79 papers
This collection explores the cutting edge of Q-Fin — Pm, a dynamic intersection where quantum computing principles meet portfolio management strategies. Researchers are rapidly developing algorithms that leverage quantum mechanics to optimize investment decisions, potentially solving complex financial problems far faster than classical computers ever could. These early studies offer a glimpse into a future where financial modeling transcends current computational limits.
Every new preprint in this category arrives directly from arXiv, where scientists first share their raw findings with the world. At Gist.Science, we process each of these submissions to provide both detailed technical breakdowns for experts and clear, plain-language summaries for anyone curious about the next generation of finance. Below are the latest papers in this emerging field, distilled to help you understand how quantum theory is reshaping the way we manage wealth.
This paper extends the Fokker-Planck framework to populations with heterogeneous, persistent return-generating abilities, demonstrating that while proportional wealth taxes act as a uniform drift shift, they lose economic neutrality by altering the stationary wealth distribution's shape and differentially impacting investors based on their ability types.
This paper establishes a martingale-based framework for continuous-time risk-sensitive reinforcement learning that incorporates a quadratic variation penalty to capture value variability, enabling the adaptation of existing algorithms and proving convergence for Merton's investment problem while demonstrating improved finite-sample performance in linear-quadratic control.
This paper proposes a Market-Implied Sustainability (MIS) framework that derives firm-level sustainability scores from the portfolio holdings of SFDR Article 9 funds, demonstrating that these market-based metrics capture distinct dimensions of sustainability compared to traditional ESG ratings and offer superior risk-adjusted performance in portfolio tilting strategies.
This paper proposes a novel algorithm for calculating risk budgeting portfolios by constructing a Cauchy sequence within the simplex, which avoids computationally challenging auxiliary optimization problems while providing a fixed-point framework to establish existence and uniqueness conditions for general risk measures.
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 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 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 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 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 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.