Parametric Portfolio Policies (PPP) estimate optimal portfolio weights directly as functions of observable signals by maximizing expected utility, bypassing the need to model asset returns and covariances. However, PPP ignores policy risk. We show that this is consequential, leading to an overstatement of expected utility and an understatement of portfolio risk. We develop Bayesian Parametric Portfolio Policies (BPPP), which place a prior on policy coefficients thereby correcting the decision rule. We derive a general result showing that the utility gap between PPP and BPPP is strictly positive and proportional to posterior parameter uncertainty and signal magnitude. Under a mean--variance approximation, this correction appears as an additional estimation-risk term in portfolio variance, implying that PPP overexposes when signals are strongest and when risk aversion is high. Empirically, in a high-dimensional setting with 242 signals and six factors over 1973--2023, BPPP delivers higher Sharpe ratios, substantially lower turnover, larger investor welfare, and lower tail risk, with advantages that increase monotonically in risk aversion and are strongest during crisis episodes.

Prediction markets suffer from reduced liquidity and price accuracy for long-horizon events due to the opportunity cost of committed capital. Recently, major platforms have introduced interest-bearing positions to mitigate this "long-horizon problem." I evaluate this policy using agent-based simulations with large language model (LLM) traders in a 2 x 2 factorial design, varying time horizon (4 days vs. 2 years) and the presence of interest. While long horizons degrade accuracy, the observed pricing bias (0.72 percentage points) is significantly smaller than theoretical and prior empirical estimates. Paying interest eliminates approximately 83% of the horizon effect on accuracy and more than triples market participation (from 17% to 62% of wealth). These findings suggest the long-horizon problem may be overstated in existing literature and that interest-bearing positions are a highly effective intervention, primarily by incentivizing participation rather than correcting bias.

How information transmits through prices -- and why this transmission breaks down -- remains poorly understood. We combine regularized deconvolution with Hawkes process analysis to study the impulse response structure of investor flows in the Korean equity market (January 2020 -- February 2025). Three findings emerge: foreign and institutional flows drive permanent price discovery while individual flows provide contrarian liquidity; individual investor surges are predominantly panic-driven and exhibit near-explosive self-excitation; and during herding episodes, institutional price impact deteriorates sharply in small-cap stocks while large-cap stocks maintain resilience. These results reframe market efficiency as a state variable -- conditional on both herding intensity and firm size -- rather than a structural constant.

Large language models (LLMs) are increasingly used to provide health advice, yet evidence on how their accuracy varies across languages, topics and information sources remains limited. We assess how linguistic and contextual factors affect the accuracy of AI-based health-claim verification. We evaluated seven widely used LLMs on two datasets: (i) 1,975 legally authorised nutrition and health claims from UK and EU regulatory registers translated into 21 languages; and (ii) 9,088 journalist-vetted public-health claims from the PUBHEALTH corpus spanning COVID-19, abortion, politics and general health, drawn from government advisories, scientific abstracts and media sources. Models classified each claim as supported or unsupported using majority voting across repeated runs. Accuracy was analysed by language, topic, source and model. Accuracy on authorised claims was highest in English and closely related European languages and declined in several widely spoken non-European languages, decreasing with syntactic distance from English. On real-world public-health claims, accuracy was substantially lower and varied systematically by topic and source. Models performed best on COVID-19 and government-attributed claims and worst on general health and scientific abstracts. High performance on English, canonical health claims masks substantial context-dependent gaps. Differences in training data exposure, editorial framing and topic-specific tuning likely contribute to these disparities, which are comparable in magnitude to cross-language differences. LLM accuracy in health-claim verification depends strongly on language, topic and information source. English-language performance does not reliably generalise across contexts, underscoring the need for multilingual, domain-specific evaluation before deployment in public-health communication.

As economics scales, a key bottleneck is representing what papers claim in a comparable, aggregable form. We introduce evidence-annotated claim graphs that map each paper into a directed network of standardized economic concepts (nodes) and stated relationships (edges), with each edge labeled by evidentiary basis, including whether it is supported by causal inference designs or by non-causal evidence. Using a structured multi-stage AI workflow, we construct claim graphs for 44,852 economics papers from 1980-2023. The share of causal edges rises from 7.7% in 1990 to 31.7% in 2020. Measures of causal narrative structure and causal novelty are positively associated with top-five publication and long-run citations, whereas non-causal counterparts are weakly related or negative.

This paper studies an optimal reinsurance problem for a utility-maximizing insurer, subject to the reinsurer's endogenous default and background risk. An endogenous default occurs when the insurer's contractual indemnity exceeds the reinsurer's available reserve, which is random due to the background risk. We obtain an analytical solution to the optimal contract for two types of reinsurance contracts, differentiated by whether their indemnity functions depend on the reinsurer's background risk. The results shed light on the joint effect of the reinsurer's default and background risk on the insurer's reinsurance demand.

This paper develops a unified framework that links firm-level predictive signals, cross-asset spillovers, and the stochastic discount factor (SDF). Signals and spillovers are jointly estimated by maximizing the Sharpe ratio, yielding an interpretable SDF that both ranks characteristic relevance and uncovers the direction of predictive influence across assets. Out-of-sample, the SDF consistently outperforms self-predictive and expected-return benchmarks across investment universes and market states. The inferred information network highlights large, low-turnover firms as net transmitters. The framework offers a clear, economically grounded view of the informational architecture underlying cross-sectional return dynamics.

I prove that competitive market outcomes require computational intractability. If P = NP, firms can efficiently solve the collusion detection problem, identifying deviations from cooperative agreements in complex, noisy markets and thereby making collusion sustainable as an equilibrium. If P != NP, the collusion detection problem is computationally infeasible for markets satisfying a natural instance-hardness condition on their demand structure, rendering punishment threats non-credible and collusion unstable. Combined with Maymin (2011), who proved that market efficiency requires P = NP, this yields a fundamental impossibility: markets can be informationally efficient or competitive, but not both. Artificial intelligence, by expanding firms' computational capabilities, is pushing markets from the competitive regime toward the collusive regime, explaining the empirical emergence of algorithmic collusion without explicit coordination.

Demographics in Malaysia and Viet Nam are evolving rapidly, potentially disrupting traditional family support to older people. We estimate a set of Poisson random effects models with panel data from the Malaysia Ageing and Retirement Survey and the Viet Nam Aging Survey to analyze how living arrangements, marital status, and support from children influence the mental and physical health of older people. In Malaysia, having living children plays an important protective role for both mental and physical health, while living with a son appears to have a protective effect for physical health. Results are similar for Viet Nam, except older women, who are at greater risk of mental and physical health problems, appear to enjoy a greater protective effect for their mental health from a child living nearby than do men. Our analysis underscores the importance of social safety nets for the health of senior citizens living alone.

Background. Low earnings are associated with household insecurity. Direct Support Professionals (DSPs) provide support for people with intellectual and developmental disabilities, typically for wages close to state minimums, and may experience insecurity. Objective. The purpose of the study was to evaluate the prevalence of food and housing insecurity among DSPs. Methods. We conducted a statewide, cross-sectional survey of DSPs in New York State (2022-2023). Measures included detailed questions about food and housing insecurity. We used chi-square analyses and logistic regressions to examine relationships between insecurity and demographic characteristics as proxies for social determinants of health. A total of 4,503 DSPs responded to the survey. The analytic sample contained 2,766 respondents with complete data for all relevant variables. Results. Overall, 62.6% experienced food and/or housing insecurity, with over half of those respondents experiencing both. Insecurity was highest among DSPs with a disability (76.2%), DSPs of color (75.7%), and those with lower income (72.4%), but over 50% of DSPs across demographic groups experienced insecurity. Conclusions. The widespread insecurity this study demonstrates is an occupational hazard that reduces worker welfare. At the macro-level, household insecurity is a critical threat to the stability of the care and support delivery system. The human services sector is projected to grow rapidly in the future. If growth continues along low wage lines, it implies an equally rapid expansion of worker insecurity. Government action to raise pay and interventions that enhance food and housing security are needed to support workers in the care delivery system for people with intellectual and developmental disabilities.

Purpose. This paper examines the extent to which job satisfaction, requests for accommodations, and the likelihood of a request being granted vary by disability status. We further analyze whether being granted workplace accommodations moderates the relationship between work satisfaction and disability. Methods. We use a novel survey of healthcare workers centered on disability status, perceptions of work experiences, and the provision of accommodations. The data are used in a descriptive analysis and multiple regressions to examine the moderating effect of accommodations on the relationship between disability and indicators related to job satisfaction. Results. Results show that people with disabilities have more negative perceptions of their work experiences than people without disabilities. Although people with disabilities are more likely to request accommodations than people without disabilities, they are equally likely to have their requests wholly or partly granted. Regression results indicate that the negative relationships between disability status and most measures of work experience are largely eliminated when accounting for the disposition of accommodation requests. The main exception is turnover intentions, in which the adverse relationship with having a disability does not change even when an accommodation is granted. Partly granting accommodations is helpful only for some metrics of job experience. Conclusion. Our paper shows that fully granting accommodations can go a long way to closing the disability gap in job satisfaction between people with and without disabilities.

We consider the pricing of energy spread options for spot prices following an exponential Ornstein-Uhlenbeck process driven by a sum of independent multivariate variance gamma processes, which gives rise to mean-reverting, infinite activity price dynamics. Within this class of driving processes, the Esscher transform is used to obtain an equivalent martingale measure with a focus on the weak variance alpha-gamma process. By deriving an analytical formula for the cumulant generating function of the innovation term, we obtain a pricing formula for forwards and apply the FFT method of Hurd and Zhou to price spread options. Lastly, we demonstrate how the model should be both estimated on energy prices under the real world measure and calibrated on forward or call prices, and provide numerical results for the pricing of spread options.

This study introduces a SABR-informed multitask Gaussian process for constructing implied volatility surfaces from sparse option quotes. We treat a dense synthetic dataset generated by a calibrated SABR model as the source task and market option quotes as the target task. Within the multitask Gaussian process framework, we learn cross-task dependence via task embeddings with hierarchical regularization, enabling adaptive transfer of structural information. On Heston ground truth across ten market regimes and in a case study with SPX options, the model achieves lower error than the single-task Gaussian process and SABR at near-term maturities and remains competitive at long-term maturities, while satisfying standard no-arbitrage conditions. The framework combines the theory-driven structure with nonparametric Bayesian regression and yields reliable implied volatility surfaces for risk management.

This paper examines bid requirements, where the government may cancel a procurement contract unless two or more bids are received. Using a first-price auction model with endogenous entry, we compare the bid requirement and reserve price mechanisms in terms of auction failure and procurement costs. We find that, in comparison with bid requirements, reserve prices can reduce procurement costs and substantially lower failure probabilities, especially when entry costs are high or signals are sufficiently informative. Bid requirements are more likely to result in zero entry, while reserve prices can sustain positive entry under broader conditions.

Systemic risk measures are crucial for the stability of financial markets, yet classical formulations fail to capture the complexity of market volatility. We propose a new framework for systemic risk measurement on the variable-exponent Bochner-Lebesgue space $L^{p(\cdot)}$, where the exponent $p(\cdot)$ is a random variable rather than a deterministic constant parameter, thereby inherently encoding latent market volatility. By constructing suitable deterministic auxiliary functions and single-firm risk measures, we decompose the quantification of systemic risk in $L^{p(\cdot)}$ into two sequential steps, ultimately deriving its dual representations. Several examples are provided to illustrate the theoretical results.

Market efficiency at least requires the absence of weak arbitrage opportunities, but this is not sufficient to establish a situation where the market is sensitive, i.e., where it "fully reflects" or "rapidly adjusts to" some information flow including the evolution of asset prices. By contrast, No Weak Arbitrage together with market sensitivity is sufficient and necessary for a market to be informationally efficient.