Deep learning in exchange markets

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

Rui Gonçalves (Autor) Outra; Vitor Miguel Ribeiro (Autor) Outra; Fernando Lobo Pereira (Autor) FEUP; Ana Paula Rocha (Autor) FEUP

Revista

Título: Information Economics and Policy

Vol. 47 Nº SI

Páginas: 38-51

ISSN: 0167-6245

Editora: Elsevier

Indexação

ISI Web of Knowledge - 7 Citações

Scopus - 8 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14