Novel Risk Measures for Portfolio Optimization Using Equal-Correlation Portfolio Strategy

TL;DR

This paper introduces new risk measures based on equal-correlation strategies to improve diversification and stability in portfolio optimization, addressing limitations of traditional covariance-based methods.

Contribution

It proposes a novel mathematical framework for equal-correlation portfolios that explicitly controls correlation and enhances diversification over traditional methods.

Findings

Portfolios using the new risk measures show better diversification.

The approach yields more stable returns across market conditions.

Empirical validation confirms improved risk management.

Abstract

Portfolio optimization has long been dominated by covariance-based strategies, such as the Markowitz Mean-Variance framework. However, these approaches often fail to ensure a balanced risk structure across assets, leading to concentration in a few securities. In this paper, we introduce novel risk measures grounded in the equal-correlation portfolio strategy, aiming to construct portfolios where each asset maintains an equal correlation with the overall portfolio return. We formulate a mathematical optimization framework that explicitly controls portfolio-wide correlation while preserving desirable risk-return trade-offs. The proposed models are empirically validated using historical stock market data. Our findings show that portfolios constructed via this approach demonstrate superior risk diversification and more stable returns under diverse market conditions. This methodology offers a compelling alternative to conventional diversification techniques and holds practical relevance for institutional investors, asset managers, and quantitative trading strategies.