0.02 0.00 -0.05 0.07 0.03 0.11 Panel D: Emerging MarketsPortfolio A (with presence) 36.53 23.69 42.70 3.70 0.40 0.19 -0.12 0.73 0.04 0.03 0.24 0.14 0.00 0.29Portfolio B (no presence) 11.68 9.92 10.64 2.61 0.28 0.13 0.25 -0.14 -0.01 -0.01 0.03 0.19 -0.02 0.50Spread (A-B) 24.84 20.98 32.06 2.77 0.12 0.06 -0.37 0.87 0.05 0.04 0.21 -0.05 0.02 0.07
  30. 30. Table 8 Sorts on hedge fund investment region presence by investment styleHedge funds are sorted based on whether they have a presence in the geographical region they invest in. Portfolio A is the equal-weighted portfolio of funds withinvestment region presence. Portfolio B is the equal-weighted portfolio of funds without investment region presence. Alpha is estimated relative to the augmented Fungand Hsieh (2004) factors. The augmented Fung and and Hsieh (2004) factors are S&P 500 return minus risk free rate (SNPMRF), Wilshire small cap minus large capreturn (SCMLC), change in the constant maturity yield of the 10 year treasury (BD10RET), change in the spread of Moodys Baa - 10 year treasury (BAAMTSY), bondPTFS (PTFSBD), currency PTFS (PTFSFX), and commodities PTFS (PTFSCOM), Asia Ex Japan index return minus risk free rate (ASIAMRF), and Japan index returnminus risk free rate (JAPMRF), where PTFS is primitive trend following strategy. The sample period is from January 2000 - December 2003. BAAMTSY PTFSCOM BD10RET ASIAMRF SNPMRF PTFSBD PTFSFX JAPMRF SCMLC Alpha Mean Ret. (pct/ t -stat ofPortfolio (pct/ year) Std. Dev. year) alpha Adj. R2 Panel A: Relative ValuePortfolio A (with presence) 12.76 7.19 10.63 2.58 -0.01 0.01 -0.14 -0.07 -0.01 0.05 -0.01 0.07 0.03 0.04Portfolio B (no presence) 6.82 5.60 3.41 1.12 0.17 0.07 -0.02 -0.30 0.01 0.01 -0.04 -0.13 0.07 0.06Spread (A-B) 5.94 8.11 7.22 1.67 -0.17 -0.07 -0.12 0.23 -0.02 0.05 0.03 0.20 -0.05 0.14 Panel B: Event DrivenPortfolio A (with presence) 14.02 5.35 13.82 5.45 0.01 0.04 -0.06 0.05 -0.01 0.00 0.04 0.12 0.02 0.36Portfolio B (no presence) 9.00 6.05 5.63 1.93 0.09 0.09 0.12 0.10 0.00 0.01 0.01 0.11 0.00 0.34Spread (A-B) 5.02 4.44 8.19 3.32 -0.08 -0.05 -0.18 -0.05 -0.01 -0.01 0.04 0.01 0.01 0.06 Panel C: Equity Long/ShortPortfolio A (with presence) 12.52 7.42 12.45 4.81 0.14 0.05 0.01 0.09 0.00 -0.01 0.03 0.14 0.06 0.64Portfolio B (no presence) 5.58 6.38 6.47 2.39 0.04 0.07 0.04 -0.14 -0.01 -0.02 0.06 0.17 0.02 0.48Spread (A-B) 6.94 4.91 5.98 2.47 0.10 -0.02 -0.03 0.23 0.01 0.00 -0.04 -0.03 0.03 0.26

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