86 papers
This collection explores the cutting edge of Q-Fin — Gn, where advanced quantum computing principles intersect with financial modeling and game theory. These emerging studies investigate how quantum algorithms can solve complex economic problems and optimize strategic interactions far beyond the reach of classical computers, offering a glimpse into a future where financial markets operate with unprecedented speed and precision.
Every new preprint in this category originates from arXiv, the premier repository for physics and computer science research. At Gist.Science, we process each submission to provide both accessible plain-language explanations and detailed technical summaries, ensuring these breakthroughs are understandable to everyone from industry experts to curious students. Below are the latest papers in this rapidly evolving field, curated to keep you ahead of the curve.
This paper argues that the pursuit of unidirectional causal reduction in empirical finance is fundamentally flawed due to the self-referential nature of capital markets, suggesting instead that quantitative tools are best suited for ex post inference and that alternative frameworks acknowledging competing causal chains and reflexivity are necessary.
This paper empirically evaluates the Deadline-Resolved Information Leakage Score (ILS-dl) on Polymarket's documented insider trading cases, specifically within the 2026 U.S.-Iran conflict cluster, demonstrating the metric's ability to distinguish genuine signal from proxy artifacts while validating its exponential-hazard fit for military-geopolitics markets and identifying limitations in wallet traceability.
This paper argues that three distinct methodological approaches to detecting informed trading in decentralized prediction markets—account-level skill screening, heuristic insider flagging, and per-market information leakage scoring—are complementary layers of detection rather than competing methods, and demonstrates how integrating them improves precision through a combined pipeline.
This paper introduces the Cycles Protocol, a distributed multilateral clearing mechanism that utilizes double-entry accounting and atomic cycle execution to compress balance sheets and integrate trade credit into formal settlement without relying on novation, thereby deepening secondary market liquidity and extending clearing capabilities to real-economy financing.
This paper introduces Foresight Arena, a permissionless, on-chain benchmark that evaluates AI forecasting agents on real-world prediction markets using trustless smart contracts and proper scoring rules to rigorously measure predictive accuracy while preventing overfitting and data contamination.
This paper evaluates the Information Leakage Score framework across 12,708 Polymarket markets from 2020 to 2026, revealing that its effective application is severely limited by resolution ambiguity and low anchor-sensitivity, thereby demonstrating that detecting informed flow requires methodological refinements in resolution typology and baseline correction rather than just score computation.
This paper introduces ForesightFlow, an Information Leakage Score framework for detecting informed trading in prediction markets, and proposes a deadline-anchored extension to address the methodological limitations revealed by empirical evaluations of public-event timestamps and documented insider trading cases.
The paper argues that the assumption of a unique, continuous global time in financial modeling is flawed due to the asynchronous, event-driven nature of markets, suggesting that this "clock mismatch" creates a fundamental form of market incompleteness that necessitates distinguishing between high-frequency operational time and long-term calendar time.
This paper provides a comprehensive microstructure analysis of Polymarket using a massive tick-level dataset, revealing several stylized facts about liquidity and trading behavior while demonstrating that trade direction inferred from the public order-book feed is highly unreliable compared to authoritative on-chain data.
This paper presents a novel, explicit formula for Black-Scholes implied volatility by expressing the call price as a survival probability of an inverse Gaussian distribution, providing a direct solution that is faster and more precise than existing iterative or approximate methods.