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 proves that an investor's Epstein-Zin preferences for dividends are mathematically equivalent to a firm executive adopting a robust dividend policy under Maenhout’s ambiguity-averse preferences, resulting in a threshold-based dividend strategy.
This paper proposes an extension of Hidden Markov Models that incorporates macroeconomic covariates via an auxiliary variable to effectively model the time-dependent heterogeneity and stress-test relationships in operational risk losses, utilizing the Expectation-Maximization algorithm for calibration and validation across various risk-event types.
This paper utilizes a differential equation model and extreme value techniques to demonstrate that supply limitations have decoupled Australian house prices from mortgage rates, indicating that an 11% rate hike is required to curb extreme costs unless housing supply is increased through deregulation or reduced government competition.
This AI-generated paper proposes that inflated AI stock valuations arise because investors use them to hedge against a future singularity that displaces their consumption, a market distortion caused by the inability to trade private AI capital that ultimately justifies government intervention.
This paper demonstrates that autonomous LLM agents in simulated asset markets exhibit classic behavioral biases and aggregate into bubble dynamics similar to human markets, while showing that targeted prompt interventions can causally manipulate these behaviors to alter bubble magnitudes.
QRAFTI is a multi-agent framework designed to emulate a quantitative research team by integrating panel data toolkits with MCP servers to facilitate equity factor research, enabling the replication of established factors, testing of new signals, and generation of standardized reports with narrative analysis and computational traces.
This paper presents a novel framework that combines high-frequency market data with LLM-classified news narratives to identify interpretable around-the-clock systematic jump risks, demonstrating that macroeconomic-driven jumps command the highest risk premium and can be effectively exploited through a real-time factor-mimicking portfolio.
This paper introduces a Quantile Bayesian Vector Autoregression (QBVAR) model that significantly improves oil price forecasts, particularly for downside risk and median predictions, by capturing quantile-specific dynamics and asymmetries that traditional mean-focused models overlook.
This paper introduces the JFR-rg framework to explain Japan's stabilized high-debt, low-growth economy under financial repression, proposing a geometric stability model and warning that aggressive rate normalization could trigger a debt trap while suggesting that strategic investment of the "repression dividend" offers a viable equilibrium path.
This paper introduces a high-precision AI patent classifier to reveal that while the United States and China exhibit converging AI patenting growth and market value premiums, they differ significantly in organizational structures—with the U.S. dominated by large private firms and China by diverse institutions—and remain technologically interdependent through cross-border knowledge flows.