Do Sophisticated Investors Believe in the Law of Small Numbers?

Believers in the law of small numbers tend to overinfer the outcome of a random process after a small series of observations. They believe that small samples replicate the probability distribution properties of the population. We provide empirical evidence indicating that investors are mistakenly driven by this psychological bias when hiring or firing a fund manager after a successful (or losing) performance streak. Using quarterly data between 1994 and 2000 of 752 hedge funds, we analyze actual money flows into and out of hedge funds and their relationship with the length of the streak. We first show that persistence patterns have a predictive ability of future relative performance of a manager: the longer the winner streak, the larger the probability for a fund to remain a winner. Investors, in turn, appear to be aware of quality dispersion across managers and respond by following a momentum strategy: the longer the winning (losing) streak, the more likely they will invest in (divest from) that fund. Yet, we find that investors place excessive weight in the managers’ track record as a criterion for decision. Our model shows that the length of the streak has an economically and statistically significant impact on money flows beyond rationally expected performance, which confirms a “hot-hand†bias driving to a large extent momentum investing. Apparently, even sophisticated investors exhibit psychological biases that may have adverse effects on their wealth.Performance Persistence;Overreaction;Hedge Fund Investors;Hot-Hand Bias;Law of Small Numbers

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

Do Sophisticated Investors Believe in the Law of Small Numbers?

Believers in the law of small numbers tend to overinfer the outcome of a random process after a small series of observations. They believe that small samples replicate the probability distribution properties of the population. We provide empirical evidence indicating that investors are mistakenly driven by this psychological bias when hiring or firing a fund manager after a successful (or losing) performance streak. Using quarterly data between 1994 and 2000 of 752 hedge funds, we analyze actual money flows into and out of hedge funds and their relationship with the length of the streak. We first show that persistence patterns have a predictive ability of future relative performance of a manager: the longer the winner streak, the larger the probability for a fund to remain a winner. Investors, in turn, appear to be aware of quality dispersion across managers and respond by following a momentum strategy: the longer the winning (losing) streak, the more likely they will invest in (divest from) that fund. Yet, we find that investors place excessive weight in the managers’ track record as a criterion for decision. Our model shows that the length of the streak has an economically and statistically significant impact on money flows beyond rationally expected performance, which confirms a “hot-hand” bias driving to a large extent momentum investing. Apparently, even sophisticated investors exhibit psychological biases that may have adverse effects on their wealth

A Portrait of Hedge Fund Investors: Flows, Performance and Smart Money

We explore the flow-performance interrelation by explicitly separating the investment and divestment decisions of hedge fund investors. The results show that different determinants and evaluation horizons underlie both decisions. While money inflows are sensitive to past long-run performance, outflows exhibit an immediate and sustained response to past performance in the short run. As a consequence, the shape of the flow-performance relation differs depending on the time horizon being analyzed. We find a weaker flow-performance relation for winning funds at quarterly horizons compared to annual horizons, which may explain why quarterly persistence in hedge fund performance is not competed away. Indeed, we also find evidence that most investors are unable to exploit the persistence of the winners. Conversely, investors are fast and successful in deallocating from the persistent losers, ensuring a disciplining mechanism for lowquality funds. Further, our findings do not support the existence of smart money

FEM complex envelope displacement (CED) analysis for damped high frequency vibrations

Complex envelope displacement analysis (CEDA, introduced by Carcaterraand Sestieri) seems to be a promising approach in the mid or high frequency range for vibroacoustic computations. The CED analysis solves for a smooth or low wave number transformed displacement variable from an accordingly transformed partial differential equation, a quasi-static problem. This paper addresses the specific problems that have been solved for generalisation of the original CED analysis to both damped high frequency vibrations in two point boundary value problems as well as the implementation for damped FEM calculations. A numerical example of the longitudinal vibration in a bar is used to illustrate and assess the new FEM method

FEM complex envelope displacement (CED) analysis for damped high frequency vibrations

Complex envelope displacement analysis (CEDA, introduced by Carcaterraand Sestieri) seems to be a promising approach in the mid or high frequency range for vibroacoustic computations. The CED analysis solves for a smooth or low wave number transformed displacement variable from an accordingly transformed partial differential equation, a quasi-static problem. This paper addresses the specific problems that have been solved for generalisation of the original CED analysis to both damped high frequency vibrations in two point boundary value problems as well as the implementation for damped FEM calculations. A numerical example of the longitudinal vibration in a bar is used to illustrate and assess the new FEM method

Trace checking of Metric Temporal Logic with Aggregating Modalities using MapReduce

Modern complex software systems produce a large amount of execution data, often stored in logs. These logs can be analyzed using trace checking techniques to check whether the system complies with its requirements specifications. Often these specifications express quantitative properties of the system, which include timing constraints as well as higher-level constraints on the occurrences of significant events, expressed using aggregate operators. In this paper we present an algorithm that exploits the MapReduce programming model to check specifications expressed in a metric temporal logic with aggregating modalities, over large execution traces. The algorithm exploits the structure of the formula to parallelize the evaluation, with a significant gain in time. We report on the assessment of the implementation - based on the Hadoop framework - of the proposed algorithm and comment on its scalability.Comment: 16 pages, 6 figures, Extended version of the SEFM 2014 pape

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