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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: One of the most important and common activities mentioned while discussing the financial markets is stock market trading. An investor is constantly searching for methods to estimate future trends to minimize losses and maximize profits due to the unavoidable volatility in stock prices. It is undeniable, nonetheless, that there is currently no mechanism for accurately estimating future market patterns despite numerous approaches being investigated to enhance model performance as much as feasible. Findings indicate notable improvements in accuracy compared to traditional Histogram-based gradient-boosting models. Experiments conducted on historical stock price datasets verify the efficacy of the proposed method. The combined strength of HGBoost and optimization techniques, including Particle Swarm Optimization, Slime Mold Algorithm, and Grey Wolf Optimization, not only increases prediction accuracy but also fortifies the model's ability to adjust to changing market conditions. The results for HGBoost, PSO- HGBoost, SMA- HGBoost, and GWO- HGBoost were 0.964, 0.973, 0.981, and 0.988, in that order. Compared to HGBoost, the result of GWO- HGBoost shows how combining with the optimizer can enhance the output of the given model.
Shigen Li, “Estimating Stock Market Prices with Histogram-based Gradient Boosting Regressor: A Case Study on Alphabet Inc” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150553
@article{Li2024,
title = {Estimating Stock Market Prices with Histogram-based Gradient Boosting Regressor: A Case Study on Alphabet Inc},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150553},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150553},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Shigen Li}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.