Analysis of the Financial Market via an Optimized Machine Learning Algorithm: A Case Study of the Nasdaq Index

The complex interaction among economic variables, market forces, and investor psychology presents a formidable obstacle to making accurate forecasts in the realm of finance. Moreover, the nonstationary, non-linear, and highly volatile nature of stock price time series data further compounds the difficulty of accurately predicting stock prices within the securities market. Traditional methods have the potential to enhance the precision of forecasting, although they concurrently introduce computational complexities that may lead to an increase in prediction mistakes. This paper presents a unique model that effectively handles several challenges by integrating the Moth Flame optimization technique with the random forest method. The hybrid model demonstrated superior efficacy and performance compared to other models in the present investigation. The model that was suggested exhibited a high level of efficacy, with little error and optimal performance. The study evaluated the efficacy of a suggested predictive model for forecasting stock prices by analyzing data from the Nasdaq index for the period spanning from January 1, 2015, to June 29, 2023. The results indicate that the proposed model is a reliable and effective approach for analyzing and forecasting the time series of stock prices. The experimental findings indicate that the proposed model exhibits superior performance in terms of predicting accuracy compared to other contemporary methodologies.

@article{Wang2024,
  title     = {Analysis of the Financial Market via an Optimized Machine Learning Algorithm: 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    = {Lei Wang and Mingzhu Xie},
  doi       = {10.14569/IJACSA.2024.0150120},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150120}
}