Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance
David H. Bailey
Lawrence Berkeley National Laboratory; University of California, Davis
Jonathan M. Borwein
University of Newcastle (Australia); Royal Society of Canada; Australian Academy of Science
Marcos Lopez de Prado
Guggenheim Partners, LLC; Lawrence Berkeley National Laboratory; Harvard University - RCC
Qiji Jim Zhu
Western Michigan University
April 1, 2014
Notices of the American Mathematical Society, 61(5), May 2014, pp.458-471
We prove that high simulated performance is easily achievable after backtesting a relatively small number of alternative strategy configurations, a practice we denote “backtest overfitting”. The higher the number of configurations tried, the greater is the probability that the backtest is overfit. Because most financial analysts and academics rarely report the number of configurations tried for a given backtest, investors cannot evaluate the degree of overfitting in most investment proposals.
The implication is that investors can be easily misled into allocating capital to strategies that appear to be mathematically sound and empirically supported by an outstanding backtest. Under memory effects, backtest overfitting leads to negative expected returns out-of-sample, rather than zero performance. This may be one of several reasons why so many quantitative funds appear to fail.
Number of Pages in PDF File: 14
Keywords: backtest, historical simulation, probability of backtest over-fitting, investment strategy, optimization, Sharpe ratio, minimum backtest length, performance degradation
JEL Classification: G0, G1, G2, G15, G24, E44
Date posted: August 12, 2013 ; Last revised: July 5, 2015
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