Momentum-Driven Mean-Variance Optimization Strategy for Large-Cap U.S. Stocks
Research Article
Open Access
CC BY

Momentum-Driven Mean-Variance Optimization Strategy for Large-Cap U.S. Stocks

Siyan Chen 1*
1 University of California, Santa Barbara
*Corresponding author: Siyan_chen@ucsb.edu
Published on 30 July 2025
Volume Cover
AEMPS Vol.200
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-257-7
ISBN (Online): 978-1-80590-258-4
Download Cover

Abstract

This study investigates the effectiveness of combining momentum investing with Mean-Variance Optimization (MVO) to improve portfolio performance in large-cap U.S. equities. The core problem is addressing the challenge of systematically enhancing returns and controlling risks compared to passive investment strategies. This research is critical as it contributes to portfolio management by evaluating whether a tactical momentum approach, when integrated with MVO, can offer sustainable risk-adjusted returns superior to standard benchmarks. The methodological approach includes calculating weekly momentum scores across 50 large-cap stocks from diverse sectors over a ten-year period (2015-2025). The top-performing momentum stocks are then weighted weekly using an MVO algorithm that balances risk and return. The strategy performance is evaluated through metrics including annualized returns, volatility, Sharpe ratio, and maximum drawdown, benchmarked against the S&P 500 and an equal-weight portfolio. Results demonstrate significant outperformance of the momentum-MVO strategy relative to benchmarks, achieving consistently higher annualized returns and Sharpe ratios. The strategy effectively reduces maximum drawdowns and provides more stable risk-adjusted returns during volatile market periods. Overall, it indicates robustness in enhancing portfolio efficiency across diverse market conditions. This research suggests significant practical implications by demonstrating that combining momentum with MVO enhances portfolio management effectiveness and can potentially reshape strategic asset allocation practices.

Keywords:

Momentum investing, Mean-Variance Optimization, Risk-adjusted returns, Portfolio strategy, Large-cap stocks.

View PDF
Chen,S. (2025). Momentum-Driven Mean-Variance Optimization Strategy for Large-Cap U.S. Stocks. Advances in Economics, Management and Political Sciences,200,229-238.

References

[1]. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.

[2]. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.

[3]. Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

[4]. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. Journal of Finance, 68(3), 929-985.

[5]. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.

[6]. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross-section of volatility and expected returns. Journal of Finance, 61(1), 259-299.

[7]. Blitz, D., & Van Vliet, P. (2007). The volatility effect: Lower risk without lower return. Journal of Portfolio Management, 34(1), 102-113.

[8]. Cremers, M., & Petajisto, A. (2009). How active is your fund manager? A new measure that predicts performance. Review of Financial Studies, 22(9), 3329-3365.

[9]. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial Economics, 122(2), 221-247.

[10]. Bali, T. G., Engle, R. F., & Murray, S. (2016). Empirical asset pricing via machine learning. Review of Financial Studies, 29(3), 723-765.

[11]. Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor sentiment aligned: A powerful predictor of stock returns. Review of Financial Studies, 28(3), 791-837.

[12]. Frazzini, A., Israel, R., & Moskowitz, T. J. (2018). Trading costs of asset pricing anomalies. Journal of Financial Economics, 128(2), 230-252.

References

[1]. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. Journal of Finance, 48(1), 65-91.

[2]. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.

[3]. Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

[4]. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. Journal of Finance, 68(3), 929-985.

[5]. Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52(1), 57-82.

[6]. Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The cross-section of volatility and expected returns. Journal of Finance, 61(1), 259-299.

[7]. Blitz, D., & Van Vliet, P. (2007). The volatility effect: Lower risk without lower return. Journal of Portfolio Management, 34(1), 102-113.

[8]. Cremers, M., & Petajisto, A. (2009). How active is your fund manager? A new measure that predicts performance. Review of Financial Studies, 22(9), 3329-3365.

[9]. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. Journal of Financial Economics, 122(2), 221-247.

[10]. Bali, T. G., Engle, R. F., & Murray, S. (2016). Empirical asset pricing via machine learning. Review of Financial Studies, 29(3), 723-765.

[11]. Huang, D., Jiang, F., Tu, J., & Zhou, G. (2015). Investor sentiment aligned: A powerful predictor of stock returns. Review of Financial Studies, 28(3), 791-837.

[12]. Frazzini, A., Israel, R., & Moskowitz, T. J. (2018). Trading costs of asset pricing anomalies. Journal of Financial Economics, 128(2), 230-252.

Cite this article

Chen,S. (2025). Momentum-Driven Mean-Variance Optimization Strategy for Large-Cap U.S. Stocks. Advances in Economics, Management and Political Sciences,200,229-238.

Data availability

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

About volume

Volume title: Proceedings of ICEMGD 2025 Symposium: Innovating in Management and Economic Development

ISBN: 978-1-80590-257-7(Print) / 978-1-80590-258-4(Online)
Editor: Florian Marcel Nuţă Nuţă, Ahsan Ali Ashraf
Conference website: https://2025.icemgd.org/Lahore.html
Conference date: 23 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.200
ISSN: 2754-1169(Print) / 2754-1177(Online)

© 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).

For authors
For editors
For reviewers

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.