Explaining Deep Learning Forecasts

We already covered in a previous post, how important it is to deal with uncertainty in financial Deep Learning forecasts. In this post, we'll attempt a first introduction on how we deal with explainability. Neural networks have been applied to various tasks including stock price prediction. Although highly successfully, these models are frequently treated as black boxes. In most cases we know that the performance on the test data is satisfying, but we do not know why the model came up with a specific output.