Forecasting stock market return using artificial neural networks

Abstract

Stock market’s return prediction is an important concept in emergent markets like Turkey and Brazil. In this thesis, I used artificial neural networks architecture to model and predict stock markets. Istanbul stock exchange indices National-100 and National-30 are used for domestic market, Brazilian stock exchange index, BVSP, is used as the international market. Input space is divided into clusters with statistical clustering technique Expectation Maximization. ANN’s structure Mixture of Experts is used and local experts are assigned to each cluster. While local experts are learning their region of interest, in parallel, gating experts combine the outputs of them to model overall structure. Besides, future returns are predicted based on patterns obtained from past trainings. In financial time series modeling using ANN, I used past returns in simulations. Since we know two investigated markets are highly volatile and chaotic, the volatility factor is added to the analysis as well. Volatility calculated from RiskMetrics™ [30] is also included in the models to capture different dynamic features of the data. Results of our simulations are evaluated by using previously defined and widely accepted performance measures. Another interesting result is gained during our simulations; two countries of different macroeconomic structures show similarities. Interaction between Turkish and Brazilian stock markets is not surprising; during the financial liberalization of the 1980-1990s, Turkey imported many constitutional laws from Latin American economies especially from Brazil.