Research on Optimal Strategy Scheme based on Neural Network Prediction Model

  • Shuo Zhang School of Business, Jiangnan University
  • Junan Zhu School of Science, Jiangnan University
  • Xuan Qiu School of Mechanical Engineering, Jiangnan University
Article ID: 2961
90 Views, 30 PDF Downloads
DOI: https://doi.org/10.18282/l-e.v10i7.2961
Keywords: Quantitative Investment, Neural Network Model, CBB Strategy

Abstract

This paper focuses on the investment plan with the maximum return around the fluctuation of the stock market. First of all, this paper is based on the neural network model price trend line, and then uses the reverse Bollinger band strategy (CBB model). We calculate the error of the price forecast, draw the residual diagram, and find that the error level of the prediction is within an acceptable range. The model in this paper has strong expansibility and good robustness. Finally, we summarize the strategies and results of the model.

References

[1] Wang Mengtian, research on futures price trend prediction and quantitative trading strategy based on machine learning, 2021, Nanjing University of Finance and economics Page 84.

[2] Li Chenyang, research on stock price prediction and quantitative stock selection based on cnn-lstm, 2021, Northwest University Page 85.

[3] Yang Bo and Liang Wei, deep neural network model construction and optimization strategy Computer age, 2022 (01): Page 8-13 + 18.

[4] Chen Junlan, empirical research on stock portfolio based on Markowitz model Brand research, 2018 (02): 146-147.

[5] Yan Wei, Li Shurong and sun Huanquan, research on dynamic mean variance portfolio based on value at risk constraint Control and decision making, 2007 (02): 169-173.

[6] Song Jusheng, Huang Yixuan and Zheng Wei, research on the effectiveness of brin line Statistics and decision making, 2014 (17): 168-171.

Published
2022-06-07
How to Cite
Zhang, S., Zhu, J., & Qiu, X. (2022). Research on Optimal Strategy Scheme based on Neural Network Prediction Model. Learning & Education, 10(7), 95-98. https://doi.org/10.18282/l-e.v10i7.2961
Issue
Vol. 10 No. 7 (2021)
Section
Article

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