DOI:
https://doi.org/10.14483/23448393.3838Published:
2011-12-18Issue:
Vol. 16 No. 2 (2011): July - DecemberSection:
ArticleRevisión del Estado del Arte en Métodos de Redes Neuronales, Máquinas de Kernel y Computación Evolutiva para Predicción de Precios Financieros
Computational Models of Financial Price Prediction: A Survey of Neural Networks, Kernel Machines and Evolutionary Computation Approaches
Keywords:
Stock price prediction, machine learning, artificial neural networks, kernel machines and evolutionary methods. (en).Keywords:
Predicción de precios en la bolsa de valores, aprendizaje computacional, redes neuronales artificiales, Máquinas de Vectores de Soporte, métodos evolutivos. (es).Downloads
Abstract (es)
El siguiente artículo revisa algunos de los trabajos de investigación mas representativos relacionados con aprendizaje computacional aplicado al problema de predicción de tipos de cambio y precios de acciones. El artículo esta organizado de la siguiente forma: La primera sección se concentra en contextualizar definiciones relevantes y la importancia del problema de predicción en el mercado de acciones y de tasa de cambio. La segunda sección contiene la revisión de modelos de aprendizaje computacional para predicción de precios financieros enfocándose en tres subareas: Redes Neuronales, SVM y métodos evolutivos. La tercera sección presenta las conclusiones.
Abstract (en)
A review of the representative models of machine learning research applied to the foreign exchange rate and stock price prediction problem is conducted. The article is organized as follows: The first section provides a context on the definitions and importance of foreign exchange rate and stock markets. The second section reviews machine learning models for financial prediction focusing on neural networks, SVM and evolutionary methods. Lastly, the third section draws some conclusions.References
A. Lendasse, E. de Bodt, V. Wertz, and M. Verleysen (2000). Non-Linear Financial Time Series Forecasting - Application to the Bel 20 Stock Market Index. E.J.E.S.S., 14(1):81–91.
Allen, F. andKarjalainen, R. (1999). Using Genetic Algorithms to Find Technical Trading Rules (Revision of 20-93). Journal of Financial Economics, (51):245–271.
Allen, H. and Taylor, M. (1990). Charts, Noise and Fundamentals in the London Foreign Exchange Market. Economic Journal, (100(400)):49–59.
Armano, G., Marchesi, M., and Murru, A. (2005).A Hybrid Genetic- Neural Architecture for Stock Indexes Forecasting. Information Sciences, 170(1):3 – 33. Computational Intelligence in Economics and Finance.
Baba, N. and Kozaki, M. (1992). An Intelligent forecasting system of stock price using neural networks. In Neural Networks, International Joint Conference, 1992.
Becker, L. A. and Seshadri, M. (2003). Gp-Evolved Technical Trading Rules Can Outperform Buy and Hold.
Brabazon, A. and ONeill, M. (2004). Evolving Technical Trading Rules for Spot Foreign-Exchange Markets Using Grammatical Evolution. Computational Management Science, 1(3):311–327.
Brabazon, A. and O’Neill, M. (2006). Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
Brabazon, T. and O’Neill, M. (2002). Trading Foreign Exchange Markets Using Evolutionary Automatic Programming. In Barry, A. M., editor, GECCO 2002: Proceedings of the Bird of a Feather Workshops, Genetic and Evolutionary Computation Conference, pages 133–136, New York. AAAI.
Cao, L. and Tay, E. (2003). Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting.IEEE transactions on neural network, 14(6).
Cao, L. and Tay, F. E. (2001). Financial Forecasting Using Support Vector Machines.Neural Computing & Applications, 10:184–192.10.1007/s005210170010.
Chalup, S. K. and Mitschele, A. (2008). Kernel Methods in Finance. In Bernus, P., editor, Handbook on Information Technology in Finance, International Handbooks on Information Systems, pages 655–687. Springer Berlin Heidelberg.
Chen, S.-H. and Yeh, C.-H. (1996). All Working Papers 011, University of California at Los Angeles, Center for Computable Economics.
Chen, Y. (2005). Stock Index Modeling Using EDA Based Local Linear Wavelet Neural Network. In: IEEE, In Proceedings of International Conference on Neural Networks and Brain, volume 3, pages 1646 – 1650.
Deboeck, G. J. (1994). Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets. John Wiley&Sons, Inc., New York, NY, USA.
Fernandez-Rodriguez, F., Gonzalez-Martel*, C., and Sosvilla-Rivero, S. (2000). On the Profitability of Technical Trading Rules Based on Artificial Neural Networks: Evidence from the Madrid Stock Market. Working Papers 99-07, FEDEA.
Fletcher, T., Hussain, Z., and Shawe-Taylor, J. (2011).Multiple Kernel Learning on the Limit Order Book. Quantitative Finance -To be published, 00(00):00–00.
Frankel, J. and Froot, K. (1987a). Short-Term and Long-Term Expectations of the Yen/Dollar Exchange Rate: Evidence from Survey Data. Journal of the Japanese and International Economies, (1):249–274.
Frankel, J. and Froot, K. (1987b). Using Survey Data to Test Standard Propositions Regarding Exchange Rate Expectations. American Economic Review, (77):133–153.
Frankel, J. andFroot, K. (1990). Chartists, Fundamentalists and the Demand for Dollars. In: Courakis, A.S., Taylor, M.P. (Eds.), Private Behavior and Government Policy in Interdependent Economies, pages 73–126.
Frankel, J.A. andFroot, K. (1990). The Rationality of the Foreign Ex- change Rate. Chartists, Fundamentalists and Trading in the Foreign Ex- change Market. American Economic Review, (80):181–185.
Franses, P. andHomelen, P. (1998). On Forecasting Exchange Rates using Neural Networks. Applied Financial Economics, 8(6):589–596.
Grudnitski, G. and Osburn, L. (1993). Forecasting s&p and Gold Futures Prices: An Application of Neural Networks. Journal of Futures Markets, 13(6):631–643.
Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edition. Prentice Hall.
Hu, M. Y., Zhang, G. P., Jiang, C. X., and Patuwo, B. E. (1999). A Cross Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting. Decision Sciences, 30(1):197–216.
Huang, W., Nakamori, Y., and Wang, S.-Y. (2005). Forecasting Stock Market Movement Direction with Support Vector Machine. Computers & Operations Research, 32(10):2513 – 2522. Applications of Neural Networks.
Ince, H. (2000). Support Vector Machine for Regression and Applications to Financial Forecasting. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN’00)-Volume 6 - Volume 6, pages 6348–, Washington, DC, USA. IEEE Computer Society.
Ince, H. and Trafalis, T. B. (2006). A Hybrid Model for Exchange Rate Prediction. Decision Support Systems, 42(2):1054 – 1062.
Kim, K.-j. (2003). Financial Time Series Forecasting Using Support Vector Machines.Neuro Computing, 5:307–319.
Koza, J. (1992). Genetic Programming. Masachussets, MIT.
Lee, M.-C. (2009). Using Support Vector Machine with a Hybrid Feature Selection Method to the Stock Trend Prediction. Expert Systems with Applications, 36(8):10896 – 10904.
Li, J. and Tsang, E. P. K. (1999). Improving Technical Analysis Predictions: An Application of Genetic Programming. In Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, pages 108–112. AAAI Press.
Lo, A. and MacKinlay, A. (1996). Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test. Review of Financial studies, (1):41–66.
Lohpetch, D. and Corne, D. (2010). Outperforming Buy-and-Hold with Evolved Technical Trading Rules: Daily, Weekly and Monthly Trading. In Di Chio, C., Brabazon, A., and Caro, D., editors, Applications of Evolutionary Computation, volume 6025 of Lecture Notes in Computer Science, pages 171–181. Springer Berlin / Heidelberg.
Lu, C.-J., Lee, T.-S., and Chiu, C.-C. (2009). Financial Time Series Forecasting Using Independent Component Analysis and Support Vector Regression. Decision Support Systems, 47(2):115 – 125.
Olson, D. and Mossman, C. (2003). Neural Network Forecasts of Canadian Stock Returns using Accounting Ratios. International Journal of Forecasting, 19(3):453 – 465.
O’Neill, M., Brabazon, A., Ryan, C., and Collins, J. J. (2001). Evolving Market Index Trading Rules Using Grammatical Evolution. In Proceedings of the EvoWorkshops on Applications of Evolutionary Computing, pages 343–352, London, UK, UK. Springer-Verlag.
Potvin, J.-Y., Soriano, P., and Vall´ee, M. (2004). Generating Trading Rules on the Stock Markets with Genetic Programming. Comput. Oper. Res., 31:1033–1047.
Roy and Rada (2008). Expert Systems and Evolutionary Computing for Financial Investing: A Review. Expert Systems with Applications, 34(4):2232 – 2240.
Sansom, D. C., Downs, T., and Saha, T. K. (2002). Evaluation of Support Vector Machine Based Forecasting Tool in Electricity Price Forecasting for Australian National Electricity Market Participants. In Proceedings of the Australasian Universities Power Engineering Conference. Australasian Universities Power Engineering Conference, Melbourne, Australia, pages (1–6). A. Zahedi.
Tan, C. L. and Yao, T. (2000). A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex. Neurocomputing, 34:79–98.
Tay, E. and Cao, L. (2002). Descending Support Vector Machines for Financial Time Series Forecasting. Neural Processing Letters, (15).
Tay, F. E. H. and Cao, L. J. (2001). Improved Financial Time Series Forecasting by Combining Support Vector Machines with Self-Organizing Feature Map.Intell. Data Anal., 5:339–354.
Taylor, M. and Allen, H. (1992). The Use of Technical Analysis in the Foreign Exchange Market. Journal of International Money and Finance, (11):304–314.
Trippi, R. and DeSieno, D. (1992). Trading Equity Index Futures with a Neural Network. Journal of Portfolio Management, 19(1):27–53.
Versace, M., Bhatt, R., Hinds, O., and Shiffer, M. (2004). Predicting the Exchange Traded Fund DIA with a Combination of Genetic Algorithms and Neural Networks. Expert Systems with Applications.
White, H. (1988). Economic Prediction Using Neural Networks: The Case of IBM Daily Stock Returns. In Neural Networks, 1988. IEEE International Conference on, pages 451–458 vol.2.
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