Deep learning in exchange markets

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Rui Gonçalves (Author) Other; Vitor Miguel Ribeiro (Author) Other; Fernando Lobo Pereira (Author) FEUP; Ana Paula Rocha (Author) FEUP

Journal

Title: Information Economics and Policy

Vol. 47 No. SI

Pages: 38-51

ISSN: 0167-6245

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00Q-NVP

DOI: 10.1016/j.infoecopol.2019.05.002

Abstract (EN): We present the implementation of a short-term forecasting system of price movements in exchange markets using market depth data and a systematic procedure to enable a fully automated trading system. Three types of Deep Learning (DL) Neural Network (NN) methodologies are trained and tested: Deep NN Classifier (DNNC), Long Short-Term Memory (LSTM) and Convolutional NN (CNN). Although the LSTM is more suitable for multivariate time series analysis from a theoretical point of view, test results indicate that the CNN has on average the best predictive power in the case study under analysis, which is the UK to Win Horse Racing market during pre-live stage in the world's most relevant betting exchange. Implications from the generalized use of automated trading systems in betting exchange markets are discussed.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14