Analysis of earnings forecast of blockchain financial products based on particle swarm optimization Abstract   The purpose of this study is to solve the problems of large number of iterations, limitations and poor fitting effect of traditional algorithms in predicting the yield rate of blockchain financial products. In this study, bitcoin yield rate is taken as the research object, and data from June 2, 2016 to December 30, 2018 are collected, totaling 943 pieces. The BP neural network, support vector regression machine algorithm and particle swarm optimization least square vector algorithm are respectively adopted to carry out model simulation and empirical analysis on the collected data, and it is concluded that particle swarm optimization least square vector algorithm has the best fitting effect. Subsequently, the Ethereum (ETH) yield rate is selected as the research object, and the model simulation and empirical analysis are carried out on it, which verifies that the optimized algorithm have better prediction and fitting on the time series. The results show that the particle swarm optimization algorithm among the three algorithms mentioned in this research has the best prediction effect. Therefore, the results of this study have a good fitting effect on the prediction of the yield rate of blockchain financial products, have a good guiding effect on the investors of blockchain financial products, and have a good guiding significance for the study of the yield rate of Chinaxe2x80x99s blockchain financial products.

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.