Deep learning in exchange markets
Vitor Miguel Ribeiro,
Fernando Lobo Pereira and
Ana Paula Rocha
Information Economics and Policy, 2019, vol. 47, issue C, 38-51
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.
Keywords: Deep learning; Betting exchange; Market depth; Classification (search for similar items in EconPapers)
JEL-codes: G17 D10 L10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:iepoli:v:47:y:2019:i:c:p:38-51
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