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

Author

Listed:
  • Jakub Micha'nk'ow
  • Pawe{l} Sakowski
  • Robert 'Slepaczuk

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, with regard to risk-adjusted return metrics on the out-of-sample data.

Suggested Citation

  • Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023. "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Papers 2309.10546, arXiv.org.
    • Handle: RePEc:arx:papers:2309.10546
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    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.
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    More about this item

    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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