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Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects

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  • Sermpinis, Georgios
  • Stasinakis, Charalampos
  • Dunis, Christian

Abstract

The motivation of this paper is 3-fold. Firstly, we apply a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN) and a Psi-Sigma Network (PSN) architecture in a forecasting and trading exercise on the EUR/USD, EUR/GBP and EUR/CHF exchange rates and explore the utility of Kalman Filter, Genetic Programming (GP) and Support Vector Regression (SVR) algorithms as forecasting combination techniques. Secondly, we introduce a hybrid leverage factor based on volatility forecasts and market shocks and study if its application improves the trading performance of our models. Thirdly, we introduce a specialized loss function for Neural Networks (NNs) in financial applications. In terms of our results, the PSN from the individual forecasts and the SVR from our forecast combination techniques outperform their benchmarks in statistical accuracy and trading efficiency. We also note that our trading strategy is successful, as it increased the trading performance of most of our models, while our NNs loss function seems promising.

Suggested Citation

  • Sermpinis, Georgios & Stasinakis, Charalampos & Dunis, Christian, 2014. "Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 30(C), pages 21-54.
    • Handle: RePEc:eee:intfin:v:30:y:2014:i:c:p:21-54
    DOI: 10.1016/j.intfin.2014.01.006
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    1. Lee, Chun I. & Mathur, Ike, 1996. "Trading rule profits in european currency spot cross-rates," Journal of Banking & Finance, Elsevier, vol. 20(5), pages 949-962, June.
    2. Grossmann, Axel & McMillan, David G., 2010. "Forecasting exchange rates: Non-linear adjustment and time-varying equilibrium," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(4), pages 436-450, October.
    3. Bacchetta, Philippe & van Wincoop, Eric, 2013. "On the unstable relationship between exchange rates and macroeconomic fundamentals," Journal of International Economics, Elsevier, vol. 91(1), pages 18-26.
    4. de Menezes, Lilian M. & Nikolaev, Nikolay Y., 2006. "Forecasting with genetically programmed polynomial neural networks," International Journal of Forecasting, Elsevier, vol. 22(2), pages 249-265.
    5. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688.
    6. Richard Meese & Kenneth Rogoff, 1983. "The Out-of-Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification?," NBER Chapters, in: Exchange Rates and International Macroeconomics, pages 67-112, National Bureau of Economic Research, Inc.
    7. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    8. Charles, Amelie & Darne, Olivier, 2006. "Large shocks and the September 11th terrorist attacks on international stock markets," Economic Modelling, Elsevier, vol. 23(4), pages 683-698, July.
    9. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    10. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    11. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    12. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    13. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    14. Rozeff, Michael S. & Kinney, William Jr., 1976. "Capital market seasonality: The case of stock returns," Journal of Financial Economics, Elsevier, vol. 3(4), pages 379-402, October.
    15. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    16. Dueker, Michael & Neely, Christopher J., 2007. "Can Markov switching models predict excess foreign exchange returns?," Journal of Banking & Finance, Elsevier, vol. 31(2), pages 279-296, February.
    17. Harrald, P. & Kamstra, M., 1995. "Evolving Artificial Neural Networks to Combine Financial Forecasts," Discussion Papers dp95-04, Department of Economics, Simon Fraser University.
    18. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
    19. Shapiro, Arnold F., 2000. "A Hitchhiker's guide to the techniques of adaptive nonlinear models," Insurance: Mathematics and Economics, Elsevier, vol. 26(2-3), pages 119-132, May.
    20. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June.
    21. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    22. Pearce, Douglas K. & Solakoglu, M. Nihat, 2007. "Macroeconomic news and exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 17(4), pages 307-325, October.
    23. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    24. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Clara Vega, 2003. "Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange," American Economic Review, American Economic Association, vol. 93(1), pages 38-62, March.
    25. Rakesh K. Bissoondeeal & Jane M. Binner & Muddun Bhuruth & Alicia Gazely & Veemadevi P. Mootanah, 2008. "Forecasting exchange rates with linear and nonlinear models," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 10(4), pages 414-429.
    26. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    27. F. FernAndez-RodrIguez & S. Sosvilla-Rivero & J. Andrada-FElix, 2003. "Technical analysis in foreign exchange markets: evidence from the EMS," Applied Financial Economics, Taylor & Francis Journals, vol. 13(2), pages 113-122.
    28. Neely, Christopher J., 2002. "The temporal pattern of trading rule returns and exchange rate intervention: intervention does not generate technical trading profits," Journal of International Economics, Elsevier, vol. 58(1), pages 211-232, October.
    29. Yang, Yuhong, 2004. "Combining Forecasting Procedures: Some Theoretical Results," Econometric Theory, Cambridge University Press, vol. 20(1), pages 176-222, February.
    30. Hu, Michael Y. & Tsoukalas, Christos, 1999. "Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(4), pages 407-422, November.
    31. Veall, Michael R & Zimmermann, Klaus F, 1996. "Pseudo-R-[superscript 2] Measures for Some Common Limited Dependent Variable Models," Journal of Economic Surveys, Wiley Blackwell, vol. 10(3), pages 241-259, September.
    32. Evans, Kevin P. & Speight, Alan E.H., 2010. "Dynamic news effects in high frequency Euro exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(3), pages 238-258, July.
    33. Dunis, Christian L & Huang, Xuehuan, 2002. "Forecasting and Trading Currency Volatility: An Application of Recurrent Neural Regression and Model Combination," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 317-354, August.
    34. Wang, Hansheng & Li, Guodong & Jiang, Guohua, 2007. "Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 347-355, July.
    35. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    36. Tastan, Hüseyin, 2006. "Estimating time-varying conditional correlations between stock and foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 360(2), pages 445-458.
    37. Lin, Chien-Fu Jeff & Terasvirta, Timo, 1994. "Testing the constancy of regression parameters against continuous structural change," Journal of Econometrics, Elsevier, vol. 62(2), pages 211-228, June.
    38. Yeung Lewis Chan & James H. Stock & Mark W. Watson, 1999. "A dynamic factor model framework for forecast combination," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 91-121.
    39. Ming Yuan & Yi Lin, 2007. "On the non‐negative garrotte estimator," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 143-161, April.
    40. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    41. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    42. Christian Dunis & Jason Laws & Georgios Sermpinis, 2010. "Modelling and trading the EUR/USD exchange rate at the ECB fixing," The European Journal of Finance, Taylor & Francis Journals, vol. 16(6), pages 541-560.
    43. Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
    44. Julius Hemminki & Vesa Puttonen, 2008. "Fundamental indexation in Europe," Journal of Asset Management, Palgrave Macmillan, vol. 8(6), pages 401-405, February.
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    More about this item

    Keywords

    Forecast combinations; Kalman Filter; Genetic programming; Support vector regression; Trading strategies;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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