Minimum-Distortion Wealth Taxation, I: Information-Theoretic versus Transport-Geometric Optimality on the Proportional Class

Battle Count: 57

The paper is primarily about tax policy design and normative optimal taxation theory, not about trading strategies or market microstructure. However, it uses GBM dynamics and Fokker-Planck equations that are foundational in quantitative finance. The effective-volatility calibration for heterogeneous portfolios (Section 7.2) has tangential relevance to portfolio risk assessment. The bluntness index mechanism (Section 5.5) provides insight into how wealth taxes differentially affect high-return vs. low-return assets, which could inform after-tax portfolio optimization. Overall relevance to active quantitative trading is minimal.

The core analytical results (Theorems 1-4) are closed-form and computationally trivial to evaluate given GBM parameters and revenue weights. The phase diagram construction requires solving implicit equations for rho_low and rho_high. The effective-volatility calibration (Section 7.2) requires portfolio composition data. The main complexity lies in understanding the Fokker-Planck framework, the JKO gradient-flow interpretation, and the variational optimization structure. The bracket extension (companion paper) would significantly increase complexity via piecewise-Gaussian matching conditions and error-function evaluations.

The paper provides fully closed-form analytical solutions (Theorems 1-4) with explicit calibration parameters (mu=0.07, sigma=0.30, T=5, v0=0.25, a=1.0, b=0.27, R*=0.05). All phase boundaries and optima are computable from these inputs. However, the empirical calibration pipeline is described as a 'forthcoming companion paper' using a synthetic-data prototype, and the full log-quadratic revenue functional is only partially specified. No code or data repository is provided. The mathematical derivations are self-contained and verifiable.