The Impact of Quantitative Methods on Hedge Fund Performance

Date01 November 2014
DOIhttp://doi.org/10.1111/eufm.12035
Publication Date01 November 2014
AuthorLudwig Chincarini
The Impact of Quantitative Methods
on Hedge Fund Performance
Ludwig Chincarini
School of Management at the University of San Francisco, 101 Howard Street, Suite 500, Ofce 600,
San Francisco, CA 94105, USA and Academic Counsel of IndexIQ
E-mails: chincarinil@hotmail.com; lbchincarini@usfca.edu
Abstract
In the last 20 years, the amount of assets managed by quantitative and qualitative
hedge funds have grown dramatically. We examine the difference between quantitative
and qualitative hedge funds in a variety of ways, including management differences
and performance differences. We nd that both quantitative and qualitative hedge
funds have positive riskadjusted returns. We also nd that overall, quantitative hedge
funds as a group have higher as than qualitative hedge funds. The outperformance
might be as high as 72 bps per year when considering all risk factors. We also suggest
that this additional performance may be due to better timing ability.
Keywords: quantitative portfolio management, alpha, hedge funds, returns
JEL classification: G0, G10, G11, G23
1. Introduction
In the last few years, quantitative portfolio management and quantitative equity portfolio
management have been on the rise (see Figure 1). The total assets managed by quantitative
funds grew by 807% from $9.98 to $90.48 billion, while the assets managed by qualitative
funds grew by 609% from $18.61 billion to $131.92 billion over the period from 1994 to
2009.
1
The growth in this method of investing can be attributed to many factors, but
perhaps four of them stand out. First, there has been an advancement in the knowledge and
The author would like to thank Alex Nakao for dedicated research assistance, Jason Price,
Mary Martin, Scott Esser for help with the data, Stephen Brown, David Hsieh, Bill Fung,
Daehwan Kim, Frank OHara, Tom Vinaimont, Richard Smith, Thomas Kim, David Mayer,
Peter Chung, Pierangelo De Pace, three anonymous referees, the editor, John Doukas, and
Kristin McDonald for technical help. I also would like to thank seminar participants at the
European Financial Management Conference in Aarhus, Denmark, Cambridge Research
Associates, NERA, PIMCO, ISCTE business school, and the Vlerick School of
Management.
1
These numbers represent the numbers in the HFR database and not the actual numbers of the
entire industry.
European Financial Management, Vol. 20, No. 5, 2014, 857890
doi: 10.1111/eufm.12035
© 2013 John Wiley & Sons Ltd
tools for assessing nancial markets quantitatively. Second, there has been a dramatic
improvement in the technology required to efciently examine the markets quantitatively.
Third, there has been an increasing demand from pension funds and other large
institutional investors for an investment process. Quantitative investing lends itself more
easily to a more structured investment process. Fourth, some have argued that a
quantitative disciplined investment process might lead to superior returns than a less
quantitative investment process. In particular, Chincarini and Kim (2006) have discussed
the potential advantages and disadvantages of quantitative funds (Table 1) arguing that the
advantages most likely outweigh the disadvantages.
This paper is focused on addressing the last of these potential reasons for the growth in
quantitative portfolio management. In particular, we attempt to study the performance
characteristics of quantitative and qualitative hedge funds. The advantages for quantitative
funds include the breadth of selections, the elimination of behavioral errors (which might have
been particularly important during the nancial crisis of 2008), and the potential lower
administration costs (after hedge fund fees). The disadvantages for quantitative hedge funds
include the reduced use of qualitative types of data, the reliance on historical data, and the
lacking of an ability to quickly react to new economic paradigms. Finally, there is the potential
of data mining, which will lead to strategies that arent as effective once implemented. In this
paper, we focus primarily on examining the return differences rather than attempting to detail
which of the advantages or disadvantages is central to the return differences.
There has been a vast amount of research to measure and understand hedge fund
performance (Agarwal and Naik, 2004; Liang, 1999, 2001; Edwards and Caglayan,
2001). Recently, Agarwal et al. (2013) show that the condential holdings of hedge
0 50 100 150 200
AUM (billions)
1995 2000 2005 2010
Year
Quantitative Qualitative
Fig. 1. The growth of quantitative and qualitative funds.
Thisgure represents the growthin assets under management(AUM) for quantitativeand qualitativehedge
fundsin billions of dollars.The qualitative fundsinitial value was normalisedto the value in thedatabase of
quantitative funds as of January 1994 at $9.98B. Qualitative fund total on this date was $18.61B.
© 2013 John Wiley & Sons Ltd
858 Ludwig Chincarini
funds as measured by amendments to their 13Flings provide superior performance to
the rest of the holdings of hedge fund portfolios. Jame (2012) examines the daily
trading of hedge funds and does not nd abnormal returns for the average hedge fund, but
nds outperformance for the top decile of hedge funds. Sadka (2012) shows that hedge
funds that load on liquidity risk have signicantly higher returns and this could explain the
higher alphas in the hedge fund industry. Chen et al. (2012) use an expectation
maximisation algorithm and nd that about 50% of hedge funds have positive skill and
that these funds deliver superior outofsample alpha. Ammann et al. (2013) use probit
regressions to identify characteristics that might be related to performance persistence.
They nd statistically signicant performance persistence for up to 36 months. Tudor and
Cao (2012) study the ability of hedge funds to produce absolute performance and nd that
certain hedge fund strategies have a better probability of producing absolute performance.
Many papers nd positive market timing ability of hedge funds (Chen, 2007; Chen and
Liang, 2007; Chincarini and Nakao, 2011; Chincarini, 2012a; Li and Shawky, 2013; Cao
et al., 2013), although some do not nd any market timing ability (Park, 2010).
Table 1
The advantages and disadvantages of quantitative versus qualitative portfolio management
This table presents the advantages and disadvantages to quantitative and qualitative investing. High or
low is used to indicate if a specic style of portfolio management has a high or low exposure to a
particular criteria. Objectivity represents the ability to remain objective and nonemotional in trading,
Breadth represents the ability to easily examine large amounts of securities for inclusion in the portfolio,
Behavioural Errors indicates the possibility to make human behavioural errors in selecting securities,
Replicability represents the ability to transfer the portfolio building knowledge to another entity, Costs
represents the costs required to manage the portfolio, Controlled risk represents the ability to precisly
quantify the risk in the strategies, Qualitative Inputs represents the ability to select securities based on
nonquantitative data, Historical Data Reliance represents the degree to which the portfolio relies on
historical data patterns to choose securities, Data Mining represents the tendency for the security
selection to have been based on data mined research, and Reactivity represents the ability of the security
selection procedure to react quickly to new paradigms or market events.
Source: Chincarini and Kim (2006).
Advantages
Criteria Quantitative Qualitative
Objectivity High Low
Breadth High Low
Behavioural Errors Low High
Replicability High Low
Costs Low High
Controlled Risk High Low
Disadvantages
Criteria Quantitative Qualitative
Qualitative Inputs Low High
Historical Data Reliance High Low
Data Mining High Low
Reactivity Low High
© 2013 John Wiley & Sons Ltd
The Impact of Quantitative Methods on Hedge Fund Performance 859

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