Research On Deep Learning Prediction Model Of RMB Exchange Rate Considering Investor Attention

RMB exchange rate is the link between China and the international economic exchanges and plays an important role in international trade.A stable exchange rate is conducive to the stability of economy and society.However,with the process of exchange rate market-oriented reform and the increase of trade frictions worldwide,the fluctuation of RMB exchange rate has become more frequent.The forecast research of RMB exchange rate can grasp the trend of exchange rate and help investors to avoid risks and regulatory authorities to make policies.Scholars at home and abroad have conducted sufficient studies on the relationship between investor attention and exchange rate trend,but no scholar has taken investor attention as a predictor of exchange rate.Due to the characteristics of high dimension,non-linearity and long memory of exchange rate data,existing classical statistical models and machine learning are not capable of accurately modeling such complex data.Because these prediction models are time-consuming,few model parameters and low practicability.Based on deep learning,this paper introduces investor attention into exchange rate prediction and systematically studies the RMB exchange rate prediction method.Firstly,the concept of investor attention is defined,and the relationship between investor attention and exchange rate,exchange rate forecasting methods are reviewed.Secondly,this paper discusses the commonly used measurement indicators of investor attention.The principal component analysis method is used to screen 46 exchange rate keywords obtained by Baidu index and determine the quantitative index to measure investor attention.Furthermore,the ARIMA model and LSTM model are combined to construct the ARIMA-LSTM prediction model considering investor attention.At the same time,using the advantage of GRU to process exchange rate series data,a deep GRU exchange rate prediction model considering investor attention is constructed.Finally,the trading day data of five indicators from January 2018 to June 2021 are selected to conduct an empirical analysis of the RMB exchange rate prediction model considering investor attention,and comparative analysis of the effect of the prediction model.The characteristics and conclusions of this paper are as follows:(1)Existing research has not included investor attention into the prediction of RMB exchange rate.This paper uses the principal component analysis method to screen Baidu index and determine the quantitative index of investor attention;(2)In view of the different trends of exchange rate fluctuations,the LSTM model is combined with ARIMA model to construct the ARIMA-LSTM prediction model considering investor attention;(3)With the advantages of simple structure and low computational complexity of the deep GRU neural network model,the prediction model of the deep GRU considering investor attention is constructed.The empirical results show the validity of the prediction model.The research of this paper not only puts forward a new method of RMB exchange rate prediction considering investor attention,but also provides theoretical support and analytical approaches for relevant government departments to implement the next plan and investors’ investment decisions.