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The geography of hedge funds

Melvyn Teo

Lee Kong Chian School of Business, Singapore Management University

Q2 2007


This article is the winner of the 2006 AIMA Singapore/INSEAD Research Award, which was sponsored by the Chartered Alternative Investment Analyst (CAIA) Association

Does distance affect hedge fund performance? Anecdotal evidence suggests that proximity to investments may be helpful for hedge funds. Being nearby allows hedge funds to maintain close contact with senior management. Fund managers can learn valuable information from other local firms along the supply chain by being on the ground. Nearby funds, who are also substantial stakeholders, can more easily engage in constructive shareholder activism.

In this paper, we investigate the link between funds’ proximity to investment markets and risk-adjusted returns. We show that nearby funds outperform distant funds on a risk-adjusted basis by on average 5.28% per year (t-statistic = 4.44). The hypothetical strategy of buying nearby funds and shorting distant funds yields risk-adjusted returns of 4.54% per year (t-statistic = 3.09). This spread is robust across all major investment regions and is substantially higher for the emerging market where public informational disclosure is weaker. These results suggest that nearby hedge funds enjoy an informational advantage.

Our study focuses on hedge funds that invest primarily in Asian financial markets. To determine distance, we use fund investment region and location information. For example, we classify funds investing in Asia ex Japan but located in the United States or the United Kingdom as distant funds. Conversely, we classify funds investing in Asia ex Japan and located in Hong Kong or Singapore as nearby funds.

1. Data

We collect data from Eurekahedge and AsiaHedge. The sample consists of monthly fund return data from January 1999 to December 2003. Since Eurekahedge only started collecting fund return data from 2000 (but includes fund returns since inception), to ameliorate survivorship bias, we focus on the period from 2000 onwards.

Our sample also includes data on management fee, performance fee, redemption period, investment style, investment region, fund location, fund size, family size, and minimum investment. We take these characteristics, which are recorded in year 2003, as constant over the sample period. AsiaHedge records fund and family size in distinct ranges. Hence, we convert size values to ten size categories (see the Appendix). Naturally, the sample includes dead funds. Of the 325 funds with at least one month of return data, 23 are dead funds.


Figure 1 - Summary Statistics

Geography of Hedge Funds - Figure 1


As mentioned, the data contains information on fund investment region and fund location and for some funds, research office location. This allows us to determine whether funds have a physical presence (head or research office) within their investment region. In Table 1, we breakdown the funds in our sample by investment style and region, and report various summary statistics.

2. Asset based style factors for hedge fund returns

First, we augment the Fung and Hsieh (2004) model so as to better explain risk in Asian hedge funds. To identify additional factors, we analyse the principal components of style/region intersections with at least five funds. Table 2 reports the R-squares from the regressions of fund style/region returns on the top ten components. The R-squares reveal that the dominant first component, which accounts for about 51% of the variation in fund returns, is likely to be an equity factor since it well explains Equity Long/Short fund returns.

Figure 2 - Explaining hedge fund returns: A principal components analysis

Geography of Hedge Funds - Figure 2


To link the first component to market prices, we compute its correlations with the Datastream Asia Ex Japan, Asia, Pacific Basin and Japan equity market indices. Since the correlation with the Asia Ex Japan index, at 0.82, is the highest, we augment the model with the Asia Ex Japan (henceforth ASIAMRF) and the Japan (henceforth JAPMRF) index excess returns:


Geography of Hedge Funds - Equation 1


where rim is the monthly return on portfolio i in excess of the risk-free rate, SNPMRF is the S&P 500 return minus risk-free rate, SCMLC is the Wilshire small minus large cap return, BD10RET is the change in the constant maturity yield of the 10-year Treasury and BAAMTSY is the change in the spread of Moody's Baa – 10-year Treasury. PTFSBD, PTFSFX, and PTFSCOM are the bond, forex, and commodities PTFS respectively, where PTFS is primitive trend following strategy [see Fung and Hsieh (2004)]. In results not reported, we find that the augmented factor model achieves an adjusted R-square of 63% that is higher than that achieved by the non-augmented model (i.e., 52%), underscoring the efficacy of the augmented factor model.

3. Geography and hedge fund risk-adjusted performance

3.1. The cross-section of hedge fund alpha
To investigate the relationship between geography and hedge fund alpha, we regress the cross-section of hedge fund alpha, measured relative to the augmented Fung and Hsieh (2004) model, on an investment region presence variable (henceforth PRESENCE). We set PRESENCE equal to one for hedge funds with a head office or research office in their respective investment regions, and equal to zero otherwise. For funds with at least 24 months of returns, we calculate monthly fund alpha or as fund excess returns minus the factor realisations times loadings estimated over the entire sample period:


Geography of Hedge Funds - Equation 2


Then, we estimate the following pooled OLS regressions:


Geography of Hedge Funds - Equation 3

where PERFFEE is performance fee, MGTFEE is management fee, REDEMP is redemption period, MININV is minimum investment, FUNDSIZE is fund size, FAMSIZE is family size, STYLEDUM is investment style dummy and YRDUM is year dummy. The multivariate regression controls for other fund characteristics that may affect returns.


Figure 3

Geography of Hedge Funds - Figure 3 


The results are striking. The coefficient estimate on PRESENCE in the univariate regression (leftmost column of Table 3) suggests that nearby funds outperform distant funds by 0.44% per month or 5.28% per year. Even after controlling for the other fund characteristics, nearby funds still outperform distant funds by 4.68% per year. Both these effects are statistically significant at the 1% level. Since the pooled OLS regressions ignore cross-correlation in residuals, we also estimate regressions run using the Fama and MacBeth (1973) method. The Fama-MacBeth regression estimates reported in Table 3 strongly corroborate our prior findings.

3.2. Sorts on hedge fund geography

Next, we measure the spread between equal-weighted portfolios of funds with (portfolio A) and without (portfolio B) investment region presence. Panel A of Table 4 shows that the hypothetical strategy of buying nearby funds and shorting distant funds yields a risk-adjusted return of 4.54% per year (t-statistic = 3.09). This suggests that astute hedge fund investors can benefit from the local informational advantage of nearby funds. Complementing these results, Figure 1 illustrates the monthly cumulative average residuals (henceforth CARs) from portfolios A and B. CAR is the cumulative difference between a portfolio's excess return and its factor loadings multiplied by the factor realisations. The CARs indicate that portfolio A consistently outperforms portfolio B over the entire sample period.


Figure 4


Geography of Hedge Funds - Figure 4


Figure 5

Geography of Hedge Funds - Figure 5


Dovrak (2005), and Choe, Kho, and Stulz (2005) argue that the local/foreign informational asymmetry is particularly severe in Emerging Markets. To check this, we break down the analysis by region for regions with least 20 funds. The spread alphas in Panels B – E of Table 4 indicate that the results are robust across regions. They are consistently above 4% per year for all four investment regions. Moreover, the spread alphas for Asia including Japan and emerging markets are statistically significant at the 1% level. Indeed, the spread alpha is highest and most significant for emerging market funds (t-statistic = 2.77). This dovetails with the intuition that any local informational advantage should be strongest in emerging markets.

4. Conclusion

The results in this paper tell a consistent story. Hedge funds with a physical presence 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 in fund portfolio sorts. Moreover, the overperformance is exceptionally strong for investment regions (e.g., Emerging Markets) where the local/foreign information asymmetry is likely to be acute. Collectively, these results suggest that nearby hedge funds enjoy a local informational advantage.

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.

Dvorak, T., 2005. Do domestic investors have an information advantage? Evidence from Indonesia. Journal of Finance 60, 817-839.

Fama, E., MacBeth, J.D., 1973. Risk, return, and equilibrium: empirical tests. Journal of Political Economy 81, 607-636.

Fung, W., Hsieh, D., 2004. Hedge fund benchmarks: A risk based approach. Financial Analyst Journal 60, 65-80.

Ragaas De Ramos, R., 2006. Hong Kong outpaces Singapore in snaring hedge fund assets. The Wall Street Journal, May 15 2006.



Fund size category
Fund size (in millions US$)



1 - This article is a summary. The full research paper can be found below.

2 - – AIMA is a co-founder of the CAIA Association

3 - A Wall Street Journal report stated that a distinct advantage for Hong Kong vis à vis Singapore as a hedge fund hub is its proximity to China (Raagas De Ramos, 2006).

4 - The Japan equity factor (JAPMRF) is included to help explain the returns of Japan only funds.


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