Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/174211
Authors: 
Witzany, Jiří
Year of Publication: 
2017
Series/Report no.: 
IES Working Paper 18/2017
Abstract: 
Quantitative investment strategies are often selected from a broad class of candidate models estimated and tested on historical data. Standard statistical technique to prevent model overfitting such as out-sample back-testing turns out to be unreliable in the situation when selection is based on results of too many models tested on the holdout sample. There is an ongoing discussion how to estimate the probability of back-test overfitting and adjust the expected performance indicators like Sharpe ratio in order to reflect properly the effect of multiple testing. We propose a consistent Bayesian approach that consistently yields the desired robust estimates based on an 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.
Subjects: 
Backtest
multiple testing
bootstrapping
cross-validation
probability of backtest overfitting
investment strategy
optimization
Sharpe ratio
Bayesianprobability
MCMC
JEL: 
G1
G2
C5
G24
C11
C12
C52
Document Type: 
Working Paper

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