Hybrid Model (SVM-GA) to Improve the Accuracy of Stock Price Forecasting
DOI:
https://doi.org/10.31272/jae.i153.1575Keywords:
Stock price prediction, supporting vector machine, genetic algorithm, hybrid models, MSEAbstract
Although hybrid models have proven effective in predicting stock prices, most previous studies have focused on deep learning algorithms (LSTM, CNN) without fully leveraging the capabilities of genetic algorithms to optimise the coefficients of supporting vector machines, especially when dealing with multi-frequency time series data. This research presents an innovative hybrid model (GA-SVM) that combines a genetic algorithm with a support vector machine. The genetic algorithm is used to dynamically optimise the support vector machine's coefficients and select optimal features.
The model was tested on 12 international time series and 3 different time frequencies (daily, hourly, and minutely), and its stability was verified with a data deficiency of up to 5%. The GA-SVM model achieved superior predictive accuracy, showing a 97.1% improvement over the conventional SVM model, with a very low coefficient of variation (2.27%) and a highly statistically significant difference (p < 0.001) compared to alternative models (CNN-PSO, RNN, and BJM).
This research provides a practical and reliable solution for investors and financial analysts, as the clearly defined optimal model parameters (C ∈ [10, 50] and γ ∈ [0.001, 0.05]) facilitate its immediate application in real financial platforms while maintaining stability under conditions of incomplete data.
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