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On the backtesting of trading strategies

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  • Yen H. Lok

Abstract

The contribution of this paper is two-fold. The first contribution is the development of a filter-combine scheme for trading strategies to diversify model risk. Multiple statistical machine learning models are used to predict the price direction of multiple assets. We demonstrate the effectiveness of model-averaging after under-performing models are removed via a filtering algorithm. The second contribution is the identification of appropriate measures of performance for selecting models. In the literature, different measures are usually designed for different applications and purposes, and it is not always clear as to whether certain measures are relevant to a particular trading strategy. By identifying relevant measures, one can identify the key drivers underlying well-performing models, and allocate more resources in optimising and improving the appropriate models.

Suggested Citation

  • Yen H. Lok, 2018. "On the backtesting of trading strategies," 2018 Papers plo493, Job Market Papers.
    • Handle: RePEc:jmp:jm2018:plo493
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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