Stock Price Prediction Using Backpropagation ANN: Case Study of ADMR (2023–2025) - Khozin, Muhammad; Abidin, Zainal; Chamidy, Totok (Journal of Information System and Informatics, 2025)
This study develops an Artificial Neural Network (ANN) backpropagation model for predicting stock prices using ADMR stock data from 2023 to 2025, obtained from Yahoo Finance. Given the inherent volatility and unpredictability of stock prices, accurate forecasting plays a crucial role in investment decision-making. ANN models are particularly effective for capturing complex, non-linear relationships and patterns in financial data, which traditional statistical models may fail to address. In this research, various configurations were tested by adjusting the number of hidden neurons (5, 10, and 15) and learning rates (0.1, 0.3, and 0.5). The optimal model architecture was found to be 3-10-1, consisting of three input neurons, ten hidden neurons, and one output neuron, which achieved the best prediction performance with a Mean Absolute Percentage Error (MAPE) of 2.26%. This model was trained with a learning rate of 0.3 and completed in 915 iterations. However, the model's predictive capabilities are constrained by its reliance on historical stock prices alone, excluding external factors such as macroeconomic indicators, market sentiment, or trading volume, which may improve its generalization and overall accuracy. Future work could integrate these variables for better robustness and predictive power.