Forecasting of Philippine Stock Exchange Index Using Optimized Artificial Neural Networks with Enhanced PSO Algorithm
Forecasting of Philippine Stock Exchange Index Using Optimized Artificial Neural Networks with Enhanced PSO Algorithm |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-6 |
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| Year of Publication : 2024 | ||
| Author : Dennis M. Barrios II, Bobby D. Gerardo |
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| DOI : 10.14445/22315381/IJETT-V72I6P113 | ||
How to Cite?
Dennis M. Barrios II, Bobby D. Gerardo, "Forecasting of Philippine Stock Exchange Index Using Optimized Artificial Neural Networks with Enhanced PSO Algorithm," International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 128-135, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I6P113
Abstract
This research paper explores the application of the Enhanced Particle Swarm Optimization (PSO) algorithm, called the Random Adaptive Backtracking Particle Swarm Optimization (RAB-PSO) algorithm, to optimize Artificial Neural Networks (ANNs) for forecasting the Philippine Stock Exchange Index (PSEi). The study utilizes a dataset spanning from May 11, 2018, to May 10, 2023, sourced from Yahoo Finance and standardized for analysis. The hyperparameter of an ANN model is finetuned using the RAB-PSO algorithm to enhance forecasting accuracy. Evaluation metrics such as Root Mean Square Error (RMSE) and R² are employed to assess the performance of the optimized ANN model. The results indicate that the ANN model optimized with RAB-PSO has minimal error rates, significantly outperforming the standard PSO algorithm. Generally, this research contributes to the field of PSEi forecasting and emphasizes the significance of optimizing hyperparameters through enhanced PSO for ANNs in financial prediction tasks.
Keywords
Artificial Neural Network, Forecasting, Machine learning, Metaheuristic optimization algorithms, Particle swarm optimization, Hyperparameter optimization.
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