[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Stock intelligent investment strategy based on support vector machine parameter optimization algorithm

  • Deep Learning for Big Data Analytics
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The changes in China’s stock market are inseparable from the country’s economic development and macroeconomic regulation and control and have far-reaching significance in promoting China’s national economic growth. Compared with the Western developed capital market, China’s current stock market’s main smart investment strategy still has certain defects. Based on the SVM model, this paper establishes a predictive model that combines kernel parameters and parameter optimization to model. The mesh search method, genetic algorithm, and particle swarm optimization algorithm are used to optimize the parameters of the SVM under various kernel functions such as radial basis kernel function. The algorithm and particle swarm optimization algorithm optimize the parameters of the SVM to strengthen the applicability of the model in practice. The empirical results show that under the three-parameter optimization algorithms, the prediction results are higher than the random prediction accuracy, which indicates that it is effective to optimize the model by adjusting the parameters of the SVM. Among them, the SVM using the genetic algorithm parameter optimization under the radial basis kernel function shows the better prediction effect, which is the closest to the real value in the stock market forecast. The particle swarm algorithm supports the vector machine to predict the effect is slightly lower than the grid. Search method. In addition, through comparison experiments, the guess accuracy of BP neural network is worse than that of the support vector machine model before the adjustment. Finally, this paper uses the well-trained model to plan the stock smart investment plan.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Chen X, Li W, Shiyang H, Liu X (2019) Quality of information disclosure, property rights, and bank loans: a bank heterogeneity perspective. China J Account Res 12(01):63–92

    Article  Google Scholar 

  2. Bildirici M (2019) The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula. Pet Sci 16(01):217–228

    Article  Google Scholar 

  3. Huang C (2019) US Stock Market Efficiency: EMH or AMH?. AEIC Academic Exchange Information Centre (China). In: Proceedings of 2019 4th international conference on financial innovation and economic development (ICFIED 2019) (Advances in Economics, Business and Management Research, VOL.76). AEIC Academic Exchange Information Centre (China): International Conference on Humanities and Social Science Research, p 5

  4. Cheng C (2018) Application of Monte Carlo simulation based on GARCH model in risk measurement of stock market in China. Institute of Management Science and Industrial Engineering. In: Proceedings of 2018 international conference on management science and industrial economy development (MSIED 2018). Institute of Management Science and Industrial Engineering: International Society of Computer Science and Electronic Technology, p 3

  5. Zeng JL (2018) Analysis of the impact of crude oil futures price on China’s a-share oil stock price based on optimized genetic algorithms. International Information and Engineering Association. In: Proceedings of 2018 international conference on data processing, artificial intelligence, and communications (DPAIC 2018). International Information and Engineering Association: International Society of Computer Science and Electronic Technology, p 5

  6. Mei W (2018) Stock price prediction based on ARIMA-SVM model. Institute of Management Science and Industrial Engineering. In: Proceedings of 2018 international conference on big data and artificial intelligence (ICBDAI 2018). Institute of Management Science and Industrial Engineering: Computer Science and Electronic Technology International Society, p 7

  7. Saimai AY, Suzhen Y, Laiti·A (2019) Research on the relationship between stock price and exchange rate fluctuation in China—an empirical analysis based on VAR model. J Beijing Finance Trade Voc Coll (02):21–25

  8. Jing C, Ling W (2019) Analysis of the factors affecting the stock price index of China’s listed insurance companies. China Collect Econ 12:117–118

    Google Scholar 

  9. Yin H, Wang P (2019) Heterogeneity change of capital flow impact, investor emotion and stock liquidity. J Dalian Univ Technol (Social Science Edition), 2019(03)

  10. Li Y, Liu H, Ge L (2019) An empirical analysis of the impact of stock liquidity on China’s stock market efficiency. Stat Decis Mak 35(06):168–172

    Google Scholar 

  11. Hao X, Yuxi S (2019) Research on the relationship between stock turnover rate and return rate. SAR Econ 03:93–95

    Google Scholar 

  12. Qing Y, Chenwei W (2019) Global stock index prediction based on deep learning LSTM neural network. Stat Res 03:65–77

    Google Scholar 

  13. Peipei K, Tao J (2019) Study on stock investment decision based on fuzzy analytic hierarchy process. Econ Res Guide 09:67–71

    Google Scholar 

  14. Peng Y, Liu Y, Zhang R (2019) Modeling and analysis of stock price forecast based on LSTM. Comput Eng Appl 55(11):209–212

    Google Scholar 

  15. Wang Z, Xie W, Li B (2019) Variable step size BLSTM integrated learning stock forecast. J Huaqiao Univ (Nat Sci) 40(02):269–276

    Google Scholar 

  16. Yuzhi L, Zhuyuan Y, Xinguo G, Cuiling H, Chunju W (2019) Stock forecasting based on wavelet neural network. J Yunnan National Univ Nat Sci Ed 28(02):156–159

    MATH  Google Scholar 

  17. Qianfeng W (2019) Rolling force prediction of rolling mill based on improved support vector machine algorithm. Forging Stamp Technol 04:131–137

    Google Scholar 

  18. Yiqing L, Wushan C (2019) Study on face detection of support vector machine based on PCA. Comput Meas Control 27(03):49–54

    Google Scholar 

  19. Yuan Y, Yu S, Wang C, Zhou A (2019) A classification model of surrounding rock stability based on grid search method for support vector machine. Geol Prospect 55(02):608–613

    Google Scholar 

  20. Shixiang Z (2019) Automobile sales forecast based on genetic algorithm optimized support vector machine. Bus Manag 01:128–131

    Google Scholar 

  21. Lei Z, Mengxi Y, Chaoen X, Youheng D (2018) Hardware Trojan detection based on optimized support vector machine algorithm. Appl Electr Techn 44(11):17–20

    Google Scholar 

  22. Pan X, Wu F (2018) Study on ACC optimization algorithm based on SVM in intrusion detection. J Longyan Univ 36(05):18–22

    Google Scholar 

  23. Qiu Z, Qian Y, Zhang Y, Zhang W (2018) Gas turbine fault diagnosis based on artificial bee colony algorithm optimized support vector machine. Therm Power Eng 33(09):39–43+57

    Google Scholar 

  24. Can C, Li Jianyong X, Wensheng NM (2018) Tool wear state recognition based on support vector machine and particle filter. J Vib Shock 37(17):48–55+71

    Google Scholar 

  25. Jin W (2018) Human motion recognition method based on support vector machine optimization. Electr Des Eng 26(17):6–9+16

    Google Scholar 

  26. Hajiaghaei-Keshteli M, Fathollahi Fard AM (2019) Sustainable closed-loop supply chain network design with discount supposition. Neural Comput Appl 31(9):5343–5377

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Nature Science Foundation of China (NSFC) (No. 71673265).

Author information

Authors and Affiliations

  1. School of Economics and Management, Hubei University of Automotive Technology, Shiyan, China

    Xuetao Li

  2. School of Economics and Management, University of the Chinese Academy of Sciences, Beijing, China

    Yi Sun

Authors
  1. Xuetao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Sun.

Ethics declarations

Conflict of interest

There are no conflicts of interests of this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Sun, Y. Stock intelligent investment strategy based on support vector machine parameter optimization algorithm. Neural Comput & Applic 32, 1765–1775 (2020). https://doi.org/10.1007/s00521-019-04566-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04566-2

Keywords

Search

Navigation