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

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.

Keywords

• Stock return,fuzzy sets,conditional probability,Markov Decision Process,Markov Chain with fuzzy states

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