| CSI 300 stock index futures are indicators that sensitively reflect the trend of stock price changes in the Chinese securities market,and are therefore an important basis for investors to analyze stock market dynamics and make investment decisions.But at the same time,the movement of stock index futures is a complex movement,and the superposition of macro,micro and other factors makes forecasting stock index futures a very challenging task.Therefore,how to accurately predict stock index futures so that investors can make more reasonable investment decisions has become a hot issue in economic academic research.This paper first summarizes the research trends of domestic and foreign scholars on stock market forecasting and neural network forecasting in the stock market,and further introduces the basic theory and forecasting methods of stock index futures in detail.Secondly,since the volatility of stock index futures is affected by many factors,the influencing factors are summarized and classified according to relevant literature research,and the dimensionality reduction of the influencing factors is carried out by principal component analysis method to construct a data space-based stock index futures forecasting indicator system.Subsequently,this paper uses the artificial fish swarm algorithm to optimize the main parameters of the LSTM neural network,and builds a long short-term memory neural network prediction model(AFSA-LSTM)optimized based on the artificial fish swarm algorithm.Finally,an empirical study on the prediction of CSI 300 stock index futures is carried out.Based on data collection and preprocessing,the basic market data of stock index futures and the index system based on the data space constructed in Chapter 4 are brought into the data selection,and the completion of AFSA-LSTM model training,and based on the AFSA-LSTM model to achieve the Shanghai and Shenzhen 300 stock index futures forecast,and the error analysis of the forecast results;at the same time,the convolutional neural network(CNN),(LSTM)single model and the hybrid proposed in this paper are selected.The models are compared and analyzed,and the analysis results prove that the AFSA-LSTM prediction model proposed in this paper has better practicability and prediction performance in the prediction of stock index futures.The research in this paper shows that:by adding the processed index system,the prediction effect is better than using only a single market data;the optimized neural network algorithm is better than a single neural network in stock index prediction,and the prediction deviation Therefore,the AFSA-LSTM hybrid prediction model proposed in this paper provides a meaningful reference for the prediction of stock index futures and for investors to make reasonable investment decisions. |
