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Deep learning in exchange markets

Author

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  • Gonçalves, Rui
  • Ribeiro, Vitor Miguel
  • Pereira, Fernando Lobo
  • Rocha, Ana Paula

Abstract

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.

Suggested Citation

  • Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo & Rocha, Ana Paula, 2019. "Deep learning in exchange markets," Information Economics and Policy, Elsevier, vol. 47(C), pages 38-51.
    • Handle: RePEc:eee:iepoli:v:47:y:2019:i:c:p:38-51
    DOI: 10.1016/j.infoecopol.2019.05.002
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    References listed on IDEAS

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    Cited by:

    1. Hwang, Yoontae & Park, Junpyo & Lee, Yongjae & Lim, Dong-Young, 2023. "Stop-loss adjusted labels for machine learning-based trading of risky assets," Finance Research Letters, Elsevier, vol. 58(PA).
    2. Catherine Rincón-Maya & Fernando Guevara-Carazas & Freddy Hernández-Barajas & Carmen Patino-Rodriguez & Olga Usuga-Manco, 2023. "Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology," Energies, MDPI, vol. 16(20), pages 1-20, October.
    3. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    4. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    5. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    6. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    7. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    8. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    9. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    10. Asit Kumar Das & Debahuti Mishra & Kaberi Das & Arup Kumar Mohanty & Mazin Abed Mohammed & Alaa S. Al-Waisy & Seifedine Kadry & Jungeun Kim, 2022. "A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market," Mathematics, MDPI, vol. 10(19), pages 1-23, October.
    11. Saeed Nosratabadi & Amirhosein Mosavi & Puhong Duan & Pedram Ghamisi & Ferdinand Filip & Shahab S. Band & Uwe Reuter & Joao Gama & Amir H. Gandomi, 2020. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods," Mathematics, MDPI, vol. 8(10), pages 1-25, October.

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    More about this item

    Keywords

    Deep learning; Betting exchange; Market depth; Classification;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • D10 - Microeconomics - - Household Behavior - - - General
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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