The Artificial-intelligence-algorithms-based Prediction About The Shanghai Interbank Offered Rate

Inter Bank Offered Rate refers to the benchmark rate that banks use in circulating funds in the monetary market. As the core rate of the marketization, the Shanghai Interbank Offered Rate(Shibor) can accurately and timely reflect the supply and demand of funds in the monetary market and its variat ion will quickly influence the monetary market in China. Therefore, it is very important to predict the fluctuation and the tendency of the Shanghai Interbank Offered Rate.This paper will research and predic t the overnight SHIOR varieties in two dimensions of time, which are the daily fluctuation and the monthly tendency. In order to predict the overnight Shibor daily data, firstly, establish a comparative prediction model based on Back-Propagation( BP) neural network algorithm. Secondly, apply the wavelet neural network to the prediction, which results in better effectiveness. Lastly, bring up the idea of wavelet neural network prediction model based on opt imizat ion of the Cuckoo Search(CS), which enhances the accuracy of the prediction and matches the daily fluctuation of the overnight Shibor in a better way. When predicting the monthly average of the overnight Shibor, from the point of the factors that will affect the tendency of the rate, select 9 indicators to inspect the correlation, and build the regressive Support Vector Regression(SVR) prediction model. After that, improve SVR algorithm by utilizing the Particle Swarm Optimizat ion(PSO) and build the PSO-SVR prediction model to help enhance the accuracy of the prediction, in which way the model can basically predict the tendency of the overnight Shibor.Select and optimize the appropriate algorithm based on the features of the daily and monthly data. The built prediction models are scientif ic and applicable in some way in predicting the daily data and the monthly average rate of the overnight Shibor. Establishing the overnight Shibor deciding system and integrating the prediction models will certainly provide some guidance for the participants of the monetary market.