Stock Market Price Trend Prediction Using Modified Recurrent Neural Network and Energised Chimp Optimization Algorithm (ECOA)

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 8

Year of Publication : 2025

Authors : Shilpa Dixit, Nitasha Soni

10.14445/23488549/IJECE-V12I8P131

How to Cite?

Shilpa Dixit, Nitasha Soni, "Stock Market Price Trend Prediction Using Modified Recurrent Neural Network and Energised Chimp Optimization Algorithm (ECOA)," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 8, pp. 361-374, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I8P131

Abstract:

Vast data is available for the Stock market, which gets instantly updated and corrected. The stock market is always forecasted as a non-linear time series due to its volatility characteristics. There are many variables that affect the Stock price. Using a simple model to predict is difficult. To overcome this gap, a new deep learning-based stock market price prediction model is used to predict the best time to buy/sell shares. Refinement, extraction of features, feature selection, and stock price prediction are the four fundamental steps of the predictive paradigm. The new Energised Chimp Optimization Algorithm (ECOA) is designed to choose the best extracted features from the processed dataset. This ECOA model is an extended version of the standard Chimp Optimization Algorithm (COA). The new Modified Recurrent Neural Network is used to forecast stock market price trends (M-RNN). The M-RNN makes accurate predictions about stock price value. To improve the RNN’s detection precision. The proposed model is then tested against the current models.

Keywords:

Energised Chimp Optimization Algorithm (ECOA), Feature Extractions, Indicators, Modified Recurrent Neural Network (M-RNN), Stock Market Prediction.

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