return smoothing
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2018 ◽  
Vol 4 (4) ◽  
pp. 203-222 ◽  
Author(s):  
Jing-zhi Huang ◽  
John Liechty ◽  
Marco Rossi


2018 ◽  
Vol 32 (3) ◽  
pp. 297-310
Author(s):  
Jiaqi Chen ◽  
Michael L. Tindall ◽  
Wenbo Wu


Author(s):  
Jeffrey S. Smith ◽  
Kenneth Small ◽  
Phillip Njoroge

This chapter discusses investment benchmarking and measurement bias in hedge fund performance. A good benchmark should be unambiguous, investible, measurable, appropriate, reflective of current investment opinions, specified in advance, and accountable. Additionally, a good benchmark should be simple, easily replicable, comparable, and representative of the market that the benchmark is trying to capture. Several biases, such as database selection bias, survivorship bias, style classification bias, backfill bias, self-reporting bias, and return-smoothing bias exist that impede the process of creating a benchmark. These biases increase the difficulty of studying hedge fund returns and managerial skill. However, most of the academic research on hedge fund returns report positive alphas for hedge funds.



2017 ◽  
Vol 63 (7) ◽  
pp. 2233-2250 ◽  
Author(s):  
Charles Cao ◽  
Grant Farnsworth ◽  
Bing Liang ◽  
Andrew W. Lo




2015 ◽  
Author(s):  
Raimond Maurer ◽  
Olivia S. Mitchell ◽  
Ralph Rogalla ◽  
Ivonne Siegelin




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