78 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 introduces FinBench, a novel benchmark designed to evaluate the probabilistic calibration and uncertainty quality of agentic financial forecasting models by enforcing strict time-gating and utilizing proper scoring rules to penalize overconfidence and hallucinated certainty.
This paper analyzes thirty U.S.-listed Taiwan-exposed ETFs from 2015 to 2025 to demonstrate that while semiconductor concentration drives heavy-tailed risks and asymmetric volatility, tail-risk optimization (CVaR) yields more concentrated portfolios and different performance rankings compared to traditional mean-variance frameworks, highlighting the insufficiency of variance-based models for technology-concentrated investments.
This paper proposes a novel robust mixed integer linear programming model for the index tracking problem under no rebalancing, accompanied by a hybrid heuristic algorithm that combines genetic and local search capabilities to efficiently solve the NP-hard problem and outperform commercial solvers on both in-sample and out-of-sample data.
This paper proposes enhanced multi-objective evolutionary algorithms featuring novel solution representations, operators, and repair mechanisms to efficiently solve large-scale portfolio optimization problems under cardinality constraints, demonstrating faster convergence and superior performance compared to traditional methods as market complexity increases.
This paper models wealth redistribution as an optimal control problem for the Fokker-Planck equation, demonstrating that while proportional taxes act as a non-distortionary but non-redistributive "gravitational" drift, progressive taxes function as a "confining potential" that effectively reduces inequality by reshaping the wealth distribution's tail, with optimal policy balancing redistribution speed against economic costs in a self-consistent general equilibrium framework.
This paper demonstrates through a two-stage S&P 500 analysis that relaxing cardinality constraints improves tracking accuracy and risk-adjusted returns, whereas a tighter constraint of 10 to 20 assets is optimal for portfolio enhancement.
This paper proposes an adaptive partitioning model-based reinforcement learning algorithm for controlled diffusion processes in unbounded continuous state spaces, establishing regret bounds that depend on a novel zooming dimension and demonstrating effectiveness in high-dimensional financial applications like multi-asset portfolio selection.
This paper introduces a spectral generalization of the variance ratio to identify a robust five-factor model that decomposes long-horizon equity returns into distinct return and volatility memory channels, revealing a late-1980s regime shift in volatility persistence and demonstrating that the cross-sectional drivers of return momentum are economically distinct from those governing volatility persistence.
This paper argues that the post-2009 collapse of short-term trend-following profitability is driven by a microstructural shift where high-frequency market makers' liquidity withdrawal in small-tick contracts disrupts the self-fulfilling feedback loop between trend signals and price impact, whereas large-tick contracts remain insulated due to sufficient residual order book depth.
This paper employs a purely probabilistic duality approach to derive the optimal portfolio strategy and value function for an exponential utility maximization problem involving an Ornstein-Uhlenbeck risky asset subject to linear temporary price impact.