Empirical Analysis of Shanghai Stock Exchange Index Based on ARMIA Model and Neural Network Model

Jian Xin, Weizhang Lai

Abstract


 Stocks are an important part of the national economy. With the increase of liquidity in people's hands, more and more people choose to enter the stock market. In stock investment, accurate prediction of stock price index is not only of great significance to investors, but also of promoting the development of my country's stock market. It even has an important role in accelerating my country's economic development. The paper chose the ARIMA method based on linear technology for time series forecasting and the NN model that is good at mining the implicit nonlinear relationship in the data to compare the China Sea Securities Composite Index from January 21, 2020 to December 31, 2020. Empirical analysis of closing prices and short-term forecasts are made.


Keywords


Shanghai Stock Exchange Index; ARIMA model; NN model; short-term forecast

Full Text:

PDF

References


Wang Qing, Wang Zhongli, Li Shixue, Xue Fuzhong. "The short-term impact of the 'new crown pneumonia' epidemic on the price fluctuations of China's stock market". Economic and Management Review, vol.36,no.06,pp.16-27,2020.

Zha Zhenghong. "Statistical analysis and prediction of Shanghai Composite Index". Journal of Shanghai Maritime University,vol.04,1999

Li Zhanjiang, Zhang Hao, Sun Pengzhe, Tong Guochao, Zhang Zhihao. "Research on Shanghai and Shenzhen 300 stock index futures price prediction based on ARIMA mode". Journal of Ludong University (Natural Science Edition), vol.29,no.01,pp.22-24,2013.

Li Jiasong. "Shanghai and Shenzhen 300 Index Forecast and Error Factor Analysis Based on ARIMA Model". Journal of Chifeng University, vol.33,no.1,pp. 73-75,2017.

Zhang Yingchao, Sun Yingjun. "An Empirical Study on the Analysis and Forecast of Shanghai Stock Exchange Index Based on ARIMA Model". Economic Research Guide,vol.11,pp.131-135,2019

Wu Wei, Chen Weiqiang, Liu Bo. "Using BP neural network to predict stock market rise and fall" . Journal of Dalian University of Technology,vol.1,pp.9-15,2001.

Deng Kai, Zhao Zhenyong. "Research and simulation of stock market prediction model based on genetic BP network". Computer Simulation, vol.26,no.05,pp.316-319,2009.

Yin Lu."The theory and application of stock prediction based on GA-BP neural network". North China Electric Power University (background) ,2010.

Liu Jiaqi, Liu Dehong, Lin Tiantian. "Research on Stock Price Based on BP Neural Network Model". China Business Journal, vol.08,pp.29-30,2018.

Zhang Rumeng, Zhang Huamei. Predicting stock prices based on the PCA-BP neural network comprehensive model. Computer Knowledge and Technology, vol.16,no.33,pp. 4-7,2020.

Huang Lixia. "Analysis and forecast of stock price based on ARIMA model——Taking Ping An of China as an example". Science and Technology Economic Market, vol.10,pp. 62-63,2020.

Liu Huihao, Jiao Wenniu, Liu Yue. "The impact of new regulatory regulations on the stock prices of my country’s listed banks: An analysis of policy intervention based on the ARIMA model". Financial Theory Research, vol.05,pp.21-31,2020.

Lin Guochao, Du Yujian, Liu Juan. "The application of two types of combined BP neural network models in stock price forecasting". Modern Business and Trade Industry, vol.41,no.26,pp.141-143,2020.

Velldo A,Liaboa P J G,Vaughan J."Neural network in business : A survery of applications(1992-1998)".Expert Systems with Applications, vol.17,pp.51-54,1999.

Xiong Zhibin."Research on GDP time series forecast based on the integration of ARIMA and neural network". Mathematical Statistics and Management, vol.30,no.02,pp. 306-314,2011.




DOI: http://dx.doi.org/10.26555/konvergensi.v8i1.20850

Refbacks

  • There are currently no refbacks.


View My Stats JIM