Explainable AI for Financial Forecasting

One of the most important steps when employing machine learning approaches is the feature engineering process. It plays a key role in the identification of features that can effectively help modeling the given classification or regression task. This process is usually not trivial and it might lead to the development of handcrafted features. Within the financial domain, this step is even more complex given the general low correlation between features extracted from financial data and their associated labels. This represents indeed a challenging task that it is possible to explore today through the explainable artificial intelligence approaches that have recently appeared in the literature. This paper examines the potential of machine learning automatic feature selection process to support decisions in financial forecasting. Using explainable artificial intelligence methods, we develop different feature selection strategies in an applied financial setting where we want to predict the next-day returns for a set of input stocks. We propose to identify the relevant features for each stock individually; in this way, we take into account the heterogeneous stocks’ behavior. We demonstrate that our approach can separate important features from unimportant ones and bring prediction performance improvements as shown by our performed comparisons between our proposed strategies and several state-of-the-art baselines on real-world financial time series