Mutual/Hedge Funds

Do investors/traders taking cues from the trades of top performers produce the momentum effect? In his December 2006 paper entitled “Follow the Leader: Peer Effects in Mutual Fund Portfolio Decisions”, Lukasz Pomorski investigates whether actively managed equity mutual funds tend to follow the stock trading leads of outperforming peers as the picks become known via the media and quarterly filings. He defines outperforming (leader) funds in two ways: (1) funds with alphas in the top 5% over the past two years, and (2) funds on the Forbes Honor Roll (high media exposure). He calculates overall leader activity in a stock based only on trades by leader funds with a position in the stock. Using mutual fund holdings and performance data for 1980-2003 (96 quarters), he finds that: Keep Reading

When investors shop for mutual funds internationally, what variables should they consider as indicators of potential outperformance? In their November 2006 paper entitled “The Determinants of Mutual Fund Performance: A Cross-Country Study”, Miguel Ferreira, Antonio Miguel and Sofia Ramos relate risk-adjusted performance for mutual funds around the world to: (1) fund characteristics such as size, age, fees (initial charges, annual charges, and redemption charges), management structure and management tenure; (2) country variables, such as economic development, financial development and investor protection; and, (3) management geographic familiarity via segmentation into domestic, foreign and global funds. Using a sample of 10,568 open-end actively managed equity funds from 19 countries for the period 1999-2005, they conclude that: Keep Reading

Do mutual fund investors pay for performance, or for something else? Are some of them oblivious to fund performance? In their November 2006 paper entitled “Yet Another Puzzle? The Relation between Price and Performance in the Mutual Fund Industry”, Javier Gil-Bazo and Pablo Ruiz-Verdu explore the relationship between mutual fund fees and before-fee performance. Using monthly data for diversified and seasoned U.S. domestic equity mutual funds during 1961-2003 (538,813 fund-month observations), they find that: Keep Reading

Can quantitative analysts replicate the statistical performance of a given hedge fund using a set of easily tradable assets, thereby: (1) recreating the “hedge” as a transparent, liquid and cheap trading system; and, (2) establishing benchmarks truly applicable to specific hedge fund performance? In their June 2005 paper entitled “Hedge Fund Returns: You Can Make Them Yourself!”, Harry Kat and Helder Palaro describe and illustrate a statistical fund replication process. In their February 2006 paper entitled “Superstars or Average Joes? A Replication-Based Performance Evaluation of 1917 Individual Hedge Funds”, they compare the performance of hedge funds to the performance of their mechanical replicants. And, in their October 2006 paper entitled “Tell Me What You Want, What You Really, Really Want! An Exercise in Tailor-Made Synthetic Fund Creation”, they present out-of-sample tests of synthetic hedge funds with specific properties. Following are some highlights from these three papers: Keep Reading

Has dramatic growth and proliferation of hedge funds used up all the alpha, or do opportunities for excess returns still abound? In their October 2006 paper entitled “Net Inflows and Time-Varying Alphas: The Case of Hedge Funds”, Andrea Beltratti and Claudio Morana investigate whether dramatic asset growth has eroded the performance of hedge fund managers. Their analysis encompasses the following categories of hedge funds: convertible arbitrage (CA – 8%), dedicated short bias (DSB – 1%), emerging markets (EM – 4%), equity market neutral (EMN – 7%), event driven (ED – 17%), fixed income arbitrage (FIA – 7%), global macro (GM – 11%), long/short equity (LSE – 32%), managed futures (MF – 5%). The percentages indicate the share of total hedge fund assets by category as of December 2005. Using quarterly fund net returns and asset flows and appropriate return benchmarks for each fund category over the period 1994-2005, they conclude that: Keep Reading

Do mutual fund investors move their money into and out of the stock market at the right times, or the wrong times? In their August 2006 paper entitled “Mutual Fund Flows and Investor Returns: An Empirical Examination of Fund Investor Timing Ability”, Geoffrey Friesen and Travis Sapp examine the flows of funds to/from individual mutual funds to measure the timing ability of fund investors. They define a “performance gap” between the time-weighted (buy-and-hold) return and the dollar-weighted (actual investor average) return as the measure of investor timing ability. Using monthly data for 7,125 mutual funds over the period 1991-2004, they find that: Keep Reading

Do mutual funds that implement the tenets of behavioral finance, in defiance of the Efficient Market Hypothesis, outperform? Can they find and exploit systematic behavioral mispricings? In their August 2006 paper entitled “Behavioral Finance: Are the Disciples Profiting from the Doctrine?”, Colby Wright, Prithviraj Banerjee and Vaneesha Boney assess whether expert investors have validated the principles of behavioral finance by examining the aggregate performance of a group of mutual funds that practice them. Using equal-weighted data for 16 mutual funds most visibly associated with behavioral finance (see table below), they find that: Keep Reading

Do equity hedge fund managers achieve positive alpha by timing the market or by picking (for or against) the right stocks? In their July 2006 paper entitled “How Hedge Funds Beat the Market”, Craig French and Damian Ko investigate the degrees to which these two potential sources of excess returns contribute to market outperformance by hedge fund managers. Using monthly returns for a sample of 157 long-short equity hedge funds reporting over the entire period 1996-2005, they conclude that: Keep Reading

Do hedge funds eliminate, or even reverse, seasonal effects in the returns of the stock market? In his September 2006 paper entitled “Seasonality in Hedge Fund Strategies”, Yan Olszewski investigates general seasonal effects for various hedge fund strategies. Using monthly excess return data during 1990-2005 for 30 of the 37 equally-weighted Hedge Fund Research strategy indexes encompassing over 1600 funds, he finds that: Keep Reading

Is it possible to replicate the returns of hedge funds by decomposing these returns into contributions from easily tradable risk factors? In the August 2006 draft of their paper entitled “Can Hedge-Fund Returns Be Replicated?: The Linear Case”, Jasmina Hasanhodzic and Andrew Lo investigate simple models of the performance of 11 types of hedge funds based on six risk factors (related to stocks, bonds, currencies, commodities, credit, and volatility). They then use the results for the five factors that are easily tradable to create hedge fund “clones” (synthetic hedge funds) from exchange-traded assets and derivatives in two ways: (1) applying data from the full sample period (with the associated look-ahead bias); and, (2) applying data from a rolling historical 24-month period. Using data for the period February 1986 through September 2005 on 1,610 hedge funds still active (“live”) at the end of the period, they find that: Keep Reading