Author(s)

Lu Xinwen ¹

Affiliation(s)

¹ Institute of Statistics and Applied Mathematics, Anhui University of Finance & Economics, Bengbu, 233030, China

Corresponding Author

ABSTRACT

In this paper, the ARIMA(2,1,2) model and LSTM model are used to predict the Shanghai Composite Index, and then the IOWGA operator is used to establish a combined forecast model to further improve the model forecast accuracy. The empirical results show that the errors of the combined forecasting model are lower than those of the individual forecasting models, and the average precision is better than that of the individual forecasting models. In addition, the forecast results show that the Shanghai Composite Index will fluctuate slightly in the next five trading days.

KEYWORDS

CITE THIS PAPER

Lu Xinwen, Research on Combination Prediction of Shanghai Composite Index Based on IOWGA Operator. Financial Engineering and Risk Management (2023) Vol. 6: 87-94. DOI: http://dx.doi.org/10.23977/ferm.2023.060810.

REFERENCES

[1] Yao Jinhai. Research on Stock Index Forecasting Based on the Combination of ARIMA and Information Granularized SVR [J]. Operational Research and Management, 2022, 31(05):214-220. 
[2] Liu Ming, Shan Yuying. Prediction of stock index closing price based on EMD-LSTM model [J]. Journal of Chongqing University of Technology (Natural Science), 2021, 35(12):269-276. 
[3] Liu Jiaming, Liu Haibin. The Application of Combined Forecasting Model in Short-term Stock Index Forecasting—An Empirical Analysis of Shanghai Composite Index as an Example [J]. China Business, 2014(05):93-96. 
[4] Cheng Changpin, Chen Qiang, Jiang Yongsheng. Stock Price Forecast Based on ARIMA-SVM Combination Model [J]. Computer Simulation, 2012, 29(06):343-346. 
[5] Wu Dashuo, Zhang Chuanlei, Chen Jia, Xiang Qihuai. Improved LSTM Neural Network Stock Index Prediction Analysis Based on Genetic Algorithm [J]. Computer Application Research, 2020, 37(S1):86-87+107.

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