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 extends dematerialization theory by incorporating technical progress and the rebound effect, then empirically demonstrates that across 57 cases, technological improvements have failed to offset rising consumption, thereby refuting the notion that unfettered technological change alone can achieve dematerialization.
This paper analyzes the sectoral impact of Information and Communications Technology (ICT) on employment in India, framing ICT as a transformative General Purpose Technology that has significantly increased investment intensity across various industries.
This paper argues that scientific research is primarily driven by the socioeconomic interests of nations, such as the pursuit of power and wealth, rather than philosophical inquiry, and is sustained by organized social efforts to address environmental threats and seize opportunities.
This paper develops and quantifies a dynamic stochastic model of cap-and-trade markets to demonstrate how the interaction of slow participation, limited intermediation, and heterogeneous information creates a unique equilibrium premium that non-additively amplifies price responses, using 2.7 million EU ETS transactions to reveal that roughly 40% of operators do not trade annually and that purchases concentrate in April when returns are systematically high.
This paper demonstrates that while put-call parity holds in terminal payoff, daily settlement and capital constraints create a systematic, path-dependent implementation wedge between option-implied discount factors and the OIS curve, which is quantitatively explained by volatility-driven carry costs and trading frictions.
This paper demonstrates that the pricing gap between the SPX and RUT, traditionally viewed through risk-neutral lenses, is significantly explained by residual physical-measure investment opportunities captured by global assets (IEFA, IGOV, IAU), suggesting that finite-capital put-call parity enforcement reflects real-world investment dynamics rather than simple arbitrage failures.
This paper provides empirical evidence that a systematic wedge in put-call parity, driven by finite arbitrage capital, reveals the physical drift of the underlying asset influencing the enforcement of risk-neutral parity rather than the option payoffs themselves.
This paper introduces a robust daily-frequency benchmark for post-GFC covered interest parity deviations using three lagged public state variables (NFCI, the U.S. dollar index, and the Treasury yield curve slope) that effectively captures persistent background components across G10 and KRW currency-tenor panels.
This paper argues that the widespread adoption of AI in investment strategies creates a self-defeating cycle of signal homogenization and accelerated decay, ultimately eroding excess returns, triggering extinction cascades, and increasing systemic fragility despite improved price discovery.
This paper introduces a novel Piecewise Deterministic Markov Process (PDMP) framework that extends a deterministic tax evasion model by incorporating stochastic audit and imitation mechanisms, demonstrating that their interaction prevents extreme equilibria and instead generates persistent fluctuations around a stationary distribution that better reflects real-world compliance dynamics.