Improving Decision Analytics with Deep Learning: the Case of Financial Disclosures

By Stefan Feuerriegel, Ralph Fehrer. 2015

45 citations recorded by the source corpus.

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%.

Subfields: Finance, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

1. Mathias Kraus
2. Joscha Märkle-Huss
3. Helmut Prendinger
4. Stefan Feuerriegel
5. Longbing Cao
6. Dogu Araci

In Management Science and Operations Research

1. Mathias Kraus
2. Helmut Prendinger
3. Stefan Feuerriegel
4. Longbing Cao
5. Dogu Araci

Citations in the corpus

• Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “Closest to our research is the application of a recursive autoencoder to the headlines of approximately 6,500 financial disclosures [12].”
• Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures (2017, background): “Feuerriegel and Fehrer 2016 [42] Word order Recurrent relationship between words Implicit feature selection Random forest Headlines only vs.”