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Neural networks in financial trading

Author

Listed:
  • Georgios Sermpinis

    (University of Glasgow)

  • Andreas Karathanasopoulos

    (University of Dubai)

  • Rafael Rosillo

    (University of Oviedo)

  • David Fuente

    (University of Oviedo)

Abstract

In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in forecasting and trading the DJIA, NASDAQ 100 and the NIKKEI 225 stock indices. The statistical significance of the forecasts is examined through the False Discovery Ratio of Bajgrowicz and Scaillet (J Financ Econ 106(3):473–491, 2012). Two financial everages, based on the levels of financial stress and the financial volatility respectively, are also applied. In terms of the results, we note that RNN have the higher percentage of significant models and present the stronger profitability compared to their Neural Network counterparts. The financial leverages doubles the trading performance of our models.

Suggested Citation

  • Georgios Sermpinis & Andreas Karathanasopoulos & Rafael Rosillo & David Fuente, 2021. "Neural networks in financial trading," Annals of Operations Research, Springer, vol. 297(1), pages 293-308, February.
    • Handle: RePEc:spr:annopr:v:297:y:2021:i:1:d:10.1007_s10479-019-03144-y
    DOI: 10.1007/s10479-019-03144-y
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