A1971
Title: Mean absolute directional loss as a new loss function for machine learning problems in algorithmic investment strategies
Authors: Pawel Sakowski - University of Warsaw (Poland) [presenting]
Robert Slepaczuk - University of Warsaw (Poland)
Jakub Michankow - University of Warsaw (Poland)
Abstract: The focus is on 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.
