Construction of stock price fluctuation prediction model based on ABC-SVR artificial bee colony algorithm

Traditional research on stock price volatility prediction faces problems such as complex models, difficulty in parameter optimisation, and insufficient model generalisation ability. In this paper, the artificial bee colony support vector regression (ABC-SVR) algorithm is applied to optimise the parameter combination of the SVR model. Firstly, the paper collects historical stock price data and related factor data, and extracts technical indicators such as closing price, trading volume, moving average, as well as company financial data features from them. Then, the ABC-SVR algorithm is applied to select the kernel function, adjust the penalty parameters, and construct a stock price volatility prediction model. Finally, the dataset is divided into training and testing sets through cross validation, and the MAE and RMSE of the models on the testing set are determined. Research shows that the model has high prediction accuracy and small errors on various test sets.