Predicting stock prices for Chinese performing arts companies using genetic algorithm-based backpropagation neural networks

Abstract

Due to China’s thriving economy and culture, the performing arts sector has grown remarkably. To study its development, this study has examined the closing prices of performing arts companies. The GA-BPN model was used to analyze the daily closing prices of Funshine Culture (ticker: 300860) and Sanxiang Impression (ticker: 000863) for the predictions of their future daily closing prices. Next, the study compared the predicted prices with the actual closing prices. By comparing four models, namely GA, 7-4-1, 7-4-4-1, and 7-4-4-4-1, the GA-BPN model has a mean square error (MSE) of 2472.580273 and a root mean square error (RMSE) of 49.72504674, which is the smallest value and the smallest error among the four assessment metrics, it was determined that the GA-BPN model yielded the most accurate prediction results, so it was suitable for forecasting stock closing prices.