Study on AI-Assisted Statistical Approach for Improving Stock Price Prediction Accuracy
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...
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Main Authors: | , , , |
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Format: | Article |
Language: | English English |
Published: |
INTI International University
2024
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Subjects: | |
Online Access: | http://eprints.intimal.edu.my/2023/1/jods2024_42.pdf http://eprints.intimal.edu.my/2023/2/564 http://eprints.intimal.edu.my/2023/ http://ipublishing.intimal.edu.my/jods.html |
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Summary: | 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. |
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