Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures
This work addresses decision analytics for financial managers and investors, but it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the problem of predicting stock movements after financial disclosures by applying deep learning, achieving a 5.66% improvement in accuracy over random forests as a benchmark.
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%.