Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix
Pp. 111-126 (16)
Cagdas Hakan Aladag and Hilal Guney
Stock exchanges forecasting is a popular research topic that is attracting
more and more attention from researchers and practitioners. Since it is a well-known
fact that stock exchanges time series include uncertainty, using conventional time
series methods can lead to misleading results. Therefore, a proper approach should be
employed for analysis according to the nature of the data. In the literature, fuzzy time
series models have been successfully used to forecast real world time series which
include vagueness. In order to handle uncertainty in stock exchanges, fuzzy time series
forecasting approach proposed by Tsaur  is utilized in this study. Fuzzy time series
forecasting model suggested by Tsaur  uses Markov chain transition matrix for
fuzzy inference. In the implementation, the fuzzy time series forecasting model is
applied to index 100 in stocks and bonds exchange market of İstanbul in order to show
the performance of the model. It is seen that the forecasting model gives accurate
forecasts for the data.
Fuzzy inference, Fuzzy relations, Fuzzy time series, Markov chain
transition matrix, Number of fuzzy set, Stock exchanges.
Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada.