q-fin.PM papers | Gist.Science

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.

🤖 machine learning

Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training

This paper proposes a semi-supervised teacher-student learning framework that leverages CVaR-optimized labels and synthetic t-copula augmented data to train robust Bayesian and deterministic models for portfolio optimization, demonstrating their ability to outperform traditional methods in data-scarce environments and under regime shifts.

Adhiraj Chattopadhyay2026-04-04✓ Author reviewed ⓘ

💰 quantitative finance

The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management

This paper presents an agentic AI framework for institutional asset management where approximately 50 specialized agents collaboratively generate, construct, and critique portfolios under the governance of an Investment Policy Statement, while a meta-agent autonomously rewrites their code and prompts to continuously improve performance based on realized returns.

Andrew Ang, Nazym Azimbayev, Andrey Kim2026-04-03

💰 quantitative finance

Bridging Stochastic Control and Deep Hedging: Structural Priors for No-Transaction Band Networks

This paper bridges stochastic control and deep hedging by deriving optimal no-transaction bands for hedging European call options under transaction costs and demonstrating that a deep learning architecture incorporating the Whalley-Wilmott asymptotic formula as a structural prior outperforms standard approaches in convergence, accuracy, and generalization.

Jules Arzel, Noureddine Lehdili2026-04-01

💰 quantitative finance

Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study

This paper proposes a continuous-time reinforcement learning framework for mean-variance portfolio selection that learns optimal investment strategies directly from data without estimating unknown market coefficients, demonstrating through theoretical regret bounds and extensive empirical studies on S&P 500 constituents that it consistently outperforms traditional model-based approaches, particularly in volatile bear markets.

Yilie Huang, Yanwei Jia, Xun Yu Zhou2026-03-31

💰 quantitative finance

Carbon-Penalised Portfolio Insurance Strategies in a Stochastic Factor Model with Partial Information

This paper derives optimal proportional portfolio insurance strategies that penalize carbon emissions within a stochastic factor model under partial information, demonstrating through numerical analysis that such approaches can reduce carbon intensity without compromising financial performance.

Katia Colaneri, Federico D'Amario, Daniele Mancinelli2026-03-25

💰 quantitative finance

Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction

This paper proposes a deep neural network framework that jointly models expected returns and covariance matrices to enable end-to-end learning of dynamic market structures, demonstrating superior risk-adjusted portfolio performance compared to traditional methods under nonstationary conditions.

Keonvin Park2026-03-23

💰 quantitative finance

Quantum-Assisted Optimal Rebalancing with Uncorrelated Asset Selection for Algorithmic Trading Walk-Forward QUBO Scheduling via QAOA

This paper presents a hybrid classical-quantum framework that formulates portfolio rebalancing as a QUBO problem solved by QAOA, demonstrating that this approach reduces transaction costs by 44.5% through fewer rebalancing events while achieving a competitive Sharpe ratio of 0.588 on S&P 500 data.

Abraham Itzhak Weinberg2026-03-19

💰 quantitative finance

Can Blindfolded LLMs Still Trade? An Anonymization-First Framework for Portfolio Optimization

This paper introduces "BlindTrade," an anonymization-first framework that validates LLM trading agents' ability to capture genuine market dynamics rather than memorized ticker associations, demonstrating robust performance with a Sharpe ratio of 1.40 in volatile 2025 market conditions while revealing regime-dependent limitations in trending bull markets.

Joohyoung Jeon, Hongchul Lee2026-03-19

📈 economics

P vs NP Problem in Portfolio Optimization: Integrating the Markowitz-CAPM Framework with Cardinality Constraints and Black-Scholes Derivative Pricing

This paper operationalizes the P vs NP problem in quantitative finance by demonstrating how cardinality constraints transform the convex Markowitz-CAPM portfolio optimization into an NP-hard mixed-integer quadratic program, evaluating scalable approximation schemes and integrating Black-Scholes derivative pricing to analyze the resulting trade-offs between computational complexity, solution stability, and the reshaped efficient frontier.

Davit Gondauri2026-03-18

📈 economics

Flow Taxes, Stock Taxes, and Portfolio Choice: A Generalised Neutrality Result

This paper demonstrates that a comprehensive system of ownership taxes preserves portfolio neutrality by acting as a uniform drift shift and rescaling of wealth dynamics, provided that corporate and capital income tax rates are aligned, shielding mechanisms match the risk-free rate, and wealth tax assessments are uniform, with deviations from these conditions generating separable distortions where non-uniform wealth taxation dominates flow-tax effects.

Anders G Frøseth2026-03-18

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