Figure 1 - available from: Journal of Asset Management
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This figure displays performance of the 60–40 portfolio of stocks and bonds for 1999–2014. The portfolio is constructed using S&P 500 Total Return index and JP Morgan Global Government Bond index.
Source publication
This article introduces a large scale simulation framework for evaluating hedge funds’ investments subject to the realistic constraints of institutional investors. The method is customizable to the preferences and constraints of individual investors, including investment objectives, performance benchmarks, rebalancing period and the desired number...
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Citations
... The dependent variable used in this analysis was the Barclay Hedge Fund Index, which, according to the indexes creators "provides a measure of the average returns of all hedge funds (except funds of funds) in the Barclay database" (BarclayHedge). According to Molyboga and L'Ahelec (2016), as referenced in Joenväärä et al. (2012, p. 4), in a comparison of the major hedge fund databases (i.e. Lipper/TASS, Eurekahedge, CIDSM, & BarclayHedge), BarclayHedge "provides the highest quality data". ...
Purpose
The purpose of this paper is to examine whether there is significant evidence that hedge fund managers engage in deceptive manipulation of their reported performance results.
Design/methodology/approach
We developed a model of hedge fund performance using standard regression analysis incorporating dependent lagged variables and an auto-regressive process. We also use the extreme bounds analysis technique to examine the robustness and sensitivity of our explanatory variables. Finally, we explored the conditional influence of the global stock market’s returns on hedge fund performance and the conditional return behavior of the hedge fund index’s performance.
Findings
We begin by identifying a model of hedge fund performance using passive index funds that is well specified and robust. Next, we examine the lag structure associated with hedge fund returns and determine that it seems to take the hedge fund managers two months to integrate the global stock market’s returns into their reported performance; however, the lagged variables were reduced from our final model. We continued to explore the smoothing behavior by conditioning the dependent lagged variables on positive and negative returns and find that managers are conservative in their estimates of positive performance events, but, when experiencing a negative result, they seem to attempt to rapidly integrate that effect into the return series. The strength of their integration increases as the magnitude of the negative performance increases. Finally, we examined the performance of returns for both the hedge fund index and our passive indices and did not find significant differences between the conditional returns.
Research limitations/implications
The results of this analysis illustrate that hedge fund performance is not all that different from the performance of passive indices included in this paper; although it does offer investors access to a unique return distribution. From a management perspective, we are reminded that we need to be cautious about hastily arriving at conclusions about something that looks different or feels different from everything else, because, at times, our preconceived notions will cause us to avoid participating in something that may add value to our organizations. From an investment perspective, sometimes having something that looks and behaves differently from everything else, improves our investment experience.
Originality/value
This paper provides a well specified and robust model of hedge fund performance and uses Extreme Bounds Analysis to test the robustness of this model. We also investigate the smoothing behavior of hedge fund performance by segmenting the returns into two cohorts and find that smoothing behavior is only significant after the hedge funds produce positive performance results, the strength of the relationship between the global stock market and hedge fund performance is more economically significant if the market generated a negative performance result in the previous period, and that as the previous period’s performance becomes increasingly negative the strength of the relationship between the hedge fund index and the global stock market increases.
We examine whether estimates of hedge fund performance reported in prior empirical research are affected by publication bias. Using a sample of 1019 intercept terms from regressions of hedge fund returns on risk factors (the “alphas”) collected from 74 studies published between 2001 and 2021, we show that the selective publication of empirical results does not significantly contaminate inferences about hedge fund returns. Most of our monthly alpha estimates adjusted for the (small) bias fall within a relatively narrow range of 30–40 basis points, indicating positive abnormal returns of hedge funds: Hedge funds generate money for investors. Studies that explicitly control for potential biases in the underlying data (e.g., backfilling and survivorship biases) report lower but still positive alphas. Our results demonstrate that despite the prevalence of publication selection bias in many other research settings, publication may not be selective when there is no strong a priori theoretical prediction about the sign of the estimated coefficients.
We examine whether the success of hedge fund market timing strategies can be replicated. We develop a methodology for creating a portfolio of ETFs to capture risk factor exposures of market timing hedge funds identified using extant market timing measures. We find that the top market timing hedge funds outperform their ETF clone peers and the superior performance cannot be replicated. We show that the irreplicable market timing skills are more profound in certain hedge fund styles. Finally, we provide evidence that the success of market timing strategies is driven by non‐cloneable hedge funds that possess managerial skills.
Standard tests for persistence in hedge fund performance are not consistent with investment practices because they ignore performance reporting delay, overlook fund selection standards of institutional investors, and often use portfolios with too many funds. This paper introduces a set of tests based on a large-scale simulation framework and stochastic dominance methodology. These tests incorporate constraints that are standard practice in the institutional investment field. To illustrate this framework, we apply it to investigate momentum in the performance of hedge funds of the managed futures industry. We find persistence in performance of the top performing fund managers that is significant in statistical and economic terms. Our methodology extends the toolbox of performance persistence tests and results in findings that can be implemented by institutional investors.
This article analyzes the portfolio management implications of using drawdownbased measures in allocation decisions. The authors introduce modified conditional expected drawdown (MCED), a new risk measure that is derived from portfolio drawdowns, or peaktotrough losses, of demeaned constituents. They show that MCED exhibits the attractive properties of coherent risk measures that are present in conditional expected drawdown (CED) but are lacking in the historical maximum drawdown (MDD) commonly used in the industry. This article introduces a robust block bootstrap approach to calculating CED, MCED, and marginal contributions from portfolio constituents. First, the authors show that MCED is less sensitive to sample error than CED and MDD. Second, they evaluate several drawdownbased minimum risk and equalrisk allocation approaches within the largescale simulation framework of Molyboga and L'Ahelec via a subset of hedge funds in the managed futures space that contains 613 live and 1,384 defunct funds over the 1993-2015 period. The authors find that the MCEDbased equalrisk approach dominates the other drawdownbased techniques but does not consistently outperform the simple equal volatility-adjusted approach. This finding highlights the importance of carefully accounting for sample error, as reported by DeMiquel et al., and cautions against overreliance on drawdownbased measures in portfolio management.
