Download pdf
Mean-variance Portfolio Optimization by LSTM-based Predictions
- 1 ESC RENNES School of Business, Rennes, France
* Author to whom correspondence should be addressed.
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
[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.
Cite this article
Yu,M. (2023). Mean-variance Portfolio Optimization by LSTM-based Predictions. Advances in Economics, Management and Political Sciences,61,102-109.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) 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 policy for details).