84 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 Systematization of Knowledge paper introduces the CLEAR framework to demonstrate that while stablecoins offer efficient, programmable, and continuous settlement, they currently lack the consumer protection and standardized risk-allocation mechanisms of card networks, limiting their viability as open-loop retail payment instruments to specific contexts like cross-border transactions and closed-loop environments.
This paper investigates the impact of the EU's MiCA regulation on stablecoin gateways, finding that while aggregate market metrics remain stable, the regulation successfully shifts trading volume and market share from the delisted USDT to the authorized USDC on regulated European exchanges.
This paper introduces a two-stage, fine-grained event extraction system for SEC 8-K filings that leverages large language models to generate over 600,000 grounded event tags with verbatim citations and calibrated quality scores, demonstrating that these labels can distinguish economically distinct events and achieve high precision when filtered by quality.
This paper leverages a unique Austrian regulatory registry to analyze transaction-level data from crypto-asset service providers, revealing that while these entities facilitate roughly $30 billion in globally integrated flows dominated by institutional counterparties, their distinct responses to major financial shocks—such as the SVB failure—demonstrate that stablecoins do not function as a uniform safe haven and that such nuanced risk transmission patterns remain invisible in aggregate data.
This paper introduces a bridge-alpha curve diagnostic to evaluate factor models across entire characteristic axes rather than isolated deciles, revealing that while value and investment factors exhibit systematic pricing errors and sign reversals in CRSP data from 1967 to 2024, profitability and momentum factors largely flatten their axes, with these pricing errors proving nearly orthogonal to maximum-Sharpe gains.
This paper argues that published academic equity anomalies have become practically useless for non-micro-cap portfolio managers in the 21st century, as their returns have diminished to negligible levels after 2005 when accounting for transaction costs and luck, indicating that public stock markets are highly efficient.
This study analyzes 832,941 Solana pump.fun token launches to reveal a sharp 3.18x decline in graduation rates between late 2025 and mid-2026, attributing the drop primarily to a shift toward zero-self-buy tokens while demonstrating that social-channel presence (particularly Telegram) significantly boosts success odds.
This paper challenges the scalability of Blandhol's (2025) estimate that wealth-tax-induced emigration reduces Norway's long-run GDP by 1.3% by demonstrating through a social contagion tipping-point model and new panel data that the underlying micro-to-macro extrapolation fails due to violated identification conditions, hidden heir-emigration channels, and the dominance of non-productive wealth holders in the sample.
This paper introduces a cap-axis integral diagnostic that evaluates factor models by analyzing pricing errors along the market-capitalization rank axis, revealing distinct insights into model performance and zero-alpha violations that traditional metrics like the Sharpe ratio fail to capture.
Using a massive proprietary dataset of 380 trillion AI tokens, this paper identifies a significant and heterogeneous "AI Premium" where firms with high exposure to advanced, closed-source AI models and interactive work skills earn substantial subsequent stock returns, while such premiums are absent in emerging markets and open-weight or casual AI usage.