Researches Upon The Factors Of Yu-Ebao Returns And Its Prediction

The current rapid development of Internet financial has caused high attention from all sectors of community. Yu Ebao, as one of its innovative products, has created a new environment of universal financial with its high yield, strong flexibility, low threshold and other advantages, accelerating the pace of market-oriented interest rate greatly. Represented of Yu Ebao for Internet financial products is changing the face of traditional financial revolutionary, especially in mobile payment, resource platform, big data and search engines, etc., which has greatly changed people’s business practices, bringing infinite surprise to users and making great influence on commercial banks. Up to the end of December 2014, the scale of Yu Ebao has reached 578.9 billion yuan, not only becoming the largest monetary funds and public funds, but also the world’s fourth-largest monetary fund. Therefore, to study the influence factors and forecast analysis of Yu Ebao Returns is of great significance.This thesis proposed a different Yu Ebao correlation analysis method based on EEMD decomposition, which were the study of the influence factors of Yu Ebao Returns based on EEMD-VAR and the forecast study of Yu Ebao Returns based on EEMD-GARCH. First, the Yu Ebao Returns series were decomposed into several components (including a set of intrinsic mode functions and a Residual component) with different frequencies by EEMD, then recombining them into high frequency, low frequency and residual components according to the t-test, respectively corresponding to market fluctuation, major events and trend components. Next, qualitative analysis were made about the influence factors of the big three changes, combining Yu Ebao Returns with actual events causing significant changes and quantifying the influence factors. Then empirical research was made through VAR model on the long-term equilibrium relationships and short-term fluctuation situations between Yu Ebao Returns and its various affecting factors. The results showed that:1) The system consisting of Yu Ebao returns and its influential factors is stable;2) The Exchange rates and Interbank lending rates have the largest influencing degree and contribution on the Yu Ebao Returns with short term fluctuation, showing that the tightness degree of the Foreign Exchange Markets and Domestic Market financing level are the most important affecting factors of Yu Ebao Returns; 3)The broad money supply and the bank loan ratio are negatively correlated with Yu Ebao Returns,long-term stable and effective, but the impact is not significant.It is challenging to predict Yu Ebao Returns as the series are non-linear and non-stationary. The traditional statistical models and single forecasting method are built on the linear assumption, so it is difficult to capture the non-linear model hidden in the Yu Ebao Returns sequence, thus making the prediction accuracy drop down. GARCH model was the regression model created specifically for the financial data quantity body custom, particularly applicable to the volatility analysis and forecasting of different kinds of returns. Therefore, in view of the prediction problem of Yu Ebao Returns, this paper proposes a nonlinear combination prediction method to combine EEMD and GARCH. This method applies EEMD to decompose the Yu Ebao Returns series into several components with different frequencies, which are then composed into three subseries according to their frequency level, namely high frequency, low frequency and the trend frequency. The three new sequences represent the market fluctuation price, major event price and trend price respectively. Different GARCH models are constructed for the three new sequences to predict the final value of each sequence respectively, and the final predictive value of EEMD-GARCH can be obtained via the aggregation of the three predict values above. At last, by comparing and analyzing the EEMD-GARCH value with the single GARCH model value, the result shows that EEMD-GARCH method has higher prediction accuracy than single GARCH model. This can not only explain the reasons causing the high and low fluctuation of Yu Ebao Returns and provide good investment decision-making reference for the market participants, but also has important theoretical value and practical significance for the judgment of its future trend and risk control.