68 papers
This paper extends concentration inequality theory to simple random tensors with heavy-tailed sub-Weibull coefficients by establishing bounds that reveal a phase transition between sub-Gaussian and heavy-tailed regimes, utilizing a new Generalized Maximal Inequality and Nagaev-type martingale analysis.
This paper challenges the conventional wisdom that diversification always reduces risk by demonstrating that for heavy-tailed losses with infinite means, a diversified portfolio can stochastically dominate a "one-basket" benchmark, thereby increasing the probability of exceeding any given loss threshold.
This paper proposes a continuous-time stochastic extension of the Gordon-Loeb model that incorporates Hawkes processes to capture attack clustering, demonstrating through dynamic programming that accounting for such clustering yields more responsive and effective cybersecurity investment policies compared to traditional static or Poisson-based approaches.
This paper derives explicit solutions for unconstrained and counter-monotonic inf-convolutions among agents with heterogeneous distortion risk measures, including risk-seeking preferences, showing that these optimal allocations can be represented by a generalization of distortion risk measures.
This paper presents a unified framework that reconstructs the multilayer bank-firm network structure of the Italian economy from balance sheet data to simulate shock propagation and identify systemic risks, enabling detailed stress testing without relying on inaccessible, privacy-protected network information.
This paper proposes that the fundamental irreversibility observed in both quantum mechanics and finance arises from a non-zero cubic term in the Taylor expansion of directed distance within a projective state space, which creates a measurable asymmetry under state exchange that explains phenomena ranging from the Second Law of Thermodynamics to the limitations of sequential traders.
This paper characterizes the approximate Nash equilibrium of competing decentralized exchanges setting dynamic fees, revealing that while the two-regime fee structure persists, competition shifts the fee-switching boundary to a weighted average of oracle and competitor rates, ultimately reducing slippage for strategic traders while having activity-dependent effects on noise traders.
Using a panel dataset of over 19,000 observations, this study reveals that transgender individuals report lower subjective well-being and health, exhibit less support for women's empowerment and gender-related statements, rely more on parental and teacher opinions for career decisions, and display higher levels of distrust compared to non-transgender people, with findings on gender attitudes and decision-making diverging from progressive expectations.
Using individual-level panel data from 2016 to 2024, this study reveals that the COVID-19 pandemic significantly shifted societal attitudes toward women's active work, resulting in a general increase in positive views across genders, a reversal where women became more supportive than men, and a notable change in perspective among older individuals and married men.
This paper evaluates how three large language models (GPT, Gemini, and Llama) generate and adjust investor risk profiles in response to standardized questionnaires and persona-based prompts, revealing distinct baseline tendencies and varying degrees of adaptability across the models.
This paper introduces a novel benchmark framework using mathematically explicit portfolio optimization problems to evaluate the quantitative reasoning capabilities of large language models, revealing distinct performance patterns among GPT-4, Gemini 1.5 Pro, and Llama 3.1-70B in handling risk-based objectives, return-based tasks, and investment constraints.
This paper introduces the AlgoXpert Alpha Research Framework, a rigorous three-stage protocol (In-Sample, Walk Forward Analysis, and Out-of-Sample) enhanced with multi-layered defense mechanisms to systematically mitigate overfitting and validate quantitative trading strategies before live deployment.
This paper introduces Slippage-at-Risk (SaR), a forward-looking quantitative framework that utilizes current order book microstructure to measure liquidity risk, optimize capital requirements, and predict systemic stress in perpetual futures exchanges, as validated by empirical analysis of the October 2025 Hyperliquid liquidation cascade.
This paper establishes a novel "Spectral Portfolio Theory" that identifies neural network weight matrices trained via stochastic gradient descent as portfolio allocation matrices, demonstrating how their spectral evolution from Marchenko-Pastur to inverse-Wishart statistics unifies diverse wealth dynamics models and yields a Spectral Invariance Theorem with applications in portfolio design, inequality measurement, and tax policy.
Using individual-level data from Keio Gijuku, this paper demonstrates that while 19th-century Japanese conscription exemptions incentivized family heads to increase their educational attendance, this surge in enrollment did not translate into a qualitative improvement in academic performance.
This paper simulates GPT-5.1 agents in credence goods markets to demonstrate that, unlike human actors, LLM-driven markets exhibit distinct behaviors such as higher participation and lower prices but entrenched fraud, suggesting that effective institutional design for AI economies must prioritize aligning agents' social preferences rather than relying on traditional mechanisms like verifiability or reputation.
This paper reformulates continuous-time optimal stopping as a regularized singular stochastic control problem using randomized stopping times and cumulative residual entropy to encourage exploration, enabling the derivation of unique optimal strategies and the development of scalable, policy-improving reinforcement learning algorithms.
This paper introduces a Continuous Shariah Compliance Index (CSCI) that unifies diverse binary screening standards into a single transparent measure, demonstrating that while it enables flexible portfolio design with varying compliance intensity, it does not function as a new priced factor for explaining expected equity returns.
This paper demonstrates that common shocks drive the tails of firm-level idiosyncratic return distributions, revealing a significant annual premium of 7–8% for stocks with high exposure to innovations in the lower-tail quantile factor, a risk that is amplified during periods of weak intermediary capital and low liquidity while also predicting aggregate market excess returns.
This paper demonstrates that currencies acting as major transmitters of volatility shocks earn lower excess returns, a predictable anomaly driven by spot exchange rate movements and explained by a general equilibrium model where such transmission proxies for priced country-specific risk.