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 demonstrates that in sender-receiver games, costly deviations from privately observed anchors can enhance information transmission by generating continuous messages and enabling uninformative anchors to convey strategic distortions, potentially allowing organizations to achieve near-full revelation even with minimal reporting costs.
This study utilizes a unified ARDL-ECM framework with PCA-derived indices to demonstrate that external demand and domestic monetary conditions significantly drive Nepal's remittance inflows, revealing a stable long-run relationship where tighter monetary policy reduces remittances while external demand boosts them, with projections indicating remittances will reach approximately 28.3% of GDP by 2030.
This paper proposes a novel generative framework that shifts emotion analysis from discrete classification to continuous intensity evaluation (0–100), demonstrating superior performance and generalization in domains like finance where the degree of emotional content is critical for decision-making.
This paper argues that the combination of discrete block-time clearing and paid-priority transaction ordering in blockchain markets creates endogenous participation cutoffs that intensify with competition, ultimately undermining price discovery, exacerbating adverse selection, and potentially causing markets to shut down.
This paper presents the first rigorous study of electricity price volatility across Germany, Norway, and Spain by employing a stochastic partial differential equation framework to estimate weekly integrated variance, revealing that while volatility drivers and generation impacts vary significantly by zone, apparent leverage effects are actually explained by conditioning on state variables rather than representing genuine asymmetric responses to price shocks.
This paper utilizes a multi-level connectedness framework to demonstrate that European insurers play a critical role in systemic risk, particularly during stress periods, by linking their spillovers to macroeconomic factors and revealing significant heterogeneity across subsectors and a stable core of systemically central firms.
This paper presents a formal taxonomy of seven canonical variants of event-linked perpetual futures beyond the single-market binary case, analyzing their unique payoff structures, design constraints, and inheritance from a foundational risk framework across four orthogonal design axes.
This paper presents a theoretical framework and regulatory synthesis analyzing how leverage in event-linked prediction markets asymmetrically alters incentives for market-price and outcome manipulation while amplifying informed-trading rents, ultimately offering a re-allocation of attack surfaces and 14 targeted recommendations for stakeholders.
This study demonstrates that increased corporate transparency significantly reduces the disposition effect by boosting investor confidence in profitable stocks and fostering a belief in the temporary nature of losses, thereby encouraging investors to hold onto winning assets longer and sell losing ones more readily.
Using data from the Chinese social investment platform Xueqiu, this study demonstrates that social media information, particularly negative news, significantly mitigates individual investors' disposition effect by fostering more rational trading behavior, with the magnitude of this impact varying based on investor characteristics such as experience, network size, region, and gender.