CSI 300 Stock Index Futures Price Forecast Research

In my country's financial market,stock index futures is a financial derivative product that avoids risks.On the one hand,it reflects the trend of stock prices.Researching the price of stock index futures is helpful for stock investors to make decisions and avoid risks in stock investment;on the other hand,stock index Futures trading has a leverage effect.Stock index futures investors and hedgers will face greater risk of price fluctuations.In order to avoid large losses,studying the price trend of stock index futures is of great significance to futures investors.In recent years,with the widespread application of machine learning,many machine learning algorithms have been applied to predict the price of various stock index futures in the financial market.This article first introduces the background,significance,and current research status of stock index futures at home and abroad,and then explains fundamental analysis,technical analysis methods,and modern forecasting methods.Next is the preparatory work for data modeling—improving the impact characteristics of stock index futures.The impact characteristics are divided into 13 basic indicators and 30 technical indicators.A total of 43 indicators are selected for data filling,outlier processing,and Standardization processing,and finally the correlation analysis and importance analysis of the selected indicators.Then comes the empirical part of this article.Taking the CSI 300 stock index futures prices as an example,an accurate forecast was made on the price of the next earnings day.Three methods are used:1.Firstly,the VAR model first analyzes the characteristics of the impact of basic indicators on the price of stock index futures,performs co-integration tests,variance decomposition,etc.,provides variables and parameter basis for the machine learning model,then uses the variable data of the lag to make predictions,and finally gives the regression relationship,the results are interpretable.2.The LSTM recurrent neural network model uses the basic index data of the previous quarter and 30 technical index data as the data input of the model,and normalizes the data,through the main parameters such as dropout and other parameters.After debugging,the optimal parameters were found,and the goodness of fit on the training set and the test set reached more than 85%.3.The XGBoost regression prediction model uses the hyperparameter grid search method and the cross-validation method to adjust the parameters step by step.After adjusting an optimal parameter each time,the other parameters need to be updated to the optimal values when the next parameter is adjusted.The goodness of fit of the optimal model is more than 80%,and the MAE value is higher than that of the LSTM model.This article uses three models for prediction and comparison.Through the comparison of model output data and actual data,the application effect of the model is analyzed.Experimental results show that:(1)The price of CSI 300 stock index futures is not only affected by financial market factors including SP500 index,CPI,etc.,but also by technical indicators,and the impact is lagging;(2)The price of CSI 300 stock index futures is greatly affected by itself;(3)Compared with traditional forecasting models,The performance of machine learning is better,and the LSTM model has smaller errors than XGBoost regression and VAR regression;(4)The VAR model has good short-term forecasting effect,and the real-time forecasting effect is also very good;(5)In the long-term forecast of the CSI 300 stock index futures prices,the LSTM network performs better,and LSTM performs best on the training set and the test set.excellent.The research results of this article are helpful to help investors establish automated investment models and investment strategies in the futures market.