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 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 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 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.
This paper proposes a stochastic model predictive control framework that optimizes stablecoin reserve portfolios by dynamically balancing cash liquidity and bill yields through a soft-thresholding rebalancing policy and mint/burn fees, ensuring peg stability during stress events while maximizing yield in calm markets.
This paper demonstrates that machine learning models integrating high-frequency Twitter sentiment and volatility data can effectively predict and monetize short-term overreactions in Apple (AAPL) stock, revealing that negative emotions and volatility drive intraday momentum patterns that outperform traditional rules at ultra-short horizons.
This paper addresses the time inconsistency in Merton's portfolio management problem caused by non-constant discount rates by deriving subgame perfect strategies that, under specific asymptotic conditions, coincide with the optimal strategy when the discount rate is replaced by a wealth- and time-dependent utility-weighted rate found via fixed point iteration.
This paper provides an explicit solution to continuous-time optimal portfolio choice problems involving transient cross-impact and temporary price impact by reducing the first-order conditions to a system of stochastic Fredholm equations, thereby deriving optimal strategies that balance revenue, risk, and alpha signals while ensuring the absence of price manipulation.
This paper proposes a data-driven approach to solving Merton's expected utility maximization problem in an incomplete market with unknown primitives by introducing policy randomization within a continuous-time reinforcement learning framework, which enables the derivation of optimal strategies through actor-critic algorithms without requiring explicit model estimation.
This paper introduces Recursive Perturbed Utility (RPU) to resolve the intractability of dynamic randomization preferences, demonstrating that in a Markovian incomplete market, the optimal portfolio policy is Gaussian with a closed-form variance and a mean policy that blends myopic and hedging components while quantifying the minimal financial cost of preferring randomized decisions over the classical Merton solution.
This paper proposes and validates an adaptive, ESG-constrained portfolio optimization framework in large-dimensional settings that leverages random matrix theory to derive asymptotic out-of-sample Sharpe ratio estimators, ultimately demonstrating through S&P 500 empirical evidence that the approach effectively balances sustainable investment goals with high risk-adjusted returns.