FORECASTING CLOSING RETURNS OF BORSA ISTANBUL INDEX WITH MARKOV CHAIN PROCESS OF THE FUZZY STATES
Purpose- The estimation regarding to the exact daily price of the stock market index has always been a difficult task in the business sector. Therefore, there are numerous research studies carried out to predict the direction of stock price index movement. Methodology- Classical Markov chain model (MC) is commonly used for this prediction and it gives valuable signals about the movements of the closing returns of the stock market index. In this paper, we propose Markov Chain Model with Fuzzy States (MCFS) to predict the closing returns of Borsa Istanbul (BIST 100) index using triangular fuzzy numbers. We apply this method to hold the information while system moves between the extreme values of the states. Findings- With this study, we show that the use of MCFS for the selected period provides a higher forecasting accuracy to the investors compared to MC model. Conclusion- Markov chains of the fuzzy states defines a stochastic system more precisely than the classical Markov chains and it gives more sensitive future prediction opportunities. It can be used for estimating returns of individual common stocks and also for the other investment instruments.
Keywords:
Stock return, fuzzy sets, conditional probability Markov Decision Process, Markov Chain with fuzzy states,
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- Avrachenkov K.E. & Sanchez E. 2000, "Fuzzy Markov chains", IPMU, Spain, pp. 1851-1856.
- Badge, J. 2012, "Forecasting of Indian Stock Market by Effective Macro- Economic Factors and Stochastic Model", Journal of Statistical and Econometric Methods, vol. 1 (2), pp. 39-51, ISSN: 2241-0384 (print), 2241-0376 (online) Sciencepress Ltd.
- Bellman, R. 1957, "A Markov Decision Process", Journal of Mathematics and Mechanics 6.
- Box G.E.P., Jenkins, G. M. 1976, "Time series analysis: forecasting and control", San Fransisco, CA: Holden-Day.
- Chiang W.C., Urban T. L. & Baldridge, G.W. 1996, "A neural network approach to mutual fund net asset value forecasting", Omega International Journal of Management Science, vol. 24 (2), pp. 205–215.
- Gupta A. & Dhingra B. 2012, "Stock Market Prediction Using Hidden Markov Models", Non-Student members, IEEE.
- Hassan, Md. R. & Nath, B. 2005, "Stock Market forecasting using Hidden Markov Model: A New Approach", Proceeding of the 5th international conference on intelligent Systems Design and Application 0-7695-2286-06/05, IEEE.
- Hassan, Md. R., Nath, B. & Kirley, M. 2006, "HMM based Fuzzy Model for Time Series Prediction", IEEE International Conference on Fuzzy Systems, pp. 2120-2126.
- Hassan, Md. R., Nath, B. & Kirley, M. 2007, "A fusion model of HMM, ANN and GA for stock market forecasting", Expert systems with Applications, pp. 171-180.
- Henry, M. K. M. 1993, "Causality of interest rate, exchange rate and stock prices at stock market open and close in Hong Kong", Asia Pacific Journal of Management, vol. 10 (2), pp. 123–143.
- Kim, K.J. & Han, I. 2000, "Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index", Expert Systems with Applications, vol.19, pp.125-132.
- Kruce, R., Buck- Emden, R. & Cordes, R. 1987, "Process or Power Considerations-An Application to Fuzzy Markov Chains", Fuzzy Sets and Systems, pp. 289-299.
- Kuranoa, M., Yasuda, M., Jakagami, J. & Yoshida, Y. 2006, "A Fuzzy Approach to Markov Decision Processes with Unceratin Transition Probabilities", Fuzzy Sets and Systems, 157, pp. 2674-2682.
- Pardo, M.J. & Fuente, D. 2010, "Fuzzy Markovian Decision Processes: Application to Queueing Systems", Computers and Mathematics with Applications, 60, pp. 2526-2535.
- Rabiner, L.R. 1993, "A tutorial on HMM and Selected Applications in Speech Recognition", In: [WL], proceedings of the IEEE, vol. 77 (2), pp. 267- 296.
- Rabiner, L.R., Juang, B. 1993, "Fundamentals of Speech Recognition", Prentice-Hall, Englewood Cliffs, NJ.
- Romahi Y. & Shen, Q. 2000, "Dynamic financial forecasting with automatically induced fuzzy associations", In Proceedings of the 9th international conference on fuzzy systems, pp. 493–498.
- Salzenstein, F., Collet, C., Lecam, S. & Hatt, M. 2007, "Non-Stationary Fuzzy Markov Chain", Pattern Recognition Letters, 28, pp. 2201-2208.
- Sanchez, E. 1976, "Resolution of Composite Fuzzy Relation Equations", Information and Control, 30, pp. 38-48.
- Stow’ınski, R. (ed.) 1998, "Fuzzy Sets in Decision Analysis", Operation Research and Statistics, Kluwer Academic Publishers.
- Symeonaki, M.A. & Stamou, G.B. 2004, "Theory of Markov Systems with Fuzzy States", Fuzzy Sets and Systems, 143, pp. 427-445.
- Thomason M. 1977, "Convergence of Powers of a Fuzzy Matrix", Journal of Mathematical Analysis and Applications, 57, pp. 476-480.
- Vajargah, B.F. & Gharehdaghi, M. 2012, "Ergodicity of fuzzy Makov chains based on simulation using Halton sequences", The Journal of Mathematics and Computer Science, vol. 4, no. 3, pp. 380-385.
- White, H., 1988, "Economic prediction using neural networks: the case of IBM daily stock returns", Department of Economics, University of California, San Diego.
- White, H. 1988, "Economic prediction using neural networks: the case of IBM daily stock returns", In Proceedings of the second IEEE annual conference on neural networks, II, pp. 451–458.
- White, H. 1989, "Learning in artificial neural networks: a statistical perspective", Neural Computation, vol. 1, pp. 425–464.
- Yoshida, Y. 1994, "Markov chains with a transition possibility measure and fuzzy dynamic programming", Fuzzy Sets and Systems, 66, pp. 39-57.
- Zadeh, L.A. 1965, "Fuzzy Sets", Information Control, 8, pp. 338-353.
- Zhou, X., Tang, Y., Xie, Y., Li, Y. & Zhang, Y. 2013, "A Fuzzy Probability- based Markov Chain Model for Electric Power Demand Forecasting of Beijing", Energy and Power Engineering, China, pp. 488-492.
- Borsa Istanbul, http://www.borsaistanbul.com
- Yayın Aralığı: Yılda 4 Sayı
- Başlangıç: 2014
- Yayıncı: PressAcademia