Presenting an Optimized Hybrid Model for Stock Price Prediction

In the finance sector, stock price forecasting is deemed crucial for traders and investors. In this study, a detailed comparison and analysis of various machine learning models for stock price forecasting were undertaken. Historical stock data and an array of technical indicators were utilized in these models. The enhancement of the Histogram-Based Gradient Boosting (HGBR) method for predicting the Nasdaq stock index was the focus. Optimization techniques such as genetic algorithm optimization, biologically-based optimization, and the grasshopper optimization algorithm were applied. Among these, the most promising results were shown by the grasshopper optimization method. The optimized HGBR models, namely GA-HGBR, BBO-HGBR, and GOA-HGBR, were found to have achieved significant improvements, with coefficient of determination values of 0.96, 0.98, and 0.99, respectively. These figures underscore the substantial advancement of these models as compared to the baseline HGBR model. Metrics such as Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, and the Coefficient of Determination were employed to assess the performance of the models.

@article{LIU2024,
  title     = {Presenting an Optimized Hybrid Model for Stock Price Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Liangchao LIU},
  doi       = {10.14569/IJACSA.2024.0150174},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150174}
}