LSTM-based Portfolio Optimization Strategy for SP500

Research Article
Open access

LSTM-based Portfolio Optimization Strategy for SP500

Lyucheng Dong 1*
  • 1 Shanghai Jiao Tong University    
  • *corresponding author david_dong@sjtu.edu.cn
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/25/20230499
AEMPS Vol.25
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-93-5
ISBN (Online): 978-1-915371-94-2

Abstract

Portfolio optimization is a perennial topic in the field of finance and recent breakthrough in deep learning techniques offers a new perspective to tackle it. This study selects 30 stocks of SP500 in different sectors through various constraints and deploys Long Short-Term Memory and Ledoit-Wolf Shrinkage to estimate returns and covariance respectively. The target portfolio is then obtained by inputting the predicted results into the mean-variance model, which is dynamically updated on a daily basis given evolving market information. The results show that the target model this study proposed surpasses the market benchmark (SP500), 1/N portfolio, and other mean-variance variants in terms of numerous financial metrics. Moreover, the target model exhibits volatility invariance and the capability to mitigate risk while extracting returns. This study showcases the revolutionary and promising applications of deep learning in the financial industry, shedding light on novel portfolio allocation strategies for risk-averse investors seeking stable positive returns in turbulent markets.

Keywords:

long short-term memory, portfolio optimization, mean-variance

Dong,L. (2023). LSTM-based Portfolio Optimization Strategy for SP500. Advances in Economics, Management and Political Sciences,25,194-202.
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References

[1]. Kalayci, C. B., Ertenlice, O., Akbay, M. A.: A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications 125, 345–368 (2019).

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

[3]. Jensen, M. C.: Some anomalous evidence regarding market efficiency. Journal of Financial Economics 6(2), 95–101 (1978).

[4]. Basak, S., Kar, S., Saha, S., Khaidem, L., Dey, S. R.: Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance 47, 552–567 (2019).

[5]. Zhang, D., Hu, M., Ji, Q.: Financial markets under the global pandemic of COVID-19. Finance Research Letters 36, 101528 (2020).

[6]. Dixon, M. F., Igor, H., Paul, B.: Machine Learning in Finance. Berlin and Heidelberg: Springer International Publishing (2020).

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

[8]. Roondiwala, M., Patel, H., Varma, S.: Predicting stock prices using LSTM. International Journal of Science and Research (IJSR) 6(4), 1754-1756 (2017).

[9]. Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018).

[10]. Khaidem, L., Saha S., Roy, D. S.: Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003 (2016).

[11]. Markowitz, H.: Portfolio Selection. The Journal of Finance 7(1), 77–91 (1952).

Cite this article

Dong,L. (2023). LSTM-based Portfolio Optimization Strategy for SP500. Advances in Economics, Management and Political Sciences,25,194-202.

Data availability

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

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About volume

Volume title: Proceedings of the 2023 International Conference on Management Research and Economic Development

ISBN：978-1-915371-93-5(Print) / 978-1-915371-94-2(Online)
Editor：Canh Thien Dang, Javier Cifuentes-Faura
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.25
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Kalayci, C. B., Ertenlice, O., Akbay, M. A.: A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Systems with Applications 125, 345–368 (2019).

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

[3]. Jensen, M. C.: Some anomalous evidence regarding market efficiency. Journal of Financial Economics 6(2), 95–101 (1978).

[4]. Basak, S., Kar, S., Saha, S., Khaidem, L., Dey, S. R.: Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance 47, 552–567 (2019).

[5]. Zhang, D., Hu, M., Ji, Q.: Financial markets under the global pandemic of COVID-19. Finance Research Letters 36, 101528 (2020).

[6]. Dixon, M. F., Igor, H., Paul, B.: Machine Learning in Finance. Berlin and Heidelberg: Springer International Publishing (2020).

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

[8]. Roondiwala, M., Patel, H., Varma, S.: Predicting stock prices using LSTM. International Journal of Science and Research (IJSR) 6(4), 1754-1756 (2017).

[9]. Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270(2), 654–669 (2018).

[10]. Khaidem, L., Saha S., Roy, D. S.: Predicting the direction of stock market prices using random forest. arXiv preprint arXiv:1605.00003 (2016).

[11]. Markowitz, H.: Portfolio Selection. The Journal of Finance 7(1), 77–91 (1952).

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