Enhancing Portfolio Performances through LSTM and Covariance Shrinkage

Research Article
Open access

Enhancing Portfolio Performances through LSTM and Covariance Shrinkage

Guangqi Li 1*
  • 1 Southwestern University of Finance and Economics    
  • *corresponding author lgq@udel.edu
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/26/20230569
AEMPS Vol.26
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-95-9
ISBN (Online): 978-1-915371-96-6

Abstract

Portfolio optimization is a crucial aspect of finance, requiring advanced analytical tools and modeling techniques. This paper proposes a new method for portfolio optimization that combines Long Short-Term Memory (LSTM) forecasting with Covariance Shrinkage and Mean-Variance Optimization (MVO) to construct diversified portfolios that maximize risk-adjusted returns. The study utilizes an LSTM-based model to predict stock prices, evaluating its performance using the RMSE metric. The calculated RMSE of 0.0849 indicates accurate and robust predictions. The portfolio constructed shows different weights each day for different assets based on the minimum variance and maximum Sharpe ratio portfolios. As of January 3rd, 2023, the assets with the largest proportion in the Maximum Sharpe Ratio portfolio and in the Minimum Volatility portfolio, are respectively BA, accounting for 27.64% of the portfolio and PG, accounting for 32.66% of the portfolio. This paper compares the performance of the proposed method and benchmark methods by applying 30 daily portfolio weights to real returns. The portfolio constructed by the proposed method has higher cumulative return with a higher Sharpe ratio and lower maximum drawdown, indicating a better ability to diversify risks and create returns. The proposed method offers a new perspective on portfolio optimization, which can potentially benefit investors and asset managers.

Keywords:

LSTM, covariance shrinkage, MVO

Li,G. (2023). Enhancing Portfolio Performances through LSTM and Covariance Shrinkage. Advances in Economics, Management and Political Sciences,26,187-198.
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References

[1]. Kalymon, B. A.: Estimation risk in the portfolio selection model. Journal of Financial and Quantitative Analysis 6(1), 559-582 (1971).

[2]. Sahamkhadam, M., Stephan, A., Östermark, R.: Portfolio optimization based on GARCH-EVT-Copula forecasting models. International Journal of Forecasting 34(3), 497-506 (2018).

[3]. 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).

[4]. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997).

[5]. Yahoo finance, https://finance.yahoo.com/,last accessed 2023/4/1.

[6]. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015).

[7]. Gers, F. A., Schmidhuber, J., Cummins, F.: Learning to forget: Continual prediction with LSTM. Neural Computation 12(10), 2451–2471 (2000).

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

[9]. Elton, E. J., Gruber, M. J.: Investments and portfolio performance. World Scientific (2011).

[10]. Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2), 365–411 (2004).

Cite this article

Li,G. (2023). Enhancing Portfolio Performances through LSTM and Covariance Shrinkage. Advances in Economics, Management and Political Sciences,26,187-198.

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：978-1-915371-95-9(Print) / 978-1-915371-96-6(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.26
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Kalymon, B. A.: Estimation risk in the portfolio selection model. Journal of Financial and Quantitative Analysis 6(1), 559-582 (1971).

[2]. Sahamkhadam, M., Stephan, A., Östermark, R.: Portfolio optimization based on GARCH-EVT-Copula forecasting models. International Journal of Forecasting 34(3), 497-506 (2018).

[3]. 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).

[4]. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9(8), 1735–1780 (1997).

[5]. Yahoo finance, https://finance.yahoo.com/,last accessed 2023/4/1.

[6]. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015).

[7]. Gers, F. A., Schmidhuber, J., Cummins, F.: Learning to forget: Continual prediction with LSTM. Neural Computation 12(10), 2451–2471 (2000).

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

[9]. Elton, E. J., Gruber, M. J.: Investments and portfolio performance. World Scientific (2011).

[10]. Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis 88(2), 365–411 (2004).

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