Interbank Offered Rate Based on Artificial Intelligence Algorithm

Wangsong Xie

doi:10.1155/2021/9931539

Abstract

Interbank offer rate is the interest rate at which banks lend money to each other in the money market. As a market-oriented core interest rate, Shibor can accurately and timely reflect the capital supply and demand relationship in the money market, and its changes will quickly transmit and affect China’s financial market. Therefore, the purpose of this paper is to predict and study the fluctuation and trend of Shibor. In this paper, the overnight varieties of Shibor were studied and predicted from two time dimensions, namely, daily fluctuation and monthly trend. In the prediction of overnight Shibor daily data, a comparison prediction model based on BP neural network algorithm was first established, and then WNN was applied in the prediction, and the effect was found to be better. When predicting the monthly mean value of overnight Shibor, nine indicators were selected and tested for correlation based on the factors affecting the trend of interest rate, and a regression model of support vector machine was established. Particle swarm optimization algorithm was used to improve the SVR algorithm, and the PSO-SVR prediction model was established to improve the prediction accuracy. The model could basically predict the trend of overnight Shibor. Furthermore, a prediction model of WNN based on cuckoo search (CS) optimization was proposed, which improved the prediction accuracy by 78% and fitted the daily fluctuation of overnight Shibor well.

Highlights

As China’s economic situation continues to improve, the importance of establishing and improving a strong financial market has become increasingly obvious
Pricing or rate of return of other financial products in the financial market can be determined based on this benchmark interest rate, and monetary authorities can make and implement monetary policies based on this interest rate [4, 5]
Among the eight types published by Shibor, overnight varieties had the largest trading volume, and commercial banks used them more, which had the greatest impact on the interest rate market. erefore, this paper studies the overnight Shibor prediction model based on artificial intelligence algorithm

Summary

Introduction

As China’s economic situation continues to improve, the importance of establishing and improving a strong financial market has become increasingly obvious. Ivan T, studied how the introduction of market pricing, which links lending rates to credit default swaps, affected bank funding. Ivanov and Ivan T’s results show that banks have simplified market-based loan pricing contracts, suggesting that the decline in bank debt costs can be explained, at least in part, by a fall in monitoring costs. Gianfranco Giulioni analyzed how changes in policy rates affect bank-related variables by changing the composition of loan portfolios. Wenting Chen’s research is the first study in the literature to consider equity loan value under the framework of stochastic interest rate. Numerical results show that this method is reliable, and the stochastic interest rate makes the optimal execution price of stock loan relatively high [12]. Among the eight types published by Shibor, overnight varieties had the largest trading volume, and commercial banks used them more, which had the greatest impact on the interest rate market. erefore, this paper studies the overnight Shibor prediction model based on artificial intelligence algorithm

Overnight Shibor Prediction Based on Artificial Intelligence

Findings

Analysis of Results