This article provides a portfolio optimization approach that takes into account extreme events. By merging a (downside only) panic copula with the empirical marginal distributions, panic-awareness is attained for the optimization process. This approach includes the likelihood of highly...

This paper employs a deep learning approach for linking stock market fundamentals to trading signals via neural networks. With an average accuracy of ~54% the model predicts whether markets go up or down on the subsequent trading day. Coupling the prediction to a binary long/cash strategy yields...