Mean Absolute Directional Loss as a New Loss Function for Machine
  Learning Problems in Algorithmic Investment Strategies
Jakub Michańków Paweł Sakowski Robert Ślepaczuk
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.