An alternative strategy for balancing profit maximization and risk reduction

Area of Science:

• Quantitative Finance
• Investment Theory
• Portfolio Management

Background:

• Portfolio diversification is a cornerstone of modern investment theory.
• The traditional return-risk trade-off is a key consideration for investors.
• Existing models like Markowitz offer frameworks but can be sensitive to estimation errors and market conditions.

Purpose of the Study:

• To propose and evaluate an alternative portfolio optimization objective that directly penalizes volatility.
• To assess the performance of this new objective against established benchmarks using empirical data.
• To determine if the proposed objective offers improved risk-adjusted returns and downside protection.

Main Methods:

• Developed a novel objective function balancing expected returns with inverse standard deviation (volatility penalty).
• Derived tractable solutions, including a closed-form solution for the two-asset case.
• Conducted out-of-sample backtests on Fama-French equity portfolios incorporating trading costs and long-only constraints.

Main Results:

• The proposed objective significantly reduced realized volatility (p < 0.01) and maximum drawdowns compared to the Sharpe-ratio maximizing benchmark.
• Performance remained comparable to Markowitz, equal-weight, and risk-parity strategies in terms of average returns.
• The benefits were observed across various market stress periods (GFC, COVID-19, 2022 rate shock) and were robust to the trade-off parameter.

Conclusions:

• The novel objective function offers a pragmatic approach for investors prioritizing smoother wealth accumulation and robust downside protection.
• It presents a viable alternative to traditional mean-variance optimization, especially in volatile market conditions.
• The strategy demonstrates resilience and effectiveness during significant economic downturns.