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2017•Journal of World Economic ResearchOpen access

Empirical Study on Shanghai Composite Index Forecast Based on ARIMA Model

Wu Haijian

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Abstract

Time series analysis is an important research tool in the field of stock price prediction. It analyzes the historical data to find out its development rules and guide people's future decision-making. This paper selects the monthly average closing price of the Shanghai Composite Index from January 1991 to September 2017 as the research object. By using EViews 7.2 software, the stationary non-white noise sequence is obtained after the first-order difference of the non-stationary raw data, and then establishing the autoregressive integrated moving average (ARIMA) model to forecast the future trend of Shanghai Stock Index.

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Time series analysis is an important research tool in the field of stock price prediction. It analyzes the historical data to find out its development rules and guide people's future decision-making. This paper selects the monthly average closing price of the Shanghai Composite Index from January 1991 to September 2017 as the research object. By using EViews 7.2 software, the stationary non-white noise sequence is obtained after the first-order difference of the non-stationary raw data, and then establishing the autoregressive integrated moving average (ARIMA) model to forecast the future trend of Shanghai Stock Index.

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Available abstract

Time series analysis is an important research tool in the field of stock price prediction. It analyzes the historical data to find out its development rules and guide people's future decision-making. This paper selects the monthly average closing price of the Shanghai Composite Index from January 1991 to September 2017 as the research object. By using EViews 7.2 software, the stationary non-white noise sequence is obtained after the first-order difference of the non-stationary raw data, and then establishing the autoregressive integrated moving average (ARIMA) model to forecast the future trend of Shanghai Stock Index.

Key concepts: Autoregressive integrated moving average, Composite index, Autoregressive–moving-average model, Econometrics, Time series, Moving average, Autoregressive model, Index (typography)

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