18 papers
This paper proposes that the fundamental irreversibility observed in both quantum mechanics and finance arises from a non-zero cubic term in the Taylor expansion of directed distance within a projective state space, which creates a measurable asymmetry under state exchange that explains phenomena ranging from the Second Law of Thermodynamics to the limitations of sequential traders.
This paper evaluates how three large language models (GPT, Gemini, and Llama) generate and adjust investor risk profiles in response to standardized questionnaires and persona-based prompts, revealing distinct baseline tendencies and varying degrees of adaptability across the models.
This paper introduces a novel benchmark framework using mathematically explicit portfolio optimization problems to evaluate the quantitative reasoning capabilities of large language models, revealing distinct performance patterns among GPT-4, Gemini 1.5 Pro, and Llama 3.1-70B in handling risk-based objectives, return-based tasks, and investment constraints.
This paper introduces the AlgoXpert Alpha Research Framework, a rigorous three-stage protocol (In-Sample, Walk Forward Analysis, and Out-of-Sample) enhanced with multi-layered defense mechanisms to systematically mitigate overfitting and validate quantitative trading strategies before live deployment.
This paper establishes a novel "Spectral Portfolio Theory" that identifies neural network weight matrices trained via stochastic gradient descent as portfolio allocation matrices, demonstrating how their spectral evolution from Marchenko-Pastur to inverse-Wishart statistics unifies diverse wealth dynamics models and yields a Spectral Invariance Theorem with applications in portfolio design, inequality measurement, and tax policy.
This paper proposes a distributed convex optimization protocol that enables portfolio managers to iteratively adjust their independent trades based on shared cost estimates, thereby significantly reducing market impact and transaction costs without requiring the disclosure of private trading strategies or direct communication between managers.
This paper proposes Generative Adversarial Regression (GAR), a minimax framework that learns conditional risk scenarios by training generators to align their policy-induced risk with real data across a broad class of policies, thereby outperforming existing baselines in preserving downstream risk metrics like Value-at-Risk and Expected Shortfall.
This paper introduces a Continuous Shariah Compliance Index (CSCI) that unifies diverse binary screening standards into a single transparent measure, demonstrating that while it enables flexible portfolio design with varying compliance intensity, it does not function as a new priced factor for explaining expected equity returns.
This paper provides the first complete characterization of optimal dynamic portfolio choice for monotone mean-variance utility in asset models with independent returns under minimal assumptions, establishing a link between maximal utility and the monotone Sharpe ratio while deriving conditions under which classical mean-variance efficient portfolios remain optimal.
This paper develops a general framework for dynamic, non-concave portfolio choice under smooth ambiguity and Bayesian learning, demonstrating how a robust representation of ambiguity aversion yields explicit trading rules and, in the context of delegated management, leads to more conservative risk-taking and reduced volatility.
This paper investigates the relationship between long-only global minimum variance portfolios and asset factor exposures, providing explicit analytical solutions for single-factor models and geometric descriptions for multi-factor scenarios, validated through empirical US stock data.
This paper introduces a generalized Bayesian framework using the Gibbs posterior to derive utility-consistent parametric portfolio policies without modeling the return generating process, and proposes a KNEEDLE algorithm to optimally select the data-weighting parameter in-sample, revealing that characteristic-based gains in U.S. equities (1955–2024) are concentrated pre-2000 and that the optimal weighting depends on risk aversion and higher-order moments.
This paper establishes a general cross-currency Heath-Jarrow-Morton framework within a multiple-curve setting that incorporates collateralization, arbitrary market indices, and instantaneous basis spreads to simultaneously model both forward-looking IBOR and backward-looking overnight rates for multi-currency interest rate portfolios.
This paper presents an explainable, regime-aware portfolio construction framework using a strictly causal Wasserstein Hidden Markov Model that dynamically adapts regime complexity while preserving economic interpretability, achieving superior risk-adjusted returns and lower drawdowns compared to traditional benchmarks and nonparametric alternatives.
This paper reformulates the concept of neutral wealth taxation as a drift-shift symmetry in stochastic wealth dynamics, demonstrating that while a proportional tax preserves the underlying statistical structure of wealth distribution, practical implementation failures correspond to specific violations of this symmetry that introduce new, non-neutral physical effects.
This paper extends Froeseth's (2026) wealth tax neutrality framework by demonstrating that while neutrality holds under stochastic volatility and Epstein-Zin preferences, it breaks under non-homothetic preferences and four specific implementation channels—such as progressive thresholds and inelastic markets—which are formalized and calibrated to the Norwegian system to evaluate global minimum tax proposals.
This paper demonstrates that a proportional wealth tax levied on market value is economically equivalent to the government acquiring a proportional stake in investor portfolios, thereby leaving optimal portfolio weights, asset pricing, and the Sharpe ratio unchanged, provided that taxation is universal, markets are frictionless, and dividends are consumed.
This paper proposes a reinforcement learning framework using Proximal Policy Optimization (PPO) to dynamically weight LLM-generated trading alphas, demonstrating that while it may not always maximize cumulative returns, it significantly improves risk-adjusted performance and stability compared to traditional baseline strategies.