A Solution to Improve the Detection of the Nominal Value of the Financial Market: A Case Study of the Alphabet Stocks

Given the regular occurrence of non-stationarity, non-linearity, and high levels of noise in time series data, predicting the value of stocks is a considerable difficulty. Traditional methods have the potential to enhance the precision of forecasting, although they concurrently introduce computational complexity, hence augmenting the probability of prediction inaccuracies. To effectively tackle a range of concerns, the existing body of research proposes a novel approach that combines a light gradient boosting machine, a machine learning methodology, with artificial bee colony optimization. In the context of the examined dynamic stock market, the proposed model demonstrated better efficiency and performance compared to alternative models. The recommended model exhibited optimal performance, characterized by a low error rate and high efficacy. The analysis utilized data about the stock of Alphabet over the period spanning from January 2, 2015, to June 29, 2023. The outcomes of the study provide evidence of the predictive accuracy of the proposed model in determining stock prices. The study's findings demonstrate how well the suggested model performs when it comes to correctly predicting stock prices. The proposed model presents a pragmatic methodology for evaluating and forecasting time series data about stock prices. The research's findings show that, in terms of forecast accuracy, the suggested model performs better than the methods currently in use.

@article{Li2024,
  title     = {A Solution to Improve the Detection of the Nominal Value of the Financial Market: A Case Study of the Alphabet Stocks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Zhaohua Li and Xinyue Chang},
  doi       = {10.14569/IJACSA.2024.0150119},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150119}
}