Artikel
A bayesian approach to measurement of backtest overfitting
Quantitative investment strategies are often selected from a broad class of candidate models estimated and tested on historical data. Standard statistical techniques to prevent model overfitting such as out-sample backtesting turn out to be unreliable in situations when the selection is based on results of too many models tested on the holdout sample. There is an ongoing discussion of how to estimate the probability of backtest overfitting and adjust the expected performance indicators such as the Sharpe ratio in order to reflect properly the effect of multiple testing. We propose a consistent Bayesian approach that yields the desired robust estimates on the basis of a Markov chain Monte Carlo (MCMC) simulation. The approach is tested on a class of technical trading strategies where a seemingly profitable strategy can be selected in the näive approach.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 9 ; Year: 2021 ; Issue: 1 ; Pages: 1-22 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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backtest overfitting
investment strategy
MCMC
multiple testing
- Event
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Geistige Schöpfung
- (who)
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Witzany, Jiří
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2021
- DOI
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doi:10.3390/risks9010018
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Witzany, Jiří
- MDPI
Time of origin
- 2021
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