A Multiple-Criteria Approach for Forecasting Stock Price Direction : Nonlinear Probability Models with Application in S & P 500 Index

This paper presents a forecasting approach, in which stock price direction in the next day can be predicted based on nonlinear probability models and technical indicators. The proposed method incorporates various indicators into Logit, Probit, and Extreme Value models permitting a decision maker to forecast the direction of stock movements more efficiently. The utilized indicators include Moving Average, Momentum, Relative Strength Index, Price Channel Index, Moving Average Convergence Divergence and Random Fluctuation. The application of the proposed approach is investigated in a cease problem where real-word data of Standard & Poor's 500 is utilized to forecast the stock market. The results reveal that Extreme Value model has a better performance in comparison with the other models.

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