• USING BEST REPLACEMENT OPTIMIZATION (BRO) TECHNIQUES TO PREDICTING NON-LINEAR DYNAMIC SYSTEM: EVIDENCE FROM S&P CNX NIFTY FIFTY STOCK INDEX

Dr. T. CHITRA KALARANI*, S. INDRAKALA**

Abstract


Stock market analysis is one of the most important and hard problems in finance analysis field. Recently, the usage of intelligent systems for stock market prediction has been widely established. In this study, we aim to design a mathematical model for Stock price prediction which can provide an accurate direction for financial firms and private investors. With knowledge of the movement of stock price, an investor can make profitable decision and reduce risk return trade-off. In this paper, the Best Replacement Optimization algorithm is proposed, which is used for the S&P CNX NIFTY stock index analysis. The results provide better forecasting accuracy than previous methods.


Keywords


Fuzzy Time Series, Particle Swarm Optimization, Prediction, Stock Index Price.

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