Study on AI-Assisted Statistical Approach for Improving Stock Price Prediction Accuracy

Gaurav, Tuteja and Varsha, Agarwal and Meeramani, N and Sumitra, Menaria (2024) Study on AI-Assisted Statistical Approach for Improving Stock Price Prediction Accuracy. Journal of Data Science, 2024 (42). pp. 1-9. ISSN 2805-5160

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Abstract

Stock price (SP) prediction is crucial for financial decision-making, yet achieving excessive accuracy remains challenging due to market volatility. Current models frequently struggle with capturing the complexities of SP fluctuations, leading to significant prediction errors. This study aims to improve SP prediction accuracy through a unique technique that uses AI-assisted statistical techniques with the Redefined Spotted Hyena great-tuned Dynamic Gated Recurrent Unit (RSHDGRU). The dataset includes the closing costs of numerous stocks influenced through market demand, corporate performance, and economic situations. Pre-processing using Z-score normalization to standardize the statistics. The proposed RSH-DGRU model significantly outperforms traditional techniques, achieving a R-squared (R²) value of 0.9852, a Mean Absolute Error (MAE) of 15.624 and Root Mean Square Error (RMSE) of 20.321. These results reveal the effectiveness of the RSH-DGRU in minimizing prediction errors and accurately capturing the complexities of SP fluctuations. By evaluating its overall performance with present fashions, the RSH-DGRU technique showcases stronger predictive capabilities. Financial analysts and investors that have access to a strong instrument for more precise market projections make better-informed investment selections.

Item Type:	Article
Uncontrolled Keywords:	Stock price prediction, Redefined spotted hyena, Dynamic gated recurrent unit, Error rates
Subjects:	H Social Sciences > HJ Public Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	08 Nov 2024 04:03
Last Modified:	08 Nov 2024 04:03
URI:	http://eprints.intimal.edu.my/id/eprint/2023

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