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Neural networks in financial trading

  • Georgios SermpinisEmail author
  • Andreas Karathanasopoulos
  • Rafael Rosillo
  • David de la Fuente
S.I.: Networks and Risk Management
  • 40 Downloads

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.

Keywords

Neural networks Forecasting Trading Multiple hypothesis testing 

Notes

References

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Georgios Sermpinis
    • 1
    Email author
  • Andreas Karathanasopoulos
    • 2
  • Rafael Rosillo
    • 3
  • David de la Fuente
    • 4
  1. 1.University of Glasgow Business SchoolUniversity of GlasgowGlasgowUK
  2. 2.Dubai Business SchoolUniversity of DubaiDubaiUAE
  3. 3.Business and Management DepartmentUniversity of OviedoGijónSpain
  4. 4.Business and Management DepartmentUniversity of OviedoOviedoSpain

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