Autoregressive moving average modeling in the financial sector

Time series modelling has long been used to make forecast in different industries with a variety of statistical models currently available. Methods for analyzing changing patterns of stock prices have always been based on fixed time series. Considering that these methods have ignored some crucial factors in stock prices, we use ARIMA model to predict stock prices given the stock-trading volume and exchange rate as independent variables to achieve a more stable and accurate prediction process. In this paper we will introduce the modeling process and give the estimate SSE (Shanghai Stock Exchange) Composite Index to see the model's estimation performance, which proves to be feasible and effective.