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Article Content:-
Abstract
There is huge amount of data with complex uncertainty in stock market. Meanwhile. Efficient stock prediction is important in financial investment. Today, financial data analysis is becoming increasingly important in the stock market. As per companies gather more and more data from daily operations, they expect to extract useful information from existing collected data to help make reasonable decisions for different customer requirements. But this data values keeps on fluctuating day by day. So it is very difficult to predict the future value of the market. Although there are various techniques implemented for the prediction of stock market values, but the predicted values are not very accurate and error rate is more. The present paper introduces the best replacement Optimization technique to develop an efficient forecasting model for prediction of niftyfifty stock index. Results presented in this paper show that the proposed model has fast convergencespeed, and it also achieves better accuracy than compared techniques in most cases.
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