Abstract: A financial marketplace where shares of companies with public listings are bought and sold is called the stock market. It serves as a gauge of a nation's economic health by taking into account the operations of individual businesses as well as the general business climate. The relationship between supply and demand affects stock prices. Though it might be dangerous, stock market investing has the potential to provide large rewards in the long run. Together with increased prediction accuracy, optimization techniques such as Biogeography-based optimization (BBO), Artificial bee colony algorithm (ABC) and Aquila Optimization (AO) Algorithm further enhance the Extreme gradient boosting (XGBoost) ability to adapt to changing market conditions. The results were 0.955, 0.966, 0.972, and 0.982 for XGBoost, BBO-XGBoost, ABC-XGBoost, and AO-XGBoost, in that order. The performance difference between AO-XGBoost and XGBoost shows how combining with the optimizer may enhance the model's performance. By comparing the output of many optimizers, the most accurate optimization has been determined to be the model's main optimizer.

Yiming LU, “Review and Analysis of Financial Market Movements: Google Stock Case Study” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150414

@article{LU2024,
title = {Review and Analysis of Financial Market Movements: Google Stock Case Study},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150414},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150414},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Yiming LU}
}