David H. Bailey, Jonathan M. Borwein, Marcos Lopez de Prado, Qiji Jim Zhu

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Recent computational advances allow investment managers to search for profitable investment strategies. In many instances, that search involves a pseudo-mathematical argument, which is spuriously validated through a simulation of its historical performance (also called backtest).

We prove that high 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 financial analysts 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. This practice is particularly pernicious, because due to the nature of financial time series, backtest overfitting has a detrimental effect on the future strategy’s performance.

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David H. Bailey,
Jonathan M. Borwein,
Marcos Lopez de Prado,
Qiji Jim Zhu ,
et al.
"Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance."
SSRN Electronic Journal.
doi:10.2139/ssrn.2308659.
Retrieved 06/24/2019 from researchcompendia.org/compendia/2014.409/

David H. Bailey,
Jonathan M. Borwein,
Marcos Lopez de Prado,
Qiji Jim Zhu ,
et al.
"Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance."
SSRN Electronic Journal.
doi:10.2139/ssrn.2308659.
Retrieved 06/24/2019 from researchcompendia.org/compendia/2014.409/

Compendium Type: Published Papers Primary Research Field: Computer and Information Sciences Secondary Research Field: Mathematics Content License: Public Domain Mark Code License: MIT License