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

Jakub Michańków, Paweł Sakowski and Robert Ślepaczuk
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Paweł Sakowski: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance

No 2023-23, Working Papers from Faculty of Economic Sciences, University of Warsaw

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.

Keywords: machine learning; recurrent neural networks; long short-term memory; algorithmic investment strategies; testing architecture; loss function; walk-forward optimization; over-optimization (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 14 pages
Date: 2023
New Economics Papers: this item is included in nep-big and nep-cmp
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https://www.wne.uw.edu.pl/download_file/3237/0 First version, 2023 (application/pdf)

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Working Paper: Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2023-23

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