Explainable Artificial Intelligence methods for financial time series

We consider the problem of developing explainable Artificial Intelligence methods to interpret the results of Artificial Intelligence models for time series data, taking time dependency into account. To thisend, we extend the Shapley-Lorenz method, normalised by construction, to Artificial Intelligence for time series, such as neural networks and recurrent neural networks. We illustrate the application of our proposal toa time series of Bitcoin prices, which acts as the response variable, alongwith time series of classical financial prices, which act as explanatory variables.Three main findings emerge from the analysis. First, recurrent neural networks lead to a better performance, in terms of accuracy and robustness, with respect to classic neural networks. Second, the best performingmodels indicate that Bitcoin prices are affected mostly by their lagged values, and that their explainability, in terms of classical financial assets, is limited. Third, although limited, the contribution of classical assets toBitcoin price prediction is well captured by recurrent neural networks.