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

No matter what kind of theoretical concept is incorporated into the heart of such an investment model, we have a few similar issues that have to be properly addressed to increase the probability of generating efficient signals on out-of-sample (OOS) data. Among many others, these include the architecture of testing various models (machine learning, econometric, macroeconomic, or statistical approaches), the structure of the walk-forward procedure (usually consisting of numerous training, validation, and testing periods of different lengths), hyperparameters tuning and parameters optimization, model estimation phase, and finally the appropriate set of time series with possibly diverse characteristics of their distributions. The point is that all of these problems have to be designed optimally in order to avoid potential over-fitting issues and find the best possible variant of the investment model. Majority of papers undertaking the topic of AIS testing do not put proper attention to these problems and focus only on the empirical testing of one or several selected investment models, on a single instrument, over quite short data periods, usually without explaining the details of the whole procedure.