An analysis of combining correlation screening with artificial neural network for FITX futures prediction FITX futures provide investors with tools to evade risks and serve as an investor commodity; in other words, speculators and arbitragers make use of the highlevel feature of futures to earn enormous returns with a small sum of money. FITX price is easily influenced by various factors, including finance, economy, politics, society, and investorsxe2x80x99 mind. In this study, some features of the FITX futures market and the back propagation network (BPN) are used to predict FITX futures. The BPN is adopted to predict the nextday closing prices of FITX futures. In this paper, the variables of the original data regarding price, quantity, and time, as well as the fluctuation estimated by GARCH, were taken as the input variables. The R software was used to establish the BPN. The nextday closing indices of FITX futures were predicted after training, as based on the back propagation model. In addition, the coefficient of correlation between the input variables and the closing prices is used to select the optimal input variable to establish the BPN model. According to the empirical research results, the proposed methods are more accurate in prediction in comparison with the original BPN model.

Blockchain financial investment based on deep learning network algorithm Abstract   In order to study the use of indepth learning to process financial data, it is proposed that the related technology of neural network and deep learning can be applied to financial data, and real stock index and futures data can be used to explore the application effect of neural network and indepth learning. Firstly, the theory and model of indepth learning and neural network are introduced in detail. Secondly, a simple neural network and indepth learning model are used in the stock index and futures price forecast. The data used in the input of the model include the price of a stock in the current trading and some data indicators, and the closing price of a stock in the next time. The decline will be reflected in output. If the output is up for 1 and down for 0, new data will be input after the training of the model. Finally, the output data can be compared with the real data to judge the application effect of the model, after comparing e and analyzing the application effect of the model. The results show that the research in this study can help investors to build an automated investment model and the investment strategy of the stock market. The construction can be used for reference to improve investorsxe2x80x99 investment strategy and return rate.