Sphere-sphere intersection for investment portfolio diversification - A new data-driven cluster analysis

Area of Science:

• Quantitative Finance
• Financial Data Analysis
• Investment Strategies

Background:

• Traditional portfolio diversification methods can be arbitrary, especially during market volatility.
• A data-driven approach is needed for precise measurement of stock similarity.
• Identifying equity market clustering patterns is crucial for effective asset allocation.

Purpose of the Study:

• To propose a new, data-driven cluster analysis for investment portfolio diversification.
• To provide a less arbitrary method for measuring similarity between equity stocks.
• To unveil equity market clustering patterns, particularly during high volatility periods.

Main Methods:

• Application of analytic geometry solutions to compare publicly traded companies.
• Development of a novel cluster analysis focusing on risk-similarity.
• Calculation of an overall clustering pattern indicator.

Main Results:

• Empirical results on synthetic data show conceptual superiority over traditional cluster analyses.
• The proposed method demonstrates practical usefulness for asset allocation and portfolio strategy.
• Sphere-sphere intersection calculations are shown to benefit portfolio diversification.

Conclusions:

• The new cluster analysis offers a more objective and data-driven approach to portfolio diversification.
• The method effectively identifies market clustering patterns and stock similarities.
• This approach enhances decision-making for financial investors, especially in volatile markets.