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. 70 , 08 January 2024
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Utilizing the Mean-Variance Model to Optimize Pension Investment Portfolios: A Detailed Analysis of Key Industries
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Abstract
Pension investment portfolios play a pivotal role in ensuring financial security for retirees. With an aging global population, the importance of optimizing these portfolios to withstand market fluctuations and ensure steady returns becomes paramount. This study delves into the complexities of pension investment portfolios using the time-tested mean-variance model, aiming to strike a balance between risk and expected returns. Focusing on key industries like technology, entertainment, automotive, resources, and air travel, the research critically analyzes a diverse set of stocks. Methods employed include a deep analysis of expected returns, variances, covariances, and the overarching risk-return trade-off. Preliminary results underscore the nuanced nature of portfolio management and the indispensability of incorporating modern portfolio theory in pension fund management. The outcomes of this study not only aid in making informed investment decisions but also shed light on the broader social implications of robust pension fund management, emphasizing its significance in securing retirees' financial futures.
Keywords
Pension, Investment Portfolio, Mean-Variance Model, Risk-Return Trade-off, Modern Portfolio Theory
References
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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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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-271-8
- ISBN (Online)
- 978-1-83558-272-5
- Published Date
- 08 January 2024
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/70/20231589
- Copyright
- 08 January 2024
- 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