Paper: arXiv 2510.06986

Authors: Jinho Cha, Long Pham, Thi Le Hoa Vo, Jaeyoung Cho, Jaejin Lee

Abstract

This study develops an inverse portfolio optimization framework for recovering latent investor preferences including risk aversion, transaction cost sensitivity, and ESG orientation from observed portfolio allocations. Using controlled synthetic data, we assess the estimator’s statistical properties such as consistency, coverage, and dynamic regret. The model integrates robust optimization and regret-based inference to quantify welfare losses under preference misspecification and market shocks. Simulation experiments demonstrate accurate recovery of transaction cost parameters, partial identifiability of ESG penalties, and sublinear regret even under stochastic volatility and liquidity shocks. A real-data illustration using ETFs confirms that transaction-cost shocks dominate volatility shocks in welfare impact. The framework thus provides a statistically rigorous and economically interpretable tool for robust preference inference and portfolio design under uncertainty.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a solid extension of inverse optimization to finance, jointly recovering risk aversion, transaction costs, and ESG preferences under uncertainty. It balances theoretical optimization formulations (KKT, bilevel programming, dynamic regret) with synthetic and real ETF empirical tests.

Research Flowchart

  flowchart TD
    A[Research Goal: Recover Latent Investor Risk, ESG, & Cost Preferences under Uncertainty] --> B[Inputs: Synthetic Data & Real ETF Market Data]
    B --> C[Methodology: Inverse Portfolio Optimization & Regret-Based Inference]
    C --> D[Computational Simulations: Robust Optimization under Stochastic Volatility & Liquidity Shocks]
    D --> E[Outcomes: Sublinear Regret & Transaction-Cost Shocks Dominating Volatility in Welfare Impact]

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0 of 63 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.