A Method to Increase the Analysis Accuracy of Stock Market Valuation: A Case Study of the Nasdaq Index

For a significant period, conventional methodologies have been employed to assess fundamental and technical aspects in forecasting and analyzing stock market performance. The precision and availability of stock market predictions have been enhanced by machine learning. Various machine learning methods have been utilized for stock market predictions. A novel, optimized machine-learning approach for financial market analysis is aimed to be introduced by this study. A unique method for improving the accuracy of stock price forecasting by incorporating support vector regression with the slime mould algorithm is presented in the present work. Other optimization algorithms were employed to enhance the prediction accuracy and the convergence speed of the network, which were Biogeography-based optimization and Gray Wolf Optimizer. An assessment of the proposed model's effectiveness in predicting stock prices was conducted through research employing Nasdaq index data extending from January 1, 2015, to June 29, 2023. Substantial improvements in accuracy for the proposed model were indicated by the results compared to other models, with an R-squared value of 0.991, a root mean absolute error of 149.248, a mean absolute percentage error of 0.930, and a mean absolute error of 116.260. Furthermore, not only is the prediction accuracy enhanced by the integration of the proposed model, but the model's adaptability to dynamic market conditions is also increased.

@article{Niu2024,
  title     = {A Method to Increase the Analysis Accuracy of Stock Market Valuation: A Case Study of the Nasdaq Index},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Haixia Niu},
  doi       = {10.14569/IJACSA.2024.0150173},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150173}
}