Neural networks in financial trading

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


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.


Neural networks Forecasting Trading Multiple hypothesis testing 



  1. Adeodato, P., Arnaud, A., Vasconcelos, G., Cunha, R., & Monteiro, D. (2011). MLP ensembles improve long term prediction accuracy over single networks. International Journal of Forecasting, 27(3), 661–671.Google Scholar
  2. Andreou, P. C., Charalambous, C., & Martzoukos, S. H. (2008). Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters. European Journal of Operational Research, 185(3), 1415–1433.Google Scholar
  3. Bagheri, A., Peyhani, H. M., & Akbari, M. (2014). Financial forecasting using ANFIS networks with quantum-behaved particle swarm optimization. Expert Systems with Applications, 41(14), 6235–6250.Google Scholar
  4. Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165–1195.Google Scholar
  5. Bajgrowicz, P., & Scaillet, O. (2012). Technical trading revisited: False discoveries, persistence tests, and transaction costs. Journal of Financial Economics, 106(3), 473–491.Google Scholar
  6. Bekiros, S. D., & Georgoutsos, D. A. (2008). Direction-of-change forecasting using a volatility-based recurrent neural network. Journal of Forecasting, 27(5), 407–417.Google Scholar
  7. Bertolini, L. (2010). Trading foreign exchange carry portfolios. PhD thesis, Cass Business School, City University London.Google Scholar
  8. Boston Consulting Group. (2016). The factory of the future: The ghost in the machine, Boston Consulting Group. Online report.Google Scholar
  9. Broomhead, D. S., & Lowe, D. (1988). Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal Signals and Radar Establishment Malvern.Google Scholar
  10. Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194–211.Google Scholar
  11. Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans—and where they can’t (yet). Mckinsey Quarterly, 7.Google Scholar
  12. Donaldson, R. G., & Kamstra, M. (1996). Forecast combining with neural networks. Journal of Forecasting, 15(1), 49–61.Google Scholar
  13. Dunis, C., Laws, J., & Sermpinis, G. (2010). Modelling and trading the EUR/USD exchange rate at the ECB fixing. The European Journal of Finance, 16(6), 541–560.Google Scholar
  14. Dunis, C., & Miao, J. (2006). Volatility filters for asset management: An application to managed futures. Journal of Asset Management, 7(3–4), 179–189.Google Scholar
  15. Dunis, C. L., Laws, J., & Sermpinis, G. (2011). Higher order and recurrent neural architectures for trading the EUR/USD exchange rate. Quantitative Finance, 11(4), 615–629.Google Scholar
  16. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.Google Scholar
  17. Fulcher, J., Zhang, M., & Xu, S. (2006). The application of higher-order neural networks to financial time series. In J. Kamruzzaman, R. Begg, & R. Sarker (Eds.), Artificial neural networks in finance and manufacturing. Hershey, PA: Idea Group, London.Google Scholar
  18. Giles, C. L., & Maxwell, T. (1987). Learning, invariance, and generalization in high-order neural networks. Applied Optics, 26(23), 4972–4978.Google Scholar
  19. Guresen, E., Kayakutlu, G., & Daim, T. U. (2011). Using artificial neural network models in stock market index prediction. Expert Systems with Applications, 38(8), 10389–10397.Google Scholar
  20. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366.Google Scholar
  21. Kaastra, I., & Boyd, M. (1996). Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236.Google Scholar
  22. Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702.Google Scholar
  23. Lisboa, P. J. G., & Vellido, A. (2000). Business applications of neural networks, vii–xxii. In P. J. G. Lisboa, B. Edisbury, & A. Vellido (Eds.), Business applications of neural networks: The state-of-the-art of real-world applications. Singapore: World Scientific.Google Scholar
  24. Lo, A. W. (2004). The adaptive markets hypothesis. Journal of Portfolio Management, 30(5), 15–29.Google Scholar
  25. Maren, A. J., Harston, C. T., & Pap, R. M. (2014). Handbook of neural computing applications. Cambridge: Academic Press.Google Scholar
  26. Matías, J. M., & Reboredo, J. C. (2012). Forecasting performance of nonlinear models for intraday stock returns. Journal of Forecasting, 31(2), 172–188.Google Scholar
  27. Orr, M. J. L. (1996). Introduction to radial basis function networks. Centre for Cognitive Science. Scothland: Edinburgh University.
  28. Panda, C., & Narasimhan, V. (2007). Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling, 29(2), 227–236.Google Scholar
  29. Park, J., & Sandberg, I. W. (1991). Universal approximation using radial-basis-function networks. Neural Computation, 3(2), 246–257.Google Scholar
  30. Politis, D. N., & Romano, J. P. (1994). The stationary bootstrap. Journal of the American Statistical Association, 89(428), 1303–1313.Google Scholar
  31. Romano, J. P., & Wolf, M. (2007). Control of generalized error rates in multiple testing. The Annals of Statistics, 35(4), 1378–1408.Google Scholar
  32. Sermpinis, G., Stasinakis, C., & Dunis, C. (2014). Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects. Journal of International Financial Markets, Institutions and Money, 30, 21–54.Google Scholar
  33. Sermpinis, G., Theofilatos, K., Karathanasopoulos, A., Georgopoulos, E. F., & Dunis, C. (2013). Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and particle swarm optimization. European Journal of Operational Research, 225(3), 528–540.Google Scholar
  34. Shapiro, A. F. (2000). A Hitchhiker’s guide to the techniques of adaptive nonlinear models. Insurance, Mathematics and Economics, 26(2), 119–132.Google Scholar
  35. Storey, J. D. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3), 479–498.Google Scholar
  36. Tenti, P. (1996). Forecasting foreign exchange rates using recurrent neural networks. Applied Artificial Intelligence, 10(6), 567–581.Google Scholar
  37. White, H. (2000). A reality check for data snooping. Econometrica, 68(5), 1097–1126.Google Scholar
  38. Yang, J., Cabrera, J., & Wang, T. (2010). Nonlinearity, data-snooping, and stock index ETF return predictability. European Journal of Operational Research, 200(2), 498–507.Google Scholar
  39. Yang, J., Su, X., & Kolari, J. W. (2008). Do Euro exchange rates follow a martingale? Some out-of-sample evidence. Journal of Banking & Finance, 32(5), 729–740.Google Scholar
  40. Yingwei, L., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Transactions on Neural Networks, 9(2), 308–318.Google Scholar
  41. Yu, H., Xie, T., Paszczynski, S., & Wilamowski, B. M. (2011). Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics, 58(12), 5438–5450.Google Scholar
  42. Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62.Google Scholar
  43. Zhang, M. (2009). Artificial higher order neural networks for economics and business. Hershey: IGI Global.Google Scholar

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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