Advances in Economics, Management and Political Sciences

- The Open Access Proceedings Series for Conferences


Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 61 , 28 December 2023


Open Access | Article

Mean-variance Portfolio Optimization by LSTM-based Predictions

Meihe Yu * 1
1 ESC RENNES School of Business, Rennes, France

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 61, 102-109
Published 28 December 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Meihe Yu. Mean-variance Portfolio Optimization by LSTM-based Predictions. AEMPS (2023) Vol. 61: 102-109. DOI: 10.54254/2754-1169/61/20231061.

Abstract

With the progress of machine learning, its application fields are gradually increasing, especially in the field of quantitative finance, which is particularly outstanding, the Portfolio optimization combine with time series prediction and machine learning has brought great development prospects for investors. This study mainly employed the LSTM model and Mean-Variance Model to predict stock return and build an optimal combination respectively. This study selects relatively high weight stocks on the NASDAQ index, 'AAPL', 'AMZN',' ASML', 'AVGO', 'GOOG', from December 31, 2019, to July 1, 2023. First, the study obtained the predicted stock prices of five stocks through LSTM Model and based calculated the predicted returns on a rolling basis. Second, based on the modern investment theory, this study uses the predicted rate of return to construct the optimal daily investment ratio through Mean-Variance Model. Finally, this study compared cumulative return of optimal portfolios with the NASDAQ within the same time frame. This study draws a conclusion that hybrid model which combine the stock price forecasting with asset allocation can indeed bring excess returns.

Keywords

Mean-variance model, portfolio management, LSTM model

References

1. Chen, W., Zhang, H., Mehlawat, M. K., and Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943.

2. Ma, Y., Han, R., and Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973.

3. Chaweewanchon, A., and Chaysiri, R. (2022). Markowitz Mean-Variance Portfolio Optimization with Predictive Stock Selection Using Machine Learning. International Journal of Financial Studies, 10(3), 64.

4. Krishnamoorthy, D. N., and Mahabub Basha, S. (2022). An empirical study on construction portfolio with reference to BSE. Int J Finance Manage Econ, 5(1), 110-114.

5. Dai, Z., and Kang, J. (2022). Some new efficient mean–variance portfolio selection models. International Journal of Finance & Economics, 27(4), 4784-4796.

6. Díaz, A., and Esparcia, C. (2021). Dynamic optimal portfolio choice under time-varying risk aversion. International Economics, 166, 1-22.

7. Zhu W. D. (2023). Stock prices forecasting——Financial time series data modeling and decision based on LSTM. Modern Marketing, (03), 39-41. (In Chinese)

8. Budiharto, W. (2021). Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM). Journal of big data, 8, 1-9.

9. Moghar, A., and Hamiche, M. (2020). Stock market prediction using LSTM recurrent neural network. Procedia Computer Science, 170, 1168-1173.

10. Zhou Z. Y., and He, X. L. (2023). Stock price prediction method based on optimized LSTM model. Statistics and Decision, (06), 143-148. (In Chinese)

11. Markowitz, H. M., and Todd, G. P. (2000). Mean-variance analysis in portfolio choice and capital markets (66). John Wiley & Sons.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
ISBN (Print)
978-1-83558-223-7
ISBN (Online)
978-1-83558-224-4
Published Date
28 December 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/61/20231061
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated