Abstract
Research and design a quantitative investment strategy based on multi-factor model. The neural network algorithms are used to make predictions, and decisions are made based on the changes in stock pools on that day. This study attempts to obtain a positive deviation in profit under certain trading probability without interaction effects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bodi, Z.: Investment Science. Machinery Industry Press (2012)
Andersen, T.G., Bollerslev, T., Diebold, F.X., et al.: The distribution of realized stock return volatility. J. Financ. Econ. 61(1), 43–76 (2001)
Mclean, R.D., Pontiff, J.: Does academic research destroy stock return predictability. J. Financ. 71(1), 5–32 (2016)
Optimizing a portfolio of liquid and illiquid assets, Springer Volume: Optimal Financial Decision Making under Uncertainty
INFORMS Paper: Integrated Asset Allocation Model: Application to Norwegian Sovereign Wealth Fund
Chen, S., Meng, Q., Zhao, Y.: Comparative analysis of FF multi-factor model in Chinese stock market. J. Soc. Sci. Jilin Univ. 05, 93–98 (2003)
Li, W., Li, J.: Improvement of semi-supervised kernel clustering algorithm based on multi-factor stock selection. Statistics and Information Forum (2018)
Sun, S.: Empirical Analysis of Quantitative Stock Selection Model Based on Shanghai and Shenzhen 300. Fudan University (2013)
Zhou, Z.: Machine Learning. Tsinghua University Press, Beijing (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, Q. (2021). Neural Network Algorithm Strategy Based on Multi-factor Stock Selection. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_44
Download citation
DOI: https://doi.org/10.1007/978-3-030-53980-1_44
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53979-5
Online ISBN: 978-3-030-53980-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)