Modern Portfolio Theory in Practice: Optimizing Risk-Return Trade-offs through a Case Study of Tesla and Procter & Gamble

DOI

10.2991/978-94-6463-811-0_73How to use a DOI?

Keywords

Modern Portfolio Theory; Risk-Return; Diversification

Abstract

This study investigates the practical application of Modern Portfolio Theory (MPT) by examining two stocks with markedly different risk-return profiles: Tesla (TSLA) and Procter & Gamble (PG). Based on historical data from 2018 to 2024, Tesla—a high-volatility growth stock—shows an annualised return of 47.96% alongside a volatility of 74.13%, while PG, a low-volatility defensive stock, delivers a 16.71% return with a volatility of 42.91%. Using Excel Solver to optimise the Sharpe ratio, the research identifies the best-performing portfolio as 88% Tesla and 12% PG, achieving an annualised return of 44.26% and reducing volatility to 68.02%—6.11% lower than holding Tesla alone—while boosting the Sharpe ratio to 0.621. These findings validate MPT’s core principle that combining low-correlation assets (covariance 0.158) can effectively manage unsystematic risk while maintaining a high risk-adjusted return. They also underscore the value of quantitative tools for small and medium-sized investors, who can dynamically rebalance their portfolios in response to changing market conditions and personal risk preferences. However, the study acknowledges limitations, including a reliance on historical data, potential discrepancies between theoretical optima and real-world implementation, and an exclusive focus on just two U.S. large-cap stocks. Future research could broaden the scope of asset classes, incorporate ESG factors, and examine different market scenarios. Overall, this study highlights MPT’s utility as both a theoretical framework and a practical guide, illustrating how disciplined diversification—combined with fundamental analysis—can balance growth and stability under constrained investment choices.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qi Zhang
PY  - 2025
DA  - 2025/08/14
TI  - Modern Portfolio Theory in Practice: Optimizing Risk-Return Trade-offs through a Case Study of Tesla and Procter & Gamble
BT  - Proceedings of the 2025 5th International Conference on Enterprise Management and Economic Development (ICEMED 2025)
PB  - Atlantis Press
SP  - 697
EP  - 705
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-811-0_73
DO  - 10.2991/978-94-6463-811-0_73
ID  - Zhang2025
ER  -