Match-fixing undermines the integrity of sport by eroding public trust and threatening the financial sustainability of clubs and leagues. The global expansion of sports betting markets has created new incentives and opportunities for manipulation, calling for rigorous, data-driven monitoring tools. Football, which accounts for the largest share of global betting turnover, remains particularly exposed: integrity reports continue to flag several suspicious matches, with past scandals in Italy and Turkey underlining the problem's persistence. This study uses high-frequency live-betting data from the Italian Serie B (2018/19-2020/21) to explore statistical approaches for detecting abnormal betting behaviour. A state-space modelling framework is employed to describe standard betting market dynamics and to predict expected betting volumes conditional on match characteristics. Deviations from these expectations can then be analysed using outlier detection techniques to identify potentially suspicious periods. The results demonstrate how statistical modelling can contribute to the early identification of irregular betting patterns, thereby supporting integrity assurance in live sports betting markets.

We investigate integration by parts (IBP) formulae for stochastic Volterra equations and we establish the smoothing effect of the expectation. Due to the inherent path-dependent dynamics of this class of processes, standard Bismut--Elworthy--Li (BEL) formulae and lifting procedures fail to produce representations for directional derivatives with respect to the initial curve. We exhibit a new type of fractional IBP for these derivatives which, by means of the Riemann--Liouville fractional derivative, interpolates between the standard chain rule and a pure BEL formula with Cameron--Martin path directions. Our assumptions describe precisely the trade-off between the direction's and the test function's regularities. Crucially, we reveal that more roughness leads to more smoothing: for a power-law kernel with Hurst parameter $H\in(0,1/2)$, we show that the expectation is differentiable along constant directions provided that the test function has Hölder continuity $β>2H$. The proof of the formula relies on a careful analysis of the conditional expectation's temporal regularity and on the well-posedness of its Riemann--Liouville derivative. We complement these results with a BEL formula along all square integrable directions whenever the noise is additive, a second order BEL formula and an application to forward and rough volatility models. In the latter case, the derivative is interpreted as the sensitivity with respect to the whole initial forward variance curve.

Do workers always work more for more? We investigate how intertemporal and uncompensated labor supply decisions change across observational and experimental windows, within the same workers. Combining a real-effort emoji-counting experiment on Prolific with observational data from platform administrative records, self-reported expectations and recalls, and smartphone-based screen-time logs, we find that expectations, and how easily accessible these are, play a central role in determining which kind of elasticities are observed. Uncompensated margins, in fact, diverge across windows and converge towards intertemporal elasticities in the observational window, where expectations lose power and income effects disappear. Similarly, intertemporal responses get loss-averse when expectations are more distant: wage losses retain an elastic effect while gains are rapidly discounted. Workers' behavior is thus simultaneously neoclassical and reference-dependent, as the type of response is largely determined by how wage changes are framed with reference to expectations or previous realizations.

We estimate long-run effects of Cuba's 1961 National Health Service and contemporaneous National Literacy Campaign using synthetic-control methods on newly assembled series for 21 former European colonies in the Americas, 1900--2022. Relative to synthetic Cuba, infant mortality falls 15--29 percent and average years of schooling rise 1.5--2 years; both effects are large, persistent, and robust to augmented SCM, synthetic difference-in-differences, interactive fixed effects, and matrix completion. Life-expectancy gains attenuate after 1990, consistent with the post-Soviet Special Period, suggesting that bundled health and literacy reforms permanently raise early-life survival and human capital, with smaller and less robust effects on adult longevity.

Tokenized real-world assets (RWAs) are often evaluated through headline indicators such as total value locked (TVL) or on-chain asset value. However, a large asset base does not necessarily imply low risk, since tokenized assets may remain illiquid, weakly traded, or highly concentrated among a small number of holders. Using public data from RWA.xyz, this paper develops an empirical and explainable risk scoring framework for tokenized RWA markets. The framework evaluates three dimensions of risk: liquidity risk $L$, concentration risk $C$, and market-quality risk $M$. These risk dimensions are constructed from observable indicators, including turnover, holder distribution, active-address activity, transfer frequency, and network concentration measured through Herfindahl indices. The analysis shows that several RWA tokens with substantial on-chain value exhibit high empirical risk because they combine limited transfer activity, low turnover, and concentrated ownership structures. In contrast, assets with broader participation and stronger on-chain activity display lower liquidity and concentration risk, even when their headline asset values are smaller. The findings demonstrate that TVL alone can obscure important risks in tokenized asset markets. By providing a transparent and data-driven risk scoring approach, this paper contributes to the empirical assessment of RWA liquidity and offers a practical basis for comparing tokenized assets beyond headline valuation metrics.

Traditional insurance pricing relies on risk-based principles that ensure actuarial fairness and solvency but do not explicitly account for policyholders' price sensitivity. We formulate insurance pricing as a decision-making problem and study it using tools from off-policy evaluation and stochastic control. We propose a kernelized inverse propensity score estimator that exploits local structure in the action space and yields variance reduction compared to the classical inverse propensity score estimator. Building on these value estimates, we investigate policy optimization and present two practical approaches for computing optimal pricing rules: an interpretable data-shared Lasso formulation and a flexible policy parameterization based on neural networks. Using a controlled synthetic travel insurance environment, we empirically confirm the theoretical results and show that neural networks outperform existing techniques for policy optimization.

Social inflation, which is the rise in liability claim costs beyond general economic inflation, has become a major concern for insurers and reinsurers, yet it is difficult to quantify because litigation outcomes are heavy-tailed and the mix of cases reaching verdict versus settlement changes over time. Using a large database of US jury verdicts and settlements, we develop case-mix-adjusted social inflation measures through multiple channels that matter to reinsurers: plaintiff win rates (a frequency-type channel), settlement propensity (a frequency-type channel), and verdict/settlement severity. The approach combines rolling-window logistic regression for probabilities and quantile (value-at-risk) regression for severities, with uncertainty quantified via a random-weighted bootstrap. We find statistically significant relative increases in plaintiff win probability of approximately 20%-30% from 2009 to 2024, alongside a statistically significant relative decline in settlement probability of more than 10% over the same period. The dominant channel is verdict severity: Even after controlling for explanatory variables, verdict awards show a sharp rise after 2020, increasing by more than 100% from 2020 to 2024, whereas settlement amounts show limited and often statistically insignificant inflation. Therefore, inflation in total amounts payable to plaintiffs closely tracks verdict severity. Social inflation is more pronounced in corporate-defendant and uninsured-defendant cases and in states without tort caps or third-party litigation funding regulation. In addition, we find that social inflation has impacts not only on "nuclear verdicts" but also, in a similar manner, on moderate losses.

Behavioral theories rest on parsimony: a small number of mechanisms organizing many decisions. We define a Maximum Rule Concentration Index that measures how parsimoniously a dataset of risky choices can be organized through a library of simple, parameter-free decision rules drawn from canonical behavioral theories: salience, regret, disappointment, modal-payoff focusing, extreme-outcome screening, and limited attention. Applied to three lottery-choice datasets, the data exhibit detectable parsimony: for a majority of subjects, observed concentration exceeds what standard utility models generate on the same menus. The concentration organizes around salience thinking, modal-payoff focusing, and regret.

Compositional data are contemporarily defined as positive vectors, the ratios among whose elements are of interest to the researcher. Financial statement analysis by means of accounting ratios a.k.a. financial ratios fulfils this definition to the letter. Compositional data analysis solves the major problems in statistical analysis of standard financial ratios at industry level, such as skewness, non-normality, non-linearity, outliers, and dependence of the results on the choice of which accounting figure goes to the numerator and to the denominator of the ratio. Despite this, compositional applications to financial statement analysis are still rare. In this article, we present some transformations within compositional data analysis that are particularly useful for financial statement analysis. We show how to compute industry or sub-industry means of standard financial ratios from a compositional perspective by means of geometric means. We show how to visualise firms in an industry with a compositional principal-component-analysis biplot; how to classify them into homogeneous financial performance profiles with compositional cluster analysis; and how to introduce financial ratios as variables in a statistical model, for instance to relate financial performance and firm characteristics with compositional regression models. We show an application to the accounting statements of Spanish wineries using the decomposition of return on equity by means of DuPont analysis, and a step-by-step tutorial to the compositional freeware CoDaPack.

We study offline change-point estimation for time series data exhibiting nonlinear serial dependence. To address this problem, we propose a copula-based Markov chain model with Weibull marginal distributions, which is suitable for modeling nonnegative data such as event times and volatility measures. Nonlinear dependence is incorporated through the Clayton and Joe copulas, allowing the model to capture asymmetric lower-tail and upper-tail dependence structures, respectively. We derive the corresponding likelihood function and estimate the change point and model parameters using maximum likelihood estimation implemented through the Newton--Raphson algorithm. Confidence intervals are constructed via a parametric bootstrap Monte Carlo procedure. Extensive numerical studies are conducted to evaluate the finite-sample performance and robustness of the proposed method under different dependence structures and copula misspecification scenarios. The results demonstrate that the proposed estimators perform well in terms of RMSE and relative error, particularly for the estimation of the change point. An empirical application to the VIX index during the COVID-19 pandemic further illustrates the practical usefulness of the proposed approach in detecting structural changes in both the marginal distributions and serial dependence structure.

This paper compares a series of contemporary portfolio construction approaches by employing ten U.S. stocks (TSLA, WMT, BAC, GS, LLY, MRK, GOOG, META, AAPL and XOM) in a time frame from September 2023 to December 2025. The paper explores both basic mean-variance optimization, constrained optimization, Fama French five factor regression modeling, Monte Carlo simulation, and the Black-Litterman model to determine how constraints to a solution, risk factors to a strategy, simulated approximations, and specific market views may all impact the outcome of portfolio allocation, performance and stability. Overall, the results show that standard optimization may result in highly concentrated portfolios, while constrained optimization leads to changes in portfolio allocations by altering the efficient frontier, five factor regression models suggest that a basic investment style of defensive large value and profitability exposure, Monte Carlo approximation is a viable technique to arrive at mean-variance optimal portfolios provided the simulations are high enough especially under a box constraint, the Black Litterman portfolio approach produces more economically intuitive allocations and greater stability compared to standard mean-variance optimization as the approach balances equilibrium returns with investor views.

This paper develops a three-currency Heath-Jarrow-Morton framework in which corporate credit is treated as a separate economy, connected to the nominal and real economies through synthetic inflation and credit exchange rates. The framework produces a testable identity. Under joint no-arbitrage, the credit spread of an issuer expressed over the inflation-rateindexed risk-free curve equals the same issuer's credit spread expressed over the nominalrate-indexed risk-free curve plus the model-implied breakeven inflation forward at the same maturity. The identity holds within any single calibration of the framework. It is empirically falsifiable across two parallel corporate-bond segments of the same market, in a segmented market the two segments may price different corporate credit economies, and the gap between their implied corporate forwards measures the failure of the shared-credit-economy assumption. Applied to Brazilian debenture markets, the framework delivers a sharp empirical finding. Fifteen large issuers placed paper in both the CDI-indexed general-purpose segment and the IPCA-indexed infrastructure segment between January 2021 and February 2026. The within-issuer triangle residual at the 3-year tenor averages 640 basis points, with crosssectional standard deviation of 26 basis points across the 15 issuer means, and remains stable through both the 2021-2023 BCB tightening cycle and the 2024-2026 easing phase. A retail post-tax indifference benchmark anchored on Lei 12.431 closes the bulk of the residual. The remainder is consistent with institutional participation on the CDI side, contractual asymmetries between debentures with different use-of-proceeds restrictions, and segment-specific liquidity gaps.

This paper estimates the carry embedded in listed IBIT options and compares it with the carry embedded in matched CME bitcoin futures. Put-call parity recovers an implied forward on the ETF; BlackRock's daily holdings file maps each ETF share into bitcoin units; and CME futures prices and BRRNY, a U.S. close bitcoin reference rate, provide the corresponding futures-market carry. The difference in carry implied by these two products is consistent with frictions that limit cross-margining between spot bitcoin or ETF exposure and CME futures. In the selected-strike IBIT sample of 386 date-bucket observations, the mean wedge is 2.58 percent and the median wedge is 2.52 percent, both measured in annual percentage points. The result is consistent with segmented collateral and margin systems limiting arbitrage between regulated bitcoin-exposure venues.

Artificial intelligence feels omnipresent, yet the disruption many expect has not fully arrived. The main reason is not model capability, nor even the tools built to harness those models. Rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world. We offer a three-stage lens: Augmentation, Automation, and Reconstruction, and argue that the most consequential disruption resides in the third stage where workflows and markets are rebuilt around delegation, machine-to-machine interaction, continuous monitoring, and auditable constraints. Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization: the complementary investments that produce the familiar "productivity J-curve" of general-purpose technologies. We illustrate this transition through examples in consumer markets, education, news, and coding. Finally, we emphasize a normative point: the agentic future is not predetermined. Leaders must both skate to where the puck is going and actively steer it toward a good place, ensuring innovation delivers welfare gains felt by businesses and consumers around the world.

Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt as the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control policies, and observability routines are patched together faster than they can be validated, standardized, and governed. We define Stochastic Tax as the recurring operating burden of keeping probabilistic agent behavior within acceptable bounds. The distinction matters: debt is a stock of design and governance liability, while the tax is a flow of operating cost that arises because stochastic agents act through tools and workflows. We outline how managers can make both visible through lightweight dashboards and governance controls.

FlashIV is a low-latency Black--Scholes implied-volatility solver for production use. It normalises each input to an out-of-the-money price and solves a tail-stable erfcx/log-price residual. The hot path combines a cheap Li/asymptotic seed with a fixed, branch-light Householder refinement and guarded boundary handling. Across regular and stressed benchmarks, FlashIV stays close to the expanded Jäckel reference price while running materially faster than a normalised Java port of Jäckel's \emph{Let's Be Rational}. FlashIV+ adds an optional Jäckel--Newton correction for applications that need tighter agreement with that reference price, trading latency for reference-price alignment.

The 2022 rise in U.S. mortgage rates increased relocation costs for homeowners with low-rate mortgages. This cost varies across destinations because each draws workers from a different mix of labor markets. We build an in-migration mortgage-payment wedge from HMDA loans and pre-shock IRS migration networks. From 2017 to 2024, higher wedges reduce college-educated homeowner in-migration, leave renters unaffected, and raise H-1B sponsorship requests. The implied offset is 14 H-1B sponsorship requests per 100 deterred college-educated domestic in-migrants. We show that mortgage lock-in operates as a destination-side labor-market shock that shifts part of firms' adjustment toward employer-sponsored immigration.

Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction costs. Standard predict-then-optimize methods first forecast returns and then solve for weights, compounding prediction errors and often failing under regime shifts. We propose an end-to-end framework that directly optimizes differentiable surrogates of key financial metrics - Sharpe ratio, Omega ratio, Conditional Value-at-Risk (CVaR), and Risk Parity - allowing neural networks to learn portfolio weights via backpropagation. Our expanding-window walk-forward procedure, applied to 50 S&P 500 stocks from 2007 to 2023, incorporates realistic bid-ask spread costs and rebalances quarterly. On the challenging out-of-sample test period (2022-2023), the best model - an AttentionLSTM with the Omega-CVaR-RiskParity loss - achieves an annualized Sharpe of 0.29 and a total compounded return of +7.86%, while the S&P 500 delivers -4.52% total return and an annualized Sharpe of -0.02. This outperforms the S&P 500 by 12.38 percentage points (a relative improvement of over 270%), while keeping tail risk (CVaR) nearly unchanged. The framework consistently outperforms the equal-weight portfolio, S&P 500, and traditional methods (MVP, HRP, NCO), demonstrating that embedding financial objectives directly into model training yields robust, economically meaningful outperformance even in adverse market conditions.

We present ThiopheneIV, a Black-Scholes implied-volatility solver with a monotone core and explicit production guards. The solver starts from the simple Choi-Huh-Su L3 lower-bound seed and applies three Euler-Chebyshev steps on a lower branch and three Halley steps on the remaining upper branch. We prove that, in exact arithmetic, the seed lies below the root and both maps increase monotonically without overshooting. We also detail the practical challenges encountered for a double-precision implementation: parity normalisation, microscopic Bachelier-limit handling, saturated price treatment, and an optional Jäckel-Newton polish. Across standard grids, market-like data, high-volatility cases, and adversarial corners, ThiopheneIV agrees closely with multiprecision Black reference prices at low latency. We provide detailed comparisons with recent solvers, including Jäckel's Let's Be Rational. The broader lesson is that a convergence proof gives a clean core, but robust production inversion still depends on boundary handling and on the pricing objective one chooses to match.

Early evaluations of NYC's congestion pricing program indicate overall improvements in vehicle speed and transit ridership. However, its distributional impacts remain understudied, as does the design of compensatory transit strategies needed to mitigate potential welfare losses. This study identifies population segments and regions most affected by congestion pricing, and evaluates how those welfare losses can be compensated through transit improvements funded by the toll revenues. We estimate joint mode and destination models using aggregated synthetic trips in the NY-NJ-CT-PA Combined Statistical Area (CSA) and calibrate toll-related parameters using post-toll changes reported by MTA. Compensatory transit strategies are evaluated by quantifying the reductions in transit wait time and fare discounts required to offset the CS losses. The results show that the program leads to an accessibility-related CS loss of $397.23 million per year, while generating net passenger toll revenue of $523.44 million per year estimated based on the MTA's report--indicating a net welfare gain. However, these gains in benefits conceal significant disparities. Achieving a general compensation requires modest investment--a 0.63-minute (13%) reduction in wait time or $165.15 million in annual fare subsidies for NYC residents, and a 2.12-minute (28%) reduction or $171.42 million for New Jersey residents. However, ensuring that no population group and county unit is made worse off is substantially more costly and infeasible through transit improvements alone. These findings underscore the need for differentiated compensation strategies: uniform fare discounts lead to overcompensation for some groups, whereas segment-specific discounts, origin-based fare reductions, or commuter pass bundles can achieve equitable accessibility restoration at lower fiscal cost.

We provide a mechanistic foundation for economic complexity methods. In our model, an economy's ability to produce an activity depends on the joint presence of required capabilities. We analytically derive the Economic Complexity Index (ECI) for this model and show that it is a monotonic function of the stock of capabilities in an economy. We then explore the family of functions and conditions that are compatible with economic complexity estimates and show that multiplicatively separable production functions are incompatible with economic complexity estimates. By contrast, additive non-separable functions are sufficient regardless of whether they are log super-modular. We also show that this model explains differences in the shape of networks of related activities, such as the product space or research space. These findings solve long standing puzzles in the literature on economic complexity.

In 1955 Fermi, Pasta, Ulam and Tsingou performed first numerical studies with the aim to obtain the thermalization in a chain of nonlinear oscillators from dynamical equations of motion. This model happend to have several specific features and the dynamical thermalization was established only later in other studies. In this work we study more generic models based on Random Matrix Theory and social networks with a nonlinear perturbation leading to dynamical thermalization above a certain chaos border. These systems have two integrals of motion being total energy and norm so that the theoretical Rayleigh-Jeans thermal distribution depends on temperature and chemical potential. We introduce the wealth thermalization hypothesis according to which the society wealth is associated with energy in the Rayleigh-Jeans distribution. At relatively small values of total wealth or energy there is a formation of the Rayleigh-Jeans condensate, well studied in physical systems such as multimode optical fibers. This condensation leads to a huge fraction of poor households at low wealth and a small oligarchic fraction which monopolizes a dominant fraction of total wealth thus generating a strong inequality in human society. We show that this thermalization gives a good description of real data of Lorenz curves of US, UK, the whole world and capitalization of companies at Stock Exchange of New York SE (NYSE), London and Hong Kong. It is also shown that above a chaos border the dynamical Rayleigh-Jeans thermalization takes place also in social networks with the Lorenz curves being similar to those of wealth distribution in world countries. Possible actions for inequality reduction are briefly discussed.

This paper introduces a trade ordering rule that aims to reduce intra-block price volatility in Automated Market Maker (AMM) powered decentralized exchanges. The ordering rule introduced here, Clever Look-ahead Volatility Reduction (CLVR), operates under the (common) framework in decentralized finance that allows some entities to observe trade requests before they are settled, assemble them into "blocks", and order them as they like. On AMM exchanges, asset prices are continuously and transparently updated as a result of each trade and therefore, transaction order has high financial value. CLVR aims to order transactions for traders' benefit. Our primary focus is intra-block price stability (minimizing volatility), which has two main benefits for traders: it reduces transaction failure rate and allows traders to receive closer prices to the reference price at which they submit their transactions accordingly. We show that CLVR constructs an ordering which approximately minimizes price volatility with a small computation cost and can be trivially verified externally.