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

Year 2017, Volume: 4 Issue: 1, 15 - 24, 30.03.2017

Ersin Kiral Berna Uzun

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.

Keywords

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

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