Given a reference risk measure, the risk budgeting is the portfolio where each asset contributes a predetermined amount to the total risk. We propose a novel approach, alternative to the ones proposed in the literature, for the calculation of the risk budgeting portfolio. This different perspective on the problem has several interesting consequences. For the calculation of the portfolio, we define a Cauchy sequence within the simplex of R^n, whose limit corresponds to the risk budgeting portfolio. This construction allows for the straightforward implementation of an efficient algorithm, avoiding the need to solve auxiliary, equivalent optimization problems, which may be computationally challenging and hard to interpret in the decision theory context. We compare our algorithm with the standard optimization-based methods proposed in the literature. From a theoretical point of view, starting from the Cauchy sequence, we define a function for which the risk budgeting portfolio is a fixed point. Therefore, sufficient conditions for the existence and uniqueness of the fixed point can be used. The methodology is developed for general risk measures and implemented in detail in the case of standard deviation.

This work evaluates the impact of contagious cyber-events, over a finite horizon, on firms' financial health and on a cyber insurance portfolio. Our approach builds on key empirical findings from economics and cybersecurity. In economics, firm size and growth-rate distributions are non-Gaussian and exhibit heavy tails. In cybersecurity, contagion dynamics strongly depend on firm size and environmental conditions. To capture these features, we propose a stochastic multi-group SIR model coupled with a granular model of firm growth. This framework allows us to quantify the financial impact of cyber-attacks on firms' revenues and on the insurer's portfolio. In the model, the arrival time and duration of cyber-attacks are driven by a combination of a Cox process and a Bernoulli random variable. The Cox process represents external contagion, with an intensity given by the force of infection derived from the stochastic SIR dynamics. The Bernoulli component captures contagion originating from an infected sister or subsidiary firm. Environmental variability enables stochastic scenario generation and the computation of aggregate exceedance probabilities, a standard metric in catastrophe modeling that provides insurers with immediate insight into the financial severity of an event. We apply the framework to the LockBit ransomware attacks observed between May and July 2024. For a portfolio of 2,929 firms located in Ile-de-France, the model predicts that, with 50% probability, the insurer will need to compensate losses equivalent to up to two days of revenue over a 100-day cyber incident.

This paper proposes a positional poverty gap measure of multidimensional poverty within the Alkire-Foster counting framework. The measure captures the depth of deprivations even when indicators are ordinal, unlike the standard poverty gap, which requires cardinal variables. The proposed method draws on the fuzzy set literature and introduces a distribution-based measure of deprivation depth using the empirical cumulative distribution of each indicator, with the most deprived group as the benchmark. For each deprived individual, the method assigns a score based on the individual's relative position in the distribution. Depth is thus expressed as a difference in distributional positions, motivating the label positional poverty gap. The paper demonstrates that this measure preserves the identification and aggregation structure of the counting approach and satisfies its axiomatic properties when the reference distribution remains fixed over time. The framework remains flexible because it accommodates different identification rules, deprivation cutoffs, and variable types. Overall, it offers a simple, meaningful, and theoretically grounded way to incorporate depth into multidimensional poverty measurement with ordinal data.

Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.

This paper examines how loss aversion affects wages offered by employers and accepted by job seekers. I introduce a behavioral search model with monopsonistic firms making wage offers to job seekers who experience steeper disutility from pay cuts than utility from equivalent pay raises. Employers strategically reduce pay cuts to avoid offer rejections, and they exactly match offers to current salaries due to corner solutions. Loss aversion makes three predictions on the distribution of salary growth for job switchers, which I empirically test and confirm with administrative data in Korea. First, excess mass at zero wage growth is 8.5 times larger than what is expected without loss aversion. Second, the density immediately above zero is 8.8% larger than the density immediately below it. Third, the slope of the density below zero is 6.5 times steeper than the slope above it. When estimating model parameters with minimum distance on salary growth bins, incorporating loss aversion substantially improves model fit, and the marginal value of additional pay is 12% higher for pay cuts than pay raises in the primary specification. For a hypothetical hiring subsidy that raises the value of labor to employers by half of a standard deviation, incorporating loss aversion lowers its pass-through to wages by 18% (relative to a standard model) due to higher elasticity for pay cuts and salary matches that constrain subsidized wage offers. Somewhat surprisingly, salary history bans do not mitigate these effects as long as employers can imperfectly observe current salaries with noise.

In this work we propose a framework to construct Market-Implied Sustainability (MIS) scores for individual firms by exploiting fund-level sustainability classifications and granular portfolio holdings. The central idea is that the relative over/under-representation of a stock in sustainability-oriented funds reveals a market-based assessment of its sustainability profile. We implement the methodology in the European context using the Sustainable Finance Disclosure Regulation (SFDR), considering Article 9 (``dark green'') funds as the sustainability-oriented segment and comparing their portfolio compositions to those of other funds. We compute MIS scores for a large cross-section of European companies over the period 2010--2025. We then examine how MIS relates to traditional firm-level ESG ratings provided by LSEG and analyze the determinants of potential divergences between the two measures. Finally, we assess the economic relevance of MIS through portfolio-tilting strategies, ranging from rule-based reallocations to constrained optimal allocation frameworks. The results show that MIS scores capture dimensions of sustainability that differ systematically from conventional ESG ratings. In portfolio applications, tilting toward firms with high MIS scores improves risk-adjusted performance, whereas strategies based solely on ESG ratings do not deliver comparable gains. Overall, the findings suggest that market-implied sustainability measures provide complementary information to fundamentals-based ESG metrics and have practical relevance for asset allocation and regulatory monitoring.

Sequential intraday electricity trading allows photovoltaic (PV) operators to reduce imbalance settlement costs as forecasts improve throughout the day. Yet deployable trading policies must jointly handle forecast uncertainty, intraday prices, liquidity, and the asymmetric economics of PV imbalance exposure. This paper proposes a feature-driven reinforcement learning (FDRL) framework for intraday PV trading in the Nordic market. Its main methodological contribution is a corrected reward that evaluates performance relative to a no-trade baseline, removing policy-independent noise that can otherwise push reinforcement learning toward inactive policies in high-price regimes. The framework combines this objective with a predominantly linear policy and a closed-form execution surrogate for efficient, interpretable training. In a strict walk-forward evaluation over 2021-2024 across four Nordic bidding zones (DK1, DK2, SE3, SE4), the method delivers statistically significant profit improvements over the spot-only baseline in every zone. Portfolio experiments show that a pooled cross-zone policy can match zone-specific models, while transfer-learning results indicate a two-cluster market structure and effective deployment in new zones with limited local data. The proposed framework offers an interpretable and computationally practical way to reduce imbalance costs, while the transfer results provide guidance for scaling strategies across bidding zones with different market designs.

We introduce Delphos, a deep reinforcement learning framework for assisting the discrete choice model specification process. Delphos aims to support the modeller by providing automated, data-driven suggestions for utility specifications, thereby reducing the effort required to develop and refine utility functions. Delphos conceptualises model specification as a sequential decision-making problem, inspired by the way human choice modellers iteratively construct models through a series of reasoned specification decisions. In this setting, an agent learns to specify high-performing candidate models by choosing a sequence of modelling actions, such as selecting variables, accommodating both generic and alternative-specific taste parameters, applying non-linear transformations, and including interactions with covariates, while interacting with a modelling environment that estimates each candidate and returns a reward signal. Specifically, Delphos uses a Deep Q-Network that receives delayed rewards based on modelling outcomes (e.g., log-likelihood) and behavioural expectations (e.g., parameter signs), and distributes this signal across the sequence of actions to learn which modelling decisions lead to well-performing candidates. We evaluate Delphos on both simulated and empirical datasets using multiple reward settings. In simulated cases, learning curves, Q-value patterns, and performance metrics show that the agent learns to adaptively explore strategies to propose well-performing models across search spaces, while covering only a small fraction of the feasible modelling space. We further apply the framework to two empirical datasets to demonstrate its practical use. These experiments illustrate the ability of Delphos to generate competitive, behaviourally plausible models and highlight the potential of this adaptive, learning-based framework to assist the model specification process.

How labor markets adjust immediately after minimum wage hikes remains an open, policy-relevant question. This paper studies short-run minimum-wage effects in Japan's spot labor market using Timee data and a wage-bin difference-in-differences design. We find a 2\% employment decline in affected bins, driven by reduced vacancy creation rather than worker supply. Effects are more negative where the minimum-wage bite is higher and in low-wage occupations. Using job descriptions and amenity information, we document reallocation across job types: postings shift toward greater amenity provision and experienced-worker targeting, while female-targeted descriptions become less common, suggesting short-run labor-demand adjustments may foreshadow longer-run reallocation.

We develop a kernel-based solver for path-dependent PDEs (PPDEs) along with a convergence theory. Our numerical scheme leverages signature kernels, a recently introduced class of kernels on path-space. Specifically, we solve an optimal recovery problem by approximating the solution of a PPDE with an element of minimal norm in the signature reproducing kernel Hilbert space constrained to satisfy the PPDE at a finite collection of collocation paths. In the linear case, we show that the optimisation has a unique closed-form solution expressed in terms of signature kernel evaluations at the collocation paths. Under strict assumptions, we prove consistency of the proposed scheme, guaranteeing convergence to the PPDE solution as the number of collocation points increases. Finally, several numerical examples are presented, in particular in the context of option pricing under rough volatility. Our numerical scheme constitutes a valid alternative to the ubiquitous Monte Carlo methods.

In this paper, we establish the stochastic ordering of the Gini indexes for multivariate elliptical risks which generalized the corresponding results for multivariate normal risks. It is shown that several conditions on dispersion matrices and the components of dispersion matrices of multivariate normal risks for the monotonicity of the Gini index in the usual stochastic order proposed by Samanthi, Wei and Brazauskas (2016) and Kim and Kim (2019) are also suitable for multivariate elliptical risks. We also study the tail probability of Gini index for multivariate elliptical risks and revised a large deviation result for the Gini indexes of multivariate normal risks in Kim and Kim (2019).

This paper introduces a new factor contributing to the decline in marriage and fertility: the growth of leisure technology. Over recent decades, high-income countries have experienced two notable shifts in household and family dynamics. First, there has been a significant decline in marriage rates and fertility. Second, time has increasingly been allocated to leisure activities. This paper presents a unified model of marriage and fertility, incorporating intra-household bargaining dynamics. The model, calibrated using data from Japan between 2019 and 2023, is employed to assess the impact of leisure technology growth on marriage and fertility during 2005-2009. The findings highlight that leisure technology growth makes single life relatively more appealing compared to marriage and parenthood. The model explains 21.1% of the decline in marriage and 73.1% of the decrease in fertility.

Banks must optimize risky investments, dividend payouts, and capital structure under tight Basel III solvency and liquidity constraints, while costly equity issuance serves as a distress-recovery tool. We formulate this as a stochastic control problem that reduces the high-dimensional balance-sheet dynamics to a tractable one-dimensional process in the leverage ratio, with state-dependent investment limits. The resulting policy is simple and interpretable: pay dividends at an upper reflection barrier and, when needed, recapitalize only at the distress boundary, jumping to a unique target level. We characterize these thresholds analytically and show their sensitivity to regulatory parameters. From a regulatory viewpoint, we solve an outer optimization problem that maps the efficient frontier between shareholder value and survival probability (via Monte Carlo), with and without leverage caps. Results highlight that tightening solvency requirements often yields the best safety-profitability trade-off.

Private credit assets under management grew from \$158 billion in 2010 to nearly \$2 trillion globally by mid-2024, fundamentally reshaping corporate credit markets. This paper provides a systematic survey of the academic literature on private credit, organizing theory and evidence around four questions: why the market has grown so rapidly, how direct lender technology differs from bank lending, what risk-adjusted returns investors earn, and whether the sector poses systemic risks. We develop an integrated theoretical framework linking delegated monitoring, soft-information processing, and incomplete contracting to the institutional specifics of modern direct lending. The empirical evidence documents a distinctive lending technology serving opaque, private-equity-sponsored borrowers at a meaningful and persistent spread premium over the broadly syndicated loan market, while performance evidence suggests that risk-adjusted returns for the average fund are largely consumed by fees.

Trend-following strategies underpin many systematic trading approaches yet struggle under nonstationary and nonlinear market regimes. We propose an LSTM-based framework to forecast next-day trend differences ($Δ_t$) for the top 30 S\&P 500 equities, validated across market cycles (2005--2025). Key contributions include: (i) formal proof of bias-variance reduction via differencing, (ii) exhaustive empirical benchmarks against OLS, Ridge, and Lasso, (iii) portfolio simulations confirming economic gains in terms of overall PNL compared to other models like OLS, Ridge, Lasso or LightGBM Regressor

Childhood socioeconomic disadvantage is a well established determinant of health in later life. Less is known about how early-life deprivation unfolds when individuals experience major institutional transformation and migration in adulthood. Cohorts socialized under Soviet institutions provide a useful setting to examine life-course divergence under systemic change. This study uses harmonized data from the Survey of Health, Ageing and Retirement in Europe (SHARE) on older adults residing in Estonia, Latvia, and Israel to examine the association between retrospectively reported childhood deprivation and multiple health outcomes in later life, including poor self-rated health, chronic disease burden, functional limitation, depression, and a composite multifrailty indicator. Logistic regression models and predicted probabilities assess whether childhood deprivation predicts late-life health across different adult institutional contexts and whether associations vary by linguistic affiliation. Higher levels of childhood deprivation are consistently associated with poorer health outcomes across all three countries. Individuals in the highest deprivation quintile show substantially higher odds of adverse health outcomes, including multifrailty. Stratified analyses for Estonia and Latvia indicate broadly similar deprivation-health gradients among national-language and Russian-speaking populations. These findings highlight the persistence of childhood disadvantage and the importance of early-life conditions in shaping health inequalities in ageing populations exposed to systemic transformation.

We address the attribution problem for apparent slow collective dynamics: is the observed persistence intrinsic, or inherited from a persistent driver? For the leading eigenvalue fraction $ψ_1=λ_{\max}/N$ of S\&P 500 60-day rolling correlation matrices ($237$ stocks, 2004--2023), a VIX-coupled Ornstein--Uhlenbeck model reduces the effective relaxation time from $298$ to $61$ trading days and improves the fit over bare mean reversion by $Δ$BIC$=109$. On the decomposition sample, an informational residual of $\log(\mathrm{VIX})$ alone retains most of that gain ($Δ$BIC$=78.6$), whereas a mechanical VIX proxy alone does not improve the fit. Autocorrelation-matched placebo fields fail ($Δ$BIC$_{\max}=2.7$), disjoint weekly reconstructions still favor the field-coupled model ($Δ$BIC$=140$--$151$), and six anchored chronological holdouts preserve the out-of-sample advantage. Quiet-regime and field-stripped residual autocorrelation controls show the same collapse of persistence. Stronger hidden-variable extensions remain only partially supported. Within the tested stochastic class, conditioning on the observed VIX proxy absorbs most of the apparent slow dynamics.

Whenever dealing with horizons of different times scales, risk evaluation of losses may incur in both interest rate uncertainty and horizon risk as introduced in [11]. With the goal to capture both effects, we work with cash subadditive fully-dynamic risk measures. In this work we consider such measures obtained via the BSDE and the shortfall approaches. We stress that we consider BSDEs both with Lipschitz and quadratic drivers. We then introduce the hq-entropic risk measure on losses as an effective example of fully-dynamic risk measure serving the scope. Shortfall risk measures are extended to capture cash non-additivity. For our newly introduced h-generalized shortfall risk measures we provide a dual representation and we connect them to fully-dynamic certainty equivalent. To conclude, we can see that the hq-entropic risk measures on losses belong to the family h-generalized shortfall, but they are not of certainty equivalent type. We note that the classical entropic risk measure, besides being generated by a BSDE, is also both a shortfall and a certainty equivalent.

Financial institutions face a trade-off between predictive accuracy and interpretability when deploying machine learning models for credit risk. Monotonicity constraints align model behavior with domain knowledge, but their performance cost - the price of monotonicity - is not well quantified. This paper benchmarks monotone-constrained versus unconstrained gradient boosting models for credit probability of default across five public datasets and three libraries. We define the Price of Monotonicity (PoM) as the relative change in standard performance metrics when moving from unconstrained to constrained models, estimated via paired comparisons with bootstrap uncertainty. In our experiments, PoM in AUC ranges from essentially zero to about 2.9 percent: constraints are almost costless on large datasets (typically less than 0.2 percent, often indistinguishable from zero) and most costly on smaller datasets with extensive constraint coverage (around 2-3 percent). Thus, appropriately specified monotonicity constraints can often deliver interpretability with small accuracy losses, particularly in large-scale credit portfolios.

This paper proposes a simulation-based framework for assessing and improving the performance of a pension fund management scheme. This framework is modular and allows the definition of customized performance metrics that are used to assess and iteratively improve asset and liability management policies. We illustrate our framework with a simple implementation that showcases the power of including adaptable features. We show that it is possible to dissipate longevity and volatility risks by permitting adaptability in asset allocation and payout levels. The numerical results show that by including a small amount of flexibility, there can be a substantial reduction in the cost to run the pension plan as well as a substantial decrease in the probability of defaulting.

This technical report presents a stochastic model for pricing weather derivatives and devising hedging strategies tailored to Indian markets. We model temperature dynamics using a modified Ornstein-Uhlenbeck process with jumps to account for sudden shocks, such as heatwaves and coldwaves. Historical data from 12 Indian states (1951-2023) is used for calibration, and Monte Carlo simulations are employed under the risk-neutral measure to price Heating Degree Days (HDD), Cooling Degree Days (CDD), and extreme event options. Sensitivity analysis reveals that a 20% increase in volatility leads to an approximate 4.2% increase in option prices, highlighting the critical impact of volatility on derivative pricing. Results show that HDD options in colder states like Himachal Pradesh are significantly more expensive, with prices reaching up to INR 684,693, while CDD options in hotter states like Gujarat are priced higher, up to INR 262,986. A comprehensive portfolio analysis indicates that investing INR 120,000 in HDD put options in Uttar Pradesh yields an expected payoff of INR 132,369, resulting in a return on investment (ROI) of 10.3%. Conversely, a similar investment in Karnataka yields a negative ROI of -66.7% due to its milder climate. Hedging strategies are tailored to each state's climatic risk, with recommendations to buy 90.66 HDD put options at a strike of 90.89 in Uttar Pradesh and invest in CDD call options in Gujarat. These insights offer practical solutions for managing temperature-related financial risk in energy and agriculture, providing actionable, state-specific hedging strategies for diverse climatic scenarios in India.

This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation with the exponential-form objective. The risk-sensitive objective arises either as the agent's risk attitude or as a distributionally robust approach against the model uncertainty. Owing to the martingale perspective in Jia and Zhou (J Mach Learn Res 24(161): 1--61, 2023) the risk-sensitive RL problem is shown to be equivalent to ensuring the martingale property of a process involving both the value function and the q-function, augmented by an additional penalty term: the quadratic variation of the value process, capturing the variability of the value-to-go along the trajectory. This characterization allows for the straightforward adaptation of existing RL algorithms developed for non-risk-sensitive scenarios to incorporate risk sensitivity by adding the realized variance of the value process. Additionally, I highlight that the conventional policy gradient representation is inadequate for risk-sensitive problems due to the nonlinear nature of quadratic variation; however, q-learning offers a solution and extends to infinite horizon settings. Finally, I prove the convergence of the proposed algorithm for Merton's investment problem and quantify the impact of temperature parameter on the behavior of the learning procedure. I also conduct simulation experiments to demonstrate how risk-sensitive RL improves the finite-sample performance in the linear-quadratic control problem.

Using rich Swedish administrative data, we apply causal machine learning methods to study how earnings losses after job displacement vary with observable characteristics that may be relevant for targeting policy interventions for workers. Heterogeneity in effects is as large within as across worker groups defined by age and schooling, and as large within as across establishments. A substantial portion of cross-establishment heterogeneity can be explained by industry and local labor market characteristics, suggesting a role for place- and industry-based targeting. The largest losses are concentrated among already vulnerable workers, indicating that well-designed targeting policies can improve both efficiency and equity.

The classical Markowitz mean-variance model uses variance as a risk measure and calculates frontier portfolios in closed form by using standard optimization techniques. For general mean-risk models such closed form optimal portfolios are difficult to obtain. In this note, assuming returns follow the class of normal mean-variance mixture (NMVM) distributions, we obtain closed form expressions for frontier portfolios under mean-risk criteria when risk is modeled by the general class of law invariant convex risk measures. To achieve this goal, we first present a sufficient condition for the stochastic dominance relation on NMVM models and we apply this result to derive closed form solution for frontier portfolios. Our main result in this paper states that when return vectors follow the class of NMVM distributions the associated mean-risk frontier portfolios can be obtained by optimizing a Markowitz mean-variance model with an appropriately adjusted return vector.

We introduce a performance-driven framework for constructing strictly causal forward-oriented observables in strongly non-stationary time series. The method combines a robustly normalized composite of heterogeneous indicators with a causally computed derivative component, yielding a local phase-leading effect that is amplified near regime transitions while remaining fully causal. A hysteresis-based decision functional maps the observable into discrete system states, with execution delayed by one step to preserve strict temporal ordering. Adaptation is achieved through a walk-forward scheme, in which model parameters are selected using rolling train--validation windows and subsequently applied out-of-sample. In this setting, the validation segment acts as an internal performance screen rather than as a statistical validation set, and no claims of generalization are inferred from it alone. The framework is evaluated on high-frequency financial time series as an experimentally accessible realization of a non-stationary complex system. Under a controlled zero-cost setting, the resulting dynamics exhibit a pronounced risk-reshaping effect, characterized by smoother trajectories and reduced drawdowns relative to direct exposure, and should be interpreted as an upper bound on achievable performance. These results illustrate how causal signal engineering can generate anticipatory structure in non-stationary systems without relying on non-causal information, explicit horizon labeling, or high-capacity predictive models.

We investigate the performance of the Kelly rule in a setting in which the dynamics of the return is represented by a time change process. We find that in this general semi-martingale setting the Kelly rule does not maximize the average growth rate, unless the log-return is normally distributed. Namely, the investment position proposed by the Kelly rule is too large, and the investor could achieve a higher average growth rate by investing less aggressively. The higher the variance of the stochastic clock, the more material the failure of the Kelly rule. The ruin threshold proposed by Thorp (1969) is closer, even though examples based on stochastic clock variance estimates taken from the literature show that Kelly rule investment remains safely in the ruin-free region. Finally, the goal of keeping the investment below the ruin threshold for a family of stochastic clock distributions generates a long term investment problem that parallels the "acceptable investment" theory.

For a single event with finitely many mutually exclusive outcomes, the full Kelly problem is to maximize expected log wealth over nonnegative stakes together with an optional cash position. The optimal formula is classical, but the support-selection step is often presented via Lagrange multipliers. This note gives a shorter state-price derivation. A cash fraction $c$ acts as an implicit position in every outcome: in terminal-wealth terms, it is equivalent to a baseline stake $cq_i$ on outcome $i$, where $q_i$ is the state price. On any active support, explicit bets therefore only top up favorable outcomes from this baseline $cq_i$ to the optimal total stake $p_i$. This yields the formula $x_i = (p_i - c q_i)_+$, the threshold rule $p_i/q_i > c$, and, after sorting outcomes by $p_i/q_i$, a one-pass greedy algorithm for support selection. The result is standard in substance, but the implicit-position viewpoint gives a compact proof and a convenient way to remember the solution.

Despite the scale of capital being deployed toward AI initiatives, no empirical framework currently exists for benchmarking where a firm stands relative to competitors in AI readiness and deployment, or for translating that position into auditable financial outcomes. In practice, private equity deal teams, management consultants, and corporate strategists have relied on qualitative judgment and ad-hoc maturity labels; tools that are neither comparable across industries nor grounded in observable economic data. This paper introduces the AI Transformation Gap Index (AITG), a composite empirical framework that measures the distance between a firm's current AI deployment and a time varying, industry constrained capability frontier, then maps that distance to dollar denominated value creation, execution feasibility under uncertainty, and competitive disruption risk. Five linked modules address this gap: cross industry normalization (IASS), a dynamic capability ceiling that evolves with frontier capabilities (AFC), trajectory based firm scoring with integrated execution risk (IFS), a CES bottleneck value decomposition mapping gap scores to enterprise value (VCB), and a competitive hazard measure for inaction (ADRI). I calibrate the framework for 22 industry verticals and apply it to 14 public companies using public filings. A retrospective construct validity exercise correlating AITG scores with observed EBITDA margin expansion yields Spearman rho_s = 0.818 (n = 10), directionally consistent with predictions though insufficient for causal identification. A counterintuitive result emerges: the largest AI transformation gaps do not produce the highest value density, because implementation friction, CES bottlenecks, and timing lags erode the theoretical upside of wide gaps.

Cross-sectional ranking models are often deployed as if point predictions were sufficient: the model outputs scores and the portfolio follows the induced ordering. Under non-stationarity, rankers can fail during regime shifts. In the AI Stock Forecaster, a LightGBM ranker performs well overall at a 20-day horizon, yet the 2024 holdout coincides with an AI thematic rally and sector rotation that breaks the signal at longer horizons and weakens 20d. This motivates treating deployment as two decisions: (i) whether the strategy should trade at all, and (ii) how to control risk within active trades. We adapt Direct Epistemic Uncertainty Prediction (DEUP) to ranking by predicting rank displacement and defining an epistemic uncertainty signal ehat relative to a point-in-time (PIT-safe) baseline. Empirically, ehat is structurally coupled with signal strength (median correlation between ehat and absolute score is about 0.6 across 1,865 dates), so inverse-uncertainty sizing de-levers the strongest signals and degrades performance. To address this, we propose a two-level deployment policy: a strategy-level regime-trust gate G(t) that decides whether to trade (AUROC around 0.72 overall and 0.75 in FINAL) and a position-level epistemic tail-risk cap that reduces exposure only for the most uncertain predictions. The operational policy, trade only when G(t) is at least 0.2, apply volatility sizing on active dates, and cap the top epistemic tail, improves risk-adjusted performance in the 20d policy comparison and indicates DEUP adds value mainly as a tail-risk guard rather than a continuous sizing denominator.

This paper develops a simple model of the world supply chain to estimate the effects of sanctions that restrict the flow of inputs from one country to another. Such restrictions operate through changes in the weights of the global production network: the sanctioning country ceases supplying certain inputs to the target country and reallocates its production to other destinations. Using the OECD Inter-Country Input--Output tables, we calibrate the model to assess the vulnerability of the Indian economy. We consider two classes of counterfactuals: restrictions on a single sector of a foreign country supplying India, and restrictions on all sectors of a foreign country supplying India. We then rank foreign countries and foreign country-sectors by the risk that their supply restrictions pose to economic activity in India. Our results show that India's greatest country-level vulnerability is to China, followed by the United Arab Emirates, the United States, Saudi Arabi and Russia, with the vulnerability to China being twice as much that to the UAE.