Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)

<Previous Article In Volume
Next Article In Volume>

Portfolio Decision Model Based on NIWPSO-LSTM

Authors
Xinyi Feng1, *, Mingshang Chen2, Zhengrong Hou2
1School of Economics and Management, North China Electric Power University, Beijing, China
2School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing, China
*Corresponding author. Email: ncepuFXY@ncepu.edu.cn
Corresponding Author
Xinyi Feng
Available Online 10 November 2022.
DOI
10.2991/978-94-6463-005-3_22How to use a DOI?
Keywords
Long Short-Term Memory; Portfolio Decision; Nonlinear Programming; Particle Swarm Optimization
Abstract

As a cross product between computer science and financial science, the primary purpose of quantitative investment is to explain the formation principle of financial asset prices and to predict the future price of financial assets. With gold and bitcoin daily price data from London Bullion Market Association and NASDAQ, we develop a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. Firstly, we choose the Long Short-Term Memory neural network based on the improved particle swarm algorithm (NIWPSO-LSTM) to predict the price. We use metabolic grey model (MGM(1,1)) to correct the price in the initial period, to make up for the shortcomings of LSTM which needs a large number of training sets to achieve better prediction results. Secondly, we quantify the return and risk of portfolio investment, establish a nonlinear programming model, and use Monte Carlo simulation method to solve the initial solution. Last, we use indicators to verify the accuracy of our model. All the results show that our model is able to provide extraordinary decision support in a real investment environment.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
Series
Atlantis Highlights in Engineering
Publication Date
10 November 2022
ISBN
10.2991/978-94-6463-005-3_22
ISSN
2589-4943
DOI
10.2991/978-94-6463-005-3_22How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xinyi Feng
AU  - Mingshang Chen
AU  - Zhengrong Hou
PY  - 2022
DA  - 2022/11/10
TI  - Portfolio Decision Model Based on NIWPSO-LSTM
BT  - Proceedings of the 2022 3rd International Conference on E-commerce and Internet Technology (ECIT 2022)
PB  - Atlantis Press
SP  - 215
EP  - 227
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-005-3_22
DO  - 10.2991/978-94-6463-005-3_22
ID  - Feng2022
ER  -