Abstract

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 these 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 classical neural networks with additional hidden layers. Indeed, deep learning often provides a viable approach to achieve a high predictive performance. In this paper, we adapt the novel deep learning technique to financial decision support, where we aim to predict the direction of stock movements following financial disclosures. As a result, our paper shows how deep learning can outperform the accuracy of benchmarks for machine learning by 5.66 %.

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