  Decision analytics commonly focuses on the text mining of financial news
sources in order to provide managerial decision support and to predict stock
market movements. Existing predictive frameworks almost exclusively apply
traditional machine learning methods, whereas recent research indicates that
traditional machine learning methods are not sufficiently capable of extracting
suitable features and capturing the non-linear nature of complex tasks. As a
remedy, novel deep learning models aim to overcome this issue by extending
traditional neural network models with additional hidden layers. Indeed, deep
learning has been shown to outperform traditional methods in terms of
predictive performance. In this paper, we adapt the novel deep learning
technique to financial decision support. In this instance, we aim to predict
the direction of stock movements following financial disclosures. As a result,
we show how deep learning can outperform the accuracy of random forests as a
benchmark for machine learning by 5.66%.
