Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

Hedge funds databases are typically subject to high attrition ratesbecause of fund termination and self-selection. Even when all fundsare included up to their last available return, one cannot preventthat ex post conditioning biases a.ect standard estimates ofperformance persistence. In this paper we analyze the persistence inthe performance of U.S. hedge funds taking into account look-aheadbias (multi-period sampling bias). To do so, we model attrition ofhedge funds and analyze how it depends upon historical performance.Next, we use a weighting procedure that eliminates look-ahead bias inmeasures for performance persistence. The results show that the impactof look-ahead bias is quite severe, even though positive and negativesurvival-related biases are sometimes suggested to cancel out. Athorizons of one and four quarters, we find clear evidence of positivepersistence in hedge fund returns, also after correcting forinvestment style. At the two-year horizon, past winning funds tend toperform poorly in the future.survival;performance measurement;investments;individual profiles;hedge funds

Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

Hedge funds databases are typicall subject to high attrition rates because of fund termination and self-selection.Even when all funds are included up to their last available return, one cannot prevent that ex post conditioning biases affect standard estimates of performance persistence.In this paper we analyze the persistence in the performance of U.S. hedge funds taking into account look-ahead bias (multi-period sampling bias).To do so, we model attrition of hedge funds and analyze how it depends upon historical performance.Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence.The results show that the impact of look-ahead bias is quitesevere, even though positive and negative survivalrelated biases are sometimes suggested to cancel out.At horizons of one and four quarters, we find clear evidence of positive persistence in hedge fund returns, also after correcting for investment style.At the two-year horizon, past winning funds tend to perform poorly in the future.hedging;performance measurement;investment trusts

Equivalence of robust stabilization and robust performance via feedback

One approach to robust control for linear plants with structured uncertainty as well as for linear parameter-varying (LPV) plants (where the controller has on-line access to the varying plant parameters) is through linear-fractional-transformation (LFT) models. Control issues to be addressed by controller design in this formalism include robust stability and robust performance. Here robust performance is defined as the achievement of a uniform specified $L^{2}$-gain tolerance for a disturbance-to-error map combined with robust stability. By setting the disturbance and error channels equal to zero, it is clear that any criterion for robust performance also produces a criterion for robust stability. Counter-intuitively, as a consequence of the so-called Main Loop Theorem, application of a result on robust stability to a feedback configuration with an artificial full-block uncertainty operator added in feedback connection between the error and disturbance signals produces a result on robust performance. The main result here is that this performance-to-stabilization reduction principle must be handled with care for the case of dynamic feedback compensation: casual application of this principle leads to the solution of a physically uninteresting problem, where the controller is assumed to have access to the states in the artificially-added feedback loop. Application of the principle using a known more refined dynamic-control robust stability criterion, where the user is allowed to specify controller partial-state dimensions, leads to correct robust-performance results. These latter results involve rank conditions in addition to Linear Matrix Inequality (LMI) conditions.Comment: 20 page

Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

Hedge funds databases are typicall subject to high attrition rates because of fund termination and self-selection.Even when all funds are included up to their last available return, one cannot prevent that ex post conditioning biases affect standard estimates of performance persistence.In this paper we analyze the persistence in the performance of U.S. hedge funds taking into account look-ahead bias (multi-period sampling bias).To do so, we model attrition of hedge funds and analyze how it depends upon historical performance.Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence.The results show that the impact of look-ahead bias is quitesevere, even though positive and negative survivalrelated biases are sometimes suggested to cancel out.At horizons of one and four quarters, we find clear evidence of positive persistence in hedge fund returns, also after correcting for investment style.At the two-year horizon, past winning funds tend to perform poorly in the future

Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

Hedge funds databases are typically subject to high attrition rates because of fund termination and self-selection. Even when all funds are included up to their last available return, one cannot prevent that ex post conditioning biases a.ect standard estimates of performance persistence. In this paper we analyze the persistence in the performance of U.S. hedge funds taking into account look-ahead bias (multi-period sampling bias). To do so, we model attrition of hedge funds and analyze how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence. The results show that the impact of look-ahead bias is quite severe, even though positive and negative survival-related biases are sometimes suggested to cancel out. At horizons of one and four quarters, we find clear evidence of positive persistence in hedge fund returns, also after correcting for investment style. At the two-year horizon, past winning funds tend to perform poorly in the future

Survival, Look-Ahead Bias and the Persistence in Hedge Fund Performance

We analyze the performance persistence in hedge funds taking into account look-ahead bias (multi-period sampling bias). We model liquidation of hedge funds by analyzing how it depends upon historical performance. Next, we use a weighting procedure that eliminates look-ahead bias in measures for performance persistence. In contrast to earlier results for mutual funds, the impact of look-ahead bias is exacerbated for hedge funds due to their greater level of total risk. At the four-quarter horizon, look-ahead bias can be as much as 3.8%, depending upon the decile of the distribution. We find positive persistence in hedge fund quarterly returns after correcting for investment style. The empirical pattern at the annual level is also consistent with positive persistence, but its statistical significance is weak

The Bezout equation on the right half plane in a Wiener space setting

This paper deals with the Bezout equation $G(s)X(s)=I_m$, $\Re s \geq 0$, in the Wiener space of analytic matrix-valued functions on the right half plane. In particular, $G$ is an $m\times p$ matrix-valued analytic Wiener function, where $p\geq m$, and the solution $X$ is required to be an analytic Wiener function of size $p\times m$. The set of all solutions is described explicitly in terms of a $p\times p$ matrix-valued analytic Wiener function $Y$, which has an inverse in the analytic Wiener space, and an associated inner function $\Theta$ defined by $Y$ and the value of $G$ at infinity. Among the solutions, one is identified that minimizes the $H^2$-norm. A Wiener space version of Tolokonnikov's lemma plays an important role in the proofs. The results presented are natural analogs of those obtained for the discrete case in [11].Comment: 15 page