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Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies

Author

Listed:
  • Jakub Michańków

    (Cracow University of Economics, Department of Informatics; University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)

  • Paweł Sakowski

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)

Abstract

This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, regarding risk-adjusted return metrics on the out-of-sample data.

Suggested Citation

  • Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023. "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers 2023-23, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2023-23
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    File URL: https://www.wne.uw.edu.pl/download_file/3237/0
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    References listed on IDEAS

    as
    1. Topcu, Mert & Gulal, Omer Serkan, 2020. "The impact of COVID-19 on emerging stock markets," Finance Research Letters, Elsevier, vol. 36(C).
    2. Caporale, Guglielmo Maria & Plastun, Alex, 2019. "The day of the week effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 31(C).
    3. Grobys, Klaus & Ahmed, Shaker & Sapkota, Niranjan, 2020. "Technical trading rules in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 32(C).
    4. Junming Yang & Yaoqi Li & Xuanyu Chen & Jiahang Cao & Kangkang Jiang, 2019. "Deep Learning for Stock Selection Based on High Frequency Price-Volume Data," Papers 1911.02502, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    machine learning; recurrent neural networks; long short-term memory; algorithmic investment strategies; testing architecture; loss function; walk-forward optimization; over-optimization;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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