Biases in Hedge Funds Performance Data and Risk Measures
The hedge fund industry does not have a very evolved performance measurement framework. This makes it difficult for investors to depend on the historical records while allocating their funds.
The performance data provided by the hedge fund databases and indexes has several biases. Let's discuss some of these biases.
Self-selection: The inclusion in a hedge fund database is voluntary and the hedge fund managers can choose to include themselves or not to. In general, if a fund has poor track record it may not want to expose its performance and will decide not to be included in a database.
Backfilling: This means that when a new hedge fund is added to an index, the past performance of the fund is back-filled in the index. For example, if the hedge fund is 3 years old, it's record for the past three years will be added to the index, and the index values will be adjusted accordingly. The successful funds are more likely to be added to an index than an unsuccessful one, which creates a bias in the index. Studies suggest that backfill bias adds about 4% or more to hedge fund returns
Survivorship Bias: This is the situation where unsuccessful funds are removed from the index, and the past index values are adjusted to remove the data of the dropped fund. Since a fund is more likely to be dropped from an index because of poor performance, such actions create bias in the index. Studies suggest that survivorship bias adds about 3% or more to hedge fund returns.
Impact on Risk Measures
Even the risk measures of the hedge funds have biases.
Smooth pricing of infrequently traded assets: Hedge funds invest in many infrequently traded and illiquid assets such as real estate, and OTC instruments. Since these assets are traded less, and don't have regular price updates, they exhibit low volatility. This is called the smoothing effect, which creates a downward bias to the risk of the assets. Also, the low correlation of these alternative assets with regular asset classes such as equity, and fixed income can make the bias even larger.
Option-like investment strategies: Hedge funds have very non-linear investment strategies such as arbitrage strategies. These strategies make their returns highly skewed, and the funds exhibit high kurtosis (fat tails), and negative skewness. The traditional measures such as standard deviation and VaR will underestimate the risk of losses for these funds. Even Sharpe ratio is inappropriate for measuring performance.
Fee Structure: Hedge funds have a high fee structure, which also exhibits option-like features. There a 1-2% fixed fee plus an incentive fee of upto 20% if the returns are positive. This creates incentives for fund managers to take higher risks to maintain high performance.
To conclude, investors should study these funds carefully and account for these biases and risks before investing.
Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.