Clustering based on Stochastic Dominance with application for risk averters and risk seekers
arXiv:2605.24422v1 Announce Type: cross Abstract: Stochastic Dominance (SD) theory provides a rigorous framework for selecting superior assets tailored to the asset allocation needs of investors with varying risk preferences (i.e., risk-averse, risk-seeking, and risk-neutral). However, traditional stock clustering methods typically rely on geometric metrics such as Euclidean distance, which often fail to effectively capture the intrinsic risk dominance relationships among assets. To address this limitation, this paper proposes an innovative clustering analysis framework based on SD test statis
The paper leverages recent advancements in sophisticated AI/ML techniques to address a long-standing challenge in financial modeling and risk assessment previously limited by traditional statistical methods.
This development allows for more nuanced and accurate asset allocation and risk management, particularly for investors with complex risk preferences, moving beyond simpler statistical approaches.
Clustering methods for financial assets can now incorporate investor-specific risk dominance, leading to potentially more efficient portfolios and better-aligned investment strategies.
- · Quantitative finance firms
- · Asset managers
- · High-net-worth individuals
- · AI/ML in finance developers
- · Traditional statistical clustering methods
- · Investors relying solely on geometric distance metrics
Improved asset clustering based on risk preferences will lead to more tailored investment products.
Increased adoption of stochastic dominance in AI-driven financial models could attract more capital to sophisticated quantitative strategies.
The enhanced accuracy in risk modeling might contribute to greater financial market stability by better matching assets to risk appetites.
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Read at arXiv cs.LG