Stock Price prediction based on AR-ARCH model: A Case Study of ICBC

Abstract

Accompanying the progress of economy and financial market, the stock market has become one of the investment channels for many people. Therefore, it is of great practical significance to forecast the stock price with High accuracy. Based on the model of Autoregression (AR) and Autoregressive conditional heteroskedasticity (ARCH) models, this paper collects the daily closing share price of Industrial and Commercial Bank of China from 1st January 2018, to 1st June 2021. An AR-ARCH model is established to analyze the model based on the time series under the difference method, and then to forecast the stock price of ICBC in the next 10 days. The predicted stock price is applied to compare with the actual observed value. At last, the conclusion that AR-ARCH model is a suitable model for forecasting volatility and return is drawn. After choosing the fitting model as the AR-ARCH model, the result can be got more quickly and conveniently when people forecast and analyze the volatility and return of a stock. In this way, it can reduce the risk of market volatility according to the forecast of the model and make the investment more stable. Moreover, it can also promote the further study of the AR-ARCH model, and study the uniqueness and universality of the model.