Abstract
In the study, a new dynamic fuzzy model is proposed in combination with support vector machine (SVM) to explore stock market dynamism. The fuzzy model integrates various factors with influential degree as the input variables, and the genetic algorithm (GA) adjusts the influential degree of each input variable dynamically. SVM then serves to predict stock market dynamism in the next phase. In the meanwhile, the multiperiod experiment method is designed to simulate the volatility of stock market. Then, we compare it with other methods. The model from the study does generate better results than others.
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References
Black, A.J., Mcmillan, D.G.: Non-linear Predictability of Value and Growth Stocks and Economic Activity. Journal of Business Finance & Accounting 31, 439–474 (2004)
Lo, A.: The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management 30, 15–44 (2004)
Armano, G., Murru, A., Roli, F.: Stock Market Prediction by A Mixture of Genetic-Neural Experts. International Journal of Pattern Recognition and Artificial Intelligence 16, 501–526 (2002)
Matilla-Garcia, G., Arguellu, C.: A Hybrid Approach Based on Neural Networks and Genetic Algorithm to the Study of Profitability in the Spanish Stock Market. Applied Economics Letter 12, 303–308 (2005)
Davis, L.: Genetic Algorithms and Financial Applications. In: Deboeck, G.J. (ed.) Trading on the Edge, pp. 133–147. Wiley, New York (1994)
Schwert, G.W.: Why Does Stock Arket Volatility Change Over Time. The Journal of Finance 44, 1115–1167 (1989)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines. Cambridge University, New York (2000)
Dickinson, D.G.: Stock Market Integration and Macroeconomic Fundamentals: An Empirical Analysis. Applied Financial Economics 10, 261–276 (2000)
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Chiu, DY., Chen, PJ. (2007). Applying Dynamic Fuzzy Model in Combination with Support Vector Machine to Explore Stock Market Dynamism. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_28
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DOI: https://doi.org/10.1007/978-3-540-71629-7_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71590-0
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