Jun Duanmu
Ph.D. in Finance
Research
Hedge funds are considered the apex of professionally actively managed investment funds, and have experienced tremendous growth in recent years. Hedge fund researchers commonly focus on alpha, which is a proxy for superior performance relative to the factor returns. However, extant literatures document that relatively few funds produce significant alpha and funds exhibit exposure to systematic risk factors. In my research, I focus on hedge fund performance that is attributable to market factors. I develop a more comprehensive method to investigate hedge fund beta management, and provide evidence of the efficacy of beta activity in explaining hedge fund returns.
(SSRN Author Page: http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=1806015)
In Search of Missing Risk Factors: Hedge Fund Return Replications with ETFs,
with Yongjia Li and Alexey Malakhov
Properly considering all potential risk factors through tradable liquid portfolios in the context of a risk based factor model is paramount to quantifying the benefits of investing in hedge funds. We attempt to span the space of potential risk factors with exchange traded funds (ETFs). We develop a methodology of hedge fund return replication with ETFs based on cluster analysis and LASSO factor selection that overcomes multicollinearity among ETFs and the data mining bias. We find that the overall out-of-sample accuracy of hedge fund replication with ETFs increases with the number of ETFs available. This is consistent with our interpretation of ETF returns as proxies to a multitude of alternative risk factors that could be driving hedge fund returns.
We further consider portfolios of “cloneable” and “non-cloneable” hedge funds, defined as top and bottom in-sample R2 matches. We find superior risk-adjusted performance for “non-cloneable” funds, while “cloneable” funds fail to deliver significantly positive risk-adjusted performance. We conclude that our methodology provides value in both identifying skilled managers of “non-cloneable” hedge funds, and also successfully replicating out-of-sample returns that are due to alternative risk exposures of “cloneable” hedge funds, thus providing a transparent and liquid alternative to investors who may find these return patterns attractive.
This figure presents the out-of-sample comparison of an anonymous hedge fund and its clone, constructed according to our in-sample matching methodology. This hedge fund is in the “fixed income” self-reported style, it has an inception year of 2004, and it was active at the end of our study period.
Cumulative wealth (in logarithmic scale) from a $1 investment in beta active and alpha active portfolios of funds in the top quartile of respective metrics and compared to an equally weighted index of all Bloomberg Peer hedge funds. Initial portfolios are constructed as of 12/31/1999 and rebalanced annually.
Beta Active Hedge Fund Management,
with Alexey Malakhov and William McCumber
(revise and resubmit, Journal of Financial and Quantitative Analysis)
We consider two distinct styles of active portfolio management: alpha active, wherein managers’ positions are uncorrelated with particular benchmarks, and beta active, wherein managers take positions that are correlated with identifiable benchmark factors. We construct a measure of overall beta activity of fund managers, and find ample evidence that top beta active managers deliver superior out-of-sample performance compared to top alpha active managers. Furthermore, our measure of beta activity successfully captures the time varying nature of beta exposures that could be interpreted as a common factor driving the long term predictive power of both SR (systematic risk) and R Squared measures.
Smart Beta ETF Portfolios: Cloning Beta Active Hedge Funds
with Yongjia Li and Alexey Malakhov
In this paper, we show that hedge funds whose returns are driven by active beta management of exposures to latent risk factors can be successfully cloned. Specifically, we develop an algorithm to construct smart beta ETF portfolios that through annual rebalancing replicate the risk factor exposures in portfolios formed on active beta hedge funds. Moreover, focusing the strategy on replicating portfolios comprised of outstanding active beta hedge funds yield smart beta ETF portfolios that generate superior long-term risk-adjusted performance..