Research Article
BibTex RIS Cite
Year 2017, , 15 - 24, 30.03.2017
https://doi.org/10.17261/Pressacademia.2017.362

Abstract

References

  • 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

FORECASTING CLOSING RETURNS OF BORSA ISTANBUL INDEX WITH MARKOV CHAIN PROCESS OF THE FUZZY STATES

Year 2017, , 15 - 24, 30.03.2017
https://doi.org/10.17261/Pressacademia.2017.362

Abstract

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.

References

  • 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
There are 30 citations in total.

Details

Journal Section Articles
Authors

Ersin Kiral

Berna Uzun This is me

Publication Date March 30, 2017
Published in Issue Year 2017

Cite

APA Kiral, E., & Uzun, B. (2017). FORECASTING CLOSING RETURNS OF BORSA ISTANBUL INDEX WITH MARKOV CHAIN PROCESS OF THE FUZZY STATES. Journal of Economics Finance and Accounting, 4(1), 15-24. https://doi.org/10.17261/Pressacademia.2017.362

Journal of Economics, Finance and Accounting (JEFA) is a scientific, academic, double blind peer-reviewed, quarterly and open-access online journal. The journal publishes four issues a year. The issuing months are March, June, September and December. The publication languages of the Journal are English and Turkish. JEFA aims to provide a research source for all practitioners, policy makers, professionals and researchers working in the area of economics, finance, accounting and auditing. The editor in chief of JEFA invites all manuscripts that cover theoretical and/or applied researches on topics related to the interest areas of the Journal. JEFA publishes academic research studies only. JEFA charges no submission or publication fee.

Ethics Policy - JEFA applies the standards of Committee on Publication Ethics (COPE). JEFA is committed to the academic community ensuring ethics and quality of manuscripts in publications. Plagiarism is strictly forbidden and the manuscripts found to be plagiarized will not be accepted or if published will be removed from the publication. Authors must certify that their manuscripts are their original work. Plagiarism, duplicate, data fabrication and redundant publications are forbidden. The manuscripts are subject to plagiarism check by iThenticate or similar. All manuscript submissions must provide a similarity report (up to 15% excluding quotes, bibliography, abstract and method).

Open Access - All research articles published in PressAcademia Journals are fully open access; immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Open access is a property of individual works, not necessarily journals or publishers. Community standards, rather than copyright law, will continue to provide the mechanism for enforcement of proper attribution and responsible use of the published work, as they do now.