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 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.
Using high-frequency data from 2020 to 2025, this study constructs time-dependent weighted directed networks based on Granger causality to reveal a dynamically evolving hierarchy in cryptocurrency markets where Ethereum consistently surpasses Bitcoin as the most influential asset, highlighting the ecosystem's competitive and non-stable nature.
This paper introduces a dynamic portfolio framework where investors target specific regions of the payoff distribution, demonstrating that policies focused on the downside offer superior risk-adjusted returns and tail protection, while those targeting the upper quantile maximize mean returns, with performance gains concentrated during periods of high downside-tail dispersion.
This paper demonstrates that while factor models may successfully price the aggregate market, they often fail to price its internal components, as evidenced by systematic, offsetting pricing errors between size-ranked "body" and "tail" portfolios that cancel out in the aggregate.
This paper demonstrates that the performance rankings of factor models are significantly dependent on test-asset construction choices, such as selection, weighting, and rebalancing strategies, rather than being solely determined by the models themselves.
This paper introduces a statistical procedure called Lookahead Propensity (LAP) to detect and quantify lookahead bias in large language model economic forecasts, demonstrating that the models' predictive power is significantly driven by their internalization of future information available only in their training data.
This paper demonstrates that large language models, when prompted to role-play as specific corporate CFOs, can accurately reproduce and forecast human responses to economic sentiment surveys, offering a scalable and high-frequency alternative to traditional, costly data collection methods.
This paper proposes a parsimonious matrix-HAR model to forecast weekly realized covariation in electricity markets, demonstrating that incorporating longer time horizons and renewable generation data significantly improves both covariation and spread risk premia predictions compared to standard methods.
This study applies average linkage hierarchical clustering to corruption perception data from 134 countries, revealing four distinct clusters that demonstrate a strong correlation between corruption levels and economic development stages.