Aditiya, Hermawan and Stanley, Ananda and Junaedi, Junaedi and Edy, Edy and Riki, Riki Enhancing Stock Price Forecasting: Optimizing Neural Networks with Moving Average Data. bit-Tech.
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Abstract
This research focuses on optimizing a neural network model for stock price prediction using Particle Swarm Optimization (PSO), considering the inherent risks and potential high returns associated with stock investment. Given the challenges posed by stock price volatility, this study combines Moving Average (MA) a fundamental statistical technique in stock market analysis with advanced data mining approaches, specifically neural networks and PSO, to enhance prediction accuracy. The primary objective is to improve the efficiency of neural networks by minimizing error rates and equipping investors with more reliable tools for financial decision-making. The proposed methodology involves converting historical stock price data into a Simple Moving Average (SMA) over a 5-day period, followed by optimizing a neural network model using PSO. This optimization process fine-tunes key parameters, particularly the weight distributions of various stock market indicators, including Open SMA, High SMA, Low SMA, and Close SMA. Model performance is evaluated using Root Mean Square Error (RMSE) as a validation metric. The findings indicate a significant enhancement in the predictive accuracy of the neural network model after PSO optimization. The optimal configuration is identified in a two-layer neural network with a specific node arrangement. This optimized model not only improves stock price forecasting precision but also has practical implications for investors and financial analysts in risk management and profit maximization.
| Item Type: | Article |
|---|---|
| Subjects: | 000 Karya Umum > 005 Pemograman > 005.11 Pemograman Khusus > 005.12 Sistem Analisis dan Desain Perangkat Lunak 000 Karya Umum > 005 Pemograman > 005.12 Sistem Analisis dan Desain Perangkat Lunak |
| Divisions: | Fakultas Sains & Teknologi > Sistem Informasi |
| Depositing User: | Riki Tan |
| Date Deposited: | 07 Jun 2026 15:40 |
| Last Modified: | 08 Jun 2026 04:14 |
| URI: | https://repositori.buddhidharma.ac.id//id/eprint/3270 |
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