STOCK INDEX DATA ANALYSIS USING RECURRENT NEURAL NETWORK MODEL WITH TRACKING SIGNAL APPROACH

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Predicting fluctuations in the stock market used to be a difficult task. Computer researchers now predict these natural declines with near-perfect accuracy by using machine learning algorithms. Many researchers are competing to develop new models to predict the closing stock market index. This research study proposes a recurrent neural network with a tracking signal approach for the prediction of the closing index of the stock market. This approach builds the 150 RNN model with different neurons and different weights. After training the RNN model, it verifies the value of the performance measure TS. If the value of the TS ranges between -4 and +4, then the appropriate model is considered a better one. This approach was tested with the S & P 100 closing stock index of the Indian stock market. The results indicate that the proposed approach gives more than 99 percent accuracy with respect to symmetric mean absolute percentage error (SMAPE).

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Keywords

Recurrent Neural Network, Time Series Data, Tracking Signal, Stock Index, Stock Market, Prediction.