Papers

Indonesia has established a regulated carbon market supported by national registry infrastructure and the IDXCarbon exchange. Carbon units can be issued, recorded, traded, and retired within this framework. IDXCarbon currently uses a private blockchain for its trading infrastructure. This creates an opportunity to examine how Indonesian carbon credits could also be represented and traded through public blockchain infrastructure. This study proposes an architecture for tokenizing Indonesian carbon credits as real-world assets (RWAs), with particular focus on Sertifikat Pengurangan Emisi Gas Rumah Kaca (SPE-GRK). The proposed architecture retains the Sistem Registri Unit Karbon (SRUK) as the authoritative source of carbon-unit status. It introduces a public-blockchain layer for token representation and programmable transactions. The architecture is designed to support lifecycle management, token-based asset representation, public observability of token activity, interoperability, wallet-based transactions, and programmable settlement. The architecture consists of four layers: the authoritative carbon layer, the registry interoperability and tokenization layer, the public-blockchain RWA layer, and the market and application layer. Access to the tokenized carbon assets remains regulated. Token issuance and transfers are linked to participant eligibility and registry status. Retirement also remains dependent on the authoritative carbon registry. The proposed architecture provides a framework for introducing public-blockchain RWA infrastructure into Indonesia's existing carbon market while maintaining SRUK authority and existing market-integrity controls.

Mobile money has widened financial access across Sub-Saharan Africa and enlarged the surface for money-laundering and terrorism-financing (ML/TF) activity in ecosystems dominated by high-volume, low-value transactions. Rwanda is a case in point: several million active mobile-money users, telecom-led wallets on the MTN and Airtel networks, and a Financial Intelligence Centre (FIC) supervising transaction streams whose scale exceeds static rule-based monitoring. This paper develops and evaluates a transaction-monitoring framework aligned to the Rwandan AML/CFT regime under (i) extreme class imbalance (~0.1% prevalence), (ii) scarce and delayed labels, and (iii) bounded investigator capacity. Using SAML-D, a synthetic dataset of 9,504,852 transactions with 17 laundering typologies, we engineer account-centric behavioural features (rolling velocity, net-flow directionality, counterparty diversity, burstiness) and benchmark supervised classifiers (Logistic Regression, Random Forest, LightGBM), unsupervised anomaly detectors (Isolation Forest, Local Outlier Factor), a dense autoencoder, and a late-fusion meta-learner. Evaluation is operational: PR-AUC, recall at a calibrated ~90%-precision point, recall at top-K%, and alerts per 10,000. On the chronologically held-out test period, LightGBM attains PR-AUC = 0.0469, capturing 64 laundering cases at precision ~0.89 with 0.51 alerts per 10,000; the fusion stacker reaches PR-AUC = 0.0477 at precision ~0.91 and 0.46 alerts per 10,000, recovering 59 true positives. We map score bands to Rwanda-relevant analyst workflows and STR/SAR escalation, and outline a staged path from synthetic prototyping to real-data validation with the National Bank of Rwanda and FIC. The contribution is operational: a governance-aware pipeline and evaluation protocol calibrated to the constraints of an African mobile-money regulator, not a new algorithm.

Investors interpret social disclosures from a risk perspective, yet relevant information can reach them through channels that differ sharply in regulatory enforcement and materiality: SEC filings, sustainability reports, or financial reports. We analyse how social disclosure via each channel relates to idiosyncratic risk. Studying S&P 1,500 constituents, we distinguish between initiated and continued disclosure along the three disclosure channels. We find first-time disclosure of social issues via SEC filings is related to increased idiosyncratic firm risk, highlighting that unexpected information is published. Continuous disclosure of social issues is related to lower idiosyncratic risk for sustainability and financial reports, which is in line with the literature. The SEC filing effect is robust for downside idiosyncratic risk measures, the separation of social disclosure into human capital, product liabilities, and stakeholder engagement, and for propensity score matching. Our findings suggest that the risk impacts of sustainability disclosure depend on the newness of information and disclosure channels.

This paper develops a unit-consistent actuarial framework for pricing capped cumulative temperature-index insurance under long-range dependence and stochastic variability. Daily temperature anomalies are modeled as increments of fractional Brownian motion evaluated at an operational time generated by the integral of a stationary normalized Cox–Ingersoll–Ross process. We show that the stochastic time change preserves stationarity and the long-memory covariance decay of the increments while introducing additional variability through the random operational clock. The cumulative temperature index admits a conditionally Gaussian representation, which leads to an exact conditional exponential kernel for capped stop-loss contracts and ensures existence of the entropic premium for every positive risk-aversion parameter. Consequently, valuation reduces to an outer Monte Carlo expectation over the accumulated CIR time, avoiding fractional Brownian path simulation and covariance-matrix construction. We further establish monotonicity properties of the premium with respect to risk aversion and conditional volatility. An empirical illustration based on Chicago temperature data shows that both long memory and stochastic time change can materially affect insurance premiums relative to conventional Brownian and fractional Brownian benchmarks, with the Hurst parameter playing an important role in valuation uncertainty. The proposed framework therefore provides a tractable approach for incorporating persistent dependence, stochastic variability, and bounded insurance losses into climate-index pricing.

Neurofinance shows that financial valuation depends on evolving neural states, while temporal experience is itself state dependent. Yet intertemporal models typically treat time as exogenous and ask how delay affects valuation. This paper examines the converse question: can valuation-related neural dynamics generate subjective financial time? We develop a continuous-time model on a joint financial–neuro-evaluative state space in which subjective financial time is accumulated valuation along dynamically admissible histories. We characterise realised temporal-rate dispersion and show that, under matched financial dynamics, distinct neuro-evaluative trajectories can generate different subjective financial times. Financial equivalence therefore need not imply temporal equivalence. Valuation curvature further determines local path dependence. A proof-of-concept behavioural–fMRI analysis uses \(1{,}183\) observations, \(1{,}126\) valuation transitions, and \(798\) financially matched pairs. Using a medial-prefrontal valuation-state coordinate based on vmPFC and dmPFC responses, the strongest participant exhibits a positive association between neural-state separation and subsequent valuation divergence (\(\rho=0.253,\ p_{\mathrm{perm}}=0.001\)), robust to residual financial distance (\(\rho=0.251\)) and tighter matching (\(\rho=0.207\)). Participant effects are heterogeneous, although combined evidence gives \(p=0.0216\). The data support the neural state-separation premise rather than directly identifying subjective financial time. The framework establishes a theoretical and empirically grounded basis for endogenous subjective time in neurofinance. \keywords{Neurofinance $\cdot$ Subjective financial time $\cdot$ Neural valuation $\cdot$ Relative subjective value $\cdot$ Intertemporal choice $\cdot$ Temporal dispersion $\cdot$ Path dependence

Human capital is a central organizational input, but standard financial data reveal little about firm-specific disruptions to workforce availability, cost, skills, and continuity. I construct a measure of disclosed human-capital disruption from earnings calls using author-defined coding criteria and a contextual language model. Within firms, a one-standard-deviation increase in the annual measure is associated with 0.55 percentage points higher idiosyncratic volatility, 0.58 percentage points higher downside deviation, and a 0.46 percentage point lower worst monthly return, with no corresponding relation to market beta. The results are stable across seven broader and narrower classification rules and remain after removing explicit labor-shortage passages and controlling for a recently published labor-shortage measure and transcript-wide negative and uncertain language. At the call level, human-capital disruption predicts approximately 0.50% higher idiosyncratic volatility over the following 42 trading days after conditioning on pre-call risk. Risk is elevated before the call as well, and the score predicts the continuation of that firm-specific risk state over the following 42 trading days. After telegraphing and succession passages are removed, the measure also predicts subsequent named-executive roster exits and the incumbent CEO's effective departure from office. Earnings calls therefore reveal disturbances to a key organizational input that are broader than labor shortages and informative about the distribution of firm outcomes.

Economics journals ration access to peer review with time and money. A July 2026 census of 500 RePEc-ranked economics journals accepting unsolicited submissions finds that 113 (22.6 per cent) charge a fee, with incidence falling from 68 per cent in the top 50 to 2 per cent in the bottom 50. A sequential-submission model shows that waiting pays part of the capacity-clearing price, leaving the fee as a residual. Because rejected manuscripts move down the ranking, a fee reduces system-wide referee demand only when authors stop, shorten their submission chains or switch to journals requiring fewer reports.

Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation. We study an alternative: estimate dependence among firm characteristics with a Maximally Filtered Clique Forest (MFCF), then map its clique structure to a Homological Neural Network (HNN). The MFCF maximum clique size K is the only parameter controlling architectural complexity, and it has a clear graphical meaning: it bounds the number of characteristics in each maximal clique and hence the highest interaction order the network can represent. The filtered graph then fixes the neural network's depth, layer widths, and sparse connections before training, in place of a separately chosen depth and width sequence. We apply two HNN variants to annual out-of-sample forecasts of U.S. stock excess returns from 1987 to 2016 using 94 firm characteristics. The HNN models match a three-hidden-layer benchmark on pooled predictive accuracy, rank the cross-section more accurately, and use roughly 80 times fewer parameters than a fully connected network with the same induced layer widths. Two structural ablations indicate that both the sparse connectivity and the estimated grouping of characteristics contribute to the ranking advantage, and both effects remain significant after correcting for multiple testing. These findings show that HNNs offer a practical and interpretable way to incorporate estimated dependence among firm characteristics into neural architecture design.

Suppose there are $N$ heterogeneous agents in a market with idiosyncratic risks but no uninsurable systematic risk factors. These agents may agree arbitrary financial contracts with one another, subject to the condition that contracts are self-enforcing under coalitions of agents in a common state. We show that, under mild conditions, this uniquely determines the limiting utility of every agent as $N$ tends to infinity. The result is an ergodic theorem: as the population grows, the number of degrees of freedom in the problem collapses, so that agents in the same state are treated identically in the limit. We exhibit an explicit, practically realisable mechanism achieving this limit using only short-dated contracts. The model can be applied either to an economy of heterogeneous agents pooling idiosyncratic risk through self-enforcing contracts or to the design of optimal insurance products such as pensions.

Academic freedom has come under growing strain worldwide. To study whether and how academics respond to political pressure, we exploit a natural experiment: the publication in early 2025 of a "blacklist" of words flagged by the U.S. government. We find that the release of this list led to a sharp reduction in the use of these flagged words among economists at universities that rely heavily on federal funding, relative to scholars from institutions that are less dependent on federal funding or based in the UK. The drop is driven by content related to gender, race, and environment. We show that changes are not simply semantic but reflect actual paper content and that neither the individual funding status nor time-invariant author characteristics are driving the effects. We also document interesting heterogeneous effects by department quality and author gender and ethnicity. Our findings are consistent with the idea that scholars respond strongly to political pressure.

Ownership matters for the media's watchdog role. We map the ownership networks behind thousands of online news outlets in the U.S., Canada, and Europe. The networks reveal who is ultimately responsible for the news: for over half of the outlets, a single entity. The rest sit behind multi-layered structures, making responsibility hard to trace. Market concentration, measured comparably across countries, is largely low to moderate. Looking at content, we find that co-owned outlets report more similarly, even within fixed outlet pairs, as ownership changes – not least in the U.S., where reader demand is often thought dominant.

The authors present a rigorous empirical evaluation of three distinct optimization paradigms for institutional factor portfolio construction: an entropy-based photonic quantum annealer (Dirac-3, Quantum Computing Inc.), a commercial mixed-integer programming solver (Gurobi), and a model-free deep reinforcement learning agent (SAC). Evaluating these pipelines on the Jensen-Kelly-Pedersen 13-factor equity library across 164 months test window, we implement a full factorial penalty sweep comprising 48 hyperparameter configurations that govern return, volatility, and skewness trade-offs. Our findings demonstrate that while photonic hardware can locate superior risk-return topologies within a narrow operating range, classical mixed-integer programming remains superior for risk-constrained mandates requiring tight tail-risk control and cross-seed stability. Furthermore, we document structural failure modes in reinforcement learning factor allocators under unanchored higher-moment shaping. We translate these empirical results into actionable, mandate-specific guidelines for quantitative portfolio managers deploying advanced optimization engines.

Two old market sayings hold that news is already priced in by the time it is published, and that the rumor is bought while the news is sold. Both place the price move associated with a piece of news before and at publication rather than after it. Whether the claims hold, for which kinds of news, and by how much are basic questions about how fast markets absorb public information. We test them on 4.57 million financial news articles covering roughly 3,000 US stocks (2023-2026). A large language model teacher, distilled into a compact classifier through active learning, assigns each article one of 17 event tags and five attributes; articles are clustered into stories to separate first reports from follow-up coverage; and beta-adjusted abnormal returns are measured around the resulting 1.68 million stock-day events, with 364,405 neutral-sentiment events as a placebo group. Three results follow. First, the price move associated with news concentrates before and at publication: pooled across all signed events, the cumulative move in the news direction by the close of publication day is 2.8 times its value 20 days later, and for rumor-flagged events the rumor day captures the entire move while the subsequent confirmation contributes nothing. Second, measured against the placebo of comparable stocks, markets underreact to numbers and overreact to stories: quantified fundamental news (earnings, dividends, guidance, analyst actions) keeps drifting in the direction of the news for weeks, while soft story-driven news (launches, macro commentary, leadership) gives back its move. Third, news carries width as well as direction: publicity raises volatility before the publication day, and volatility declines once the news is out, because publication resolves uncertainty. The study also produces a table of measured drift for each event tag, usable as a prior in news-conditioned forecasting models.

We develop a model of interbank networks with random liquidity shocks. Networks of dilutable debt—e.g., long-term, unsecured—facilitate efficient liquidity transfers: Shocked banks pledge interbank claims as collateral for new senior debt, diluting existing debt. Unlike with non-dilutable debt, indebtedness and connectedness are sources of stability, not fragility. Dilution is thus a “backdoor bail-in” that reallocates losses absent a resolution authority, trigger security, or ex post renegotiation. We uncover a class of networks, “exponential networks,” that implement optimal contingent transfers via plain debt. Yet exponential networks are not pairwise stable, whereas some core–periphery networks are, rationalizing observed interbank structures and their under-insurance against crises.

While technological innovations typically increase wage inequality by favoring skilled workers, we show that instant payment systems instead reduce it. We study the labor market effects of instant payment systems in the context of Brazil's Pix rollout. Using matched employer-employee data, we implement a triple-difference design that exploits pre-Pix mobile penetration across municipalities, the differential benefits of Pix for small versus large establishments, and the timing of Pix. We find that wages in small establishments rise significantly relative to large establishments after Pix. These gains are concentrated in cash-intensive sectors such as retail and services, with no effects in wholesale or manufacturing. Crucially, wage inequality declines, driven by wage gains in the lower half of the distribution, with no effect at the top. Our evidence points to increased small-firm labor demand, consistent with lower payment frictions. These effects are amplified where low-skill labor is scarce. A calibrated monopsony model implies that uniform Pix adoption would reduce both the within- and between-municipality components of wage dispersion, amplifying the aggregate inequality reduction.

When a data license expires, deleting stored records does not remove influence encoded in a trained forecaster. Machine unlearning seeks to remove this influence without retraining. We benchmark temporal unlearning with 3,200 paired references trained on all data and oracles retrained without the requested period. The grid covers five architectures, four rolling folds, five deletable years, and three experimental deletion levels on an S&P 500 volatility panel. The 2020 COVID crisis year produces the largest memorization gap for every architecture. Removing it improves all three deployable models in every fold, with the largest improvement in the 2022 bear market, while the two non-deployable models respond inconsistently. The target for approximate unlearning is the oracle, not low predictive accuracy on the deleted period. In one Transformer cell, an oracle that never trained on 2020 still predicts it at an information coefficient of 0.51, compared with 0.55 for the reference; pushing predictions toward noise reduces test skill. Across twelve deployable architecture-method pairs, only TSMixer with the hinge method remains near the oracle in every fold, closing 74-118% of the reference-to-oracle gap without a measurable loss of test skill. Method rankings vary across architectures and rolling windows. Audit separation rises with prior memorization but can remain small after exact deletion. The window-level loss comparison reaches at most 0.69, and treating stock-level windows as independent inflates the absolute t-statistic by a median factor of 1.9. These results call for an explicit deletion scope, oracle validation for the relevant architecture and window, and power-aware auditing.

Pawn loans offer borrowers a substantial degree of repayment flexibility in exchange for a harsh penalty in case of default: forfeit of collateral worth more than the loan amount along with any payments made toward recovery. Using a large RCT conducted in Mexico City, we document key stylized facts about pawn lending and explore the merits of replacing flexibility with structured repayment contracts in this important but understudied form of credit. Our experimental design includes a mandatory frequent-payments arm, a (status quo) flexible payments arm, and a choice between the two. This design point-identifies not only the average treatment effect, but also the effects of treatment on the treated and the untreated along with the average selection on gains, allowing a rigorous study of mandates versus choice. Although the average treatment effect of assigning borrowers to structured payments is a 19% decrease in their financial cost and a 17.5% decrease in the probability of default, only 11% of borrowers choose structured repayment contracts voluntarily. We show that structured repayment reduces financial costs for nearly all borrowers, including those who would not freely choose it, and find no evidence of selection on gains in cost savings.

We study dynamic physical hedging for insurers exposed jointly to catastrophe losses and stochastic reconstruction costs. Surplus evolves as a controlled jump diffusion whose loss amplitude combines marked catastrophe severity, an exogenous mean-reverting cost factor, and endogenous mitigation. We establish well-posedness, moment and stability estimates, and a stopping-time dynamic programming principle, and prove that the value function is the unique viscosity solution of the resulting nonlocal Hamilton-Jacobi-Bellman (HJB) equation Strategic interaction is introduced through a mean field game (MFG) with reduced-form vulnerability costs, yielding a coupled backward-forward HJB-Kolmogorov system. We establish relaxed equilibrium existence, Markovian realization, and uniqueness under appropriate compactness and monotonicity conditions. Numerical experiments show that reconstruction costs and capitalization materially affect optimal hedging and that cross-sectional vulnerability alters equilibrium costs. Tail-family robustness calculations further assess the sensitivity of these conclusions to alternative catastrophe-severity specifications.

Using the local time-space calculus of Peskir (2005) and the method developed in Mijatovic (2010), we derive a new integral representation for the backward probability associated with the first-passage time (FPT) of a diffusion process through a time-dependent barrier. We present a complete three-step numerical algorithm: first, the problem is reduced to a Volterra-type integral equation; second, its kernel is approximated by a Markov chain; finally, the resulting equation is solved using a quadrature rule. The method is implemented for several representative examples, and its convergence properties are established. An extension to double barrier problems is carried out

Narrow Uniswap v3 liquidity ranges resemble short dated options, and Panoptic's streaming premium echoes the short maturity concentration of Black-Scholes theta near the strike. This motivates a natural question: can implied volatility be extracted from Uniswap v3 and Panoptic using only on chain observables? A direct identification of theta with realized fee income is too strong, since fee income captures only the compensation leg of a narrow range LP position. The remaining leg, the cost of dynamically hedging the LP's negative convexity through arbitrage aligned trades, is formalized elsewhere as predictable loss or loss versus rebalancing and is not observable from fees alone. We therefore reformulate the object of interest. We derive $\sigma_{fee} = 2 \cdot feeRate \cdot \sqrt{Volume/L_{tick}}$, and interpret it as a DEX native fee implied volatility proxy: an observable, oracle free measure of fee flow relative to active liquidity. We show that recovering a structural latent volatility from pool observables alone is not identified in general, since aggregate volume mixes informed and uninformed flow while the missing hedging cost term depends on external price dynamics and arbitrage timing. What the pool data support directly is a fee implied activity index, not a Black-Scholes consistent implied volatility.

Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training—properties that existing agent-based and deep generative simulators provide only partially. We present FlowLOB, a conditional flow-matching generator of LOB trajectories, trained on multiple Hong Kong Exchange (HKEX) symbols at three sampling frequencies ($0.1$s, $1$s, $10$s) in tick-relative representation that transfers to unseen instruments. Because flow and diffusion models admit a common formulation, we train both with identical data, architecture, and budget, and sample both through the same fixed-step ODE solvers, yielding a controlled comparison of sampling efficiency and fidelity. Flow matching attains its best quality with only $10$ ODE-solver steps, whereas diffusion needs many more function evaluations to approach the same fidelity. At this efficient operating point, FlowLOB improves realism over baselines, two learned and two agent-based models, in most distributional metrics at the two finer sampling frequencies. We evaluate counterfactual controllability with a distributional test that asks whether changing a scenario condition moves the generated statistic toward the corresponding real tail regime; FlowLOB satisfies this criterion in most tested settings. Both realism and control effects transfer zero-shot on a held-out symbol. We additionally conduct ablation studies on the network architecture and the learning rate.

Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics. These measures provide useful diagnostics but may not capture the joint temporal and cross-level structure of order-book trajectories. We introduce LOB-ID, an embedding-based framework that adapts the Fréchet Inception Distance (FID) and Monge Inception Distance (MIND) to LOB data. To obtain domain-specific embeddings, we train the DeepLOB architecture on four months of Level-2 order-book data for five equities. We show that LOB-ID is stable across time, instruments, and embedding checkpoints, and rises monotonically under controlled distortions. We then construct a moment-matching attack against FID and a deep-book perturbation that evades statistic-based evaluation. MIND remains substantially more sensitive to both distortions. Finally, we score five generative LOB models, spanning stochastic baselines and deep learning approaches, and find that LOB-ID ranks them in line with the joint temporal and cross-level structure each captures by construction.

We study stress-scenario generation for systems driven by multivariate heavy-tailed risk factors. Within regions where several financial losses are simultaneously extreme, stress analysis concerns both the conditional law of the risk factors and the most plausible configurations producing those losses. We show that both are governed by the same limiting tail law. Preserving its measure recovers rare-event probabilities and scaled conditional stress laws, while misspecifying extremal dependence distorts some regular joint-stress probability. Its density governs reverse-stress optimization, whose maximizers identify the most plausible stress configurations. To exploit this common structure in finite samples, we develop SSGEN (Self-Similar Generative Estimation), which learns extremal dependence from intermediate exceedances and extrapolates to rarer levels using a Pareto radial component. Even when the target event is absent from the sample, we establish convergence rates for the generated conditional law, and data-driven reverse-stress solutions.

An investor may be optimistic about aggregate endowment growth at some times and pessimistic at others. The weight placed on her forecast in bond valuation can therefore vary across maturities. We study whether this maturity dependence disappears at the long end of the yield curve. In a two-investor Arrow–Debreu economy, physical extinction follows when total disagreement grows without bound. We find that bond valuation depends on net disagreement. If net disagreement has no limit, neither the long-forward measure nor the long bond exists. Bond yields can converge along the same belief path. The long yield alone therefore cannot reveal whether valuation weights and bond returns converge across maturities. We also obtain finite-maturity error bounds for bond prices and forward densities.

The dispersion of self-reported life satisfaction has been proposed and used as a comprehensive measure of societal inequality. A negative cross-country association between mean life satisfaction and its standard deviation has been read as evidence that this inequality is itself welfare-relevant, but critics have pointed to the nonlinearity and boundedness of response scales. I describe a further problem: a substantial and predictable share of respondents simplify the 0–10 scale to the subset {0, 5, 10} — "focal-value rounding" (FVR) — a behaviour that breaks not only linearity of the scale but even its order. I derive a sharp bound on the bias that FVR can induce in the standard-deviation; at empirically-typical FVR fractions the possible bias is roughly half the cross-country range of dispersion. I estimate and correct for the bias by fitting a model of FVR behavior to the Gallup World Poll Cantril ladder and three other multi-country life evaluations. FVR inflates measured standard deviation in 133 of 135 countries, by a median of 0.090 points. However, because the corrections are nearly uniform in sign across countries, rankings of the standard deviation survive essentially intact. The ordinal inequality indices advocated as the theoretically appropriate alternative suffer even worse from FVR. The correlation between mean and standard deviation survives FVR correction with modest attenuation and some caveats. Thus FVR does not, by itself, overturn previously reported correlations; it does, however, add a further reason for caution in interpreting scalar measures of subjective wellbeing inequality: candidate statistics are differently contaminated, the ordinal repairs no less than the cardinal originals, and they disagree with one another. Lastly, I show that FVR-correction can improve the pairwise dominance-comparability of full distributions.

Shariah-compliant equity screening provides a transparent setting in which institutional rules determine who may own a stock. A binary label identifies current eligibility but not whether the feasible investor base is fragmented across standards or close to changing. We define this instability as classification uncertainty and formalize its investor-base consequence through permitted investor mass. In a 1999-2024 CRSP-Compustat panel of 13,188 securities classified under seven researcher-emulated Shariah rulebooks, screening-rule disagreement and proximity to active boundaries rank next-month screen-implied transitions. U.S. Fama-MacBeth diagnostics do not support an unconditional equal-weighted permission premium, and a September 2023 DJIM/S&P methodology change produces no robust matched repricing. The central event evidence uses 25 official Securities Commission Malaysia lists. The 410 inclusions already trading before the preceding review have positive but imprecise matched returns. Applying the pre-event turnover floor yields 295 inclusions with 1.76 percentage points over $[0,10]$ trading days ($p_{\mathrm{date}}=0.008$; $p_{\mathrm{wild}}=0.017$) and 2.25 points over $[0,20]$ ($p_{\mathrm{date}}=0.018$; $p_{\mathrm{wild}}=0.035$). Leave-one-date-out, first-inclusion-only, and mid-review placebo checks are supportive, although a joint 20-day pre-event test rejects. Ownership and demand-pressure diagnostics do not identify a unique marginal buyer or clean causal demand shock. The evidence supports treating classification risk as a portfolio-monitoring state. Official Shariah permission is associated with price effects in a recognized local market among sufficiently tradable securities; formal eligibility alone is insufficient.

As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer companies for comparison is a fundamental challenge for valuation, due diligence, portfolio construction, and risk management. We propose an ensemble tree-based supervised similarity learning framework that defines company similarity through the lens of market valuation rather than static feature matching or semantic descriptions. Specifically, we train a CatBoost gradient-boosted decision tree model on observed private company valuations and derive a valuation-aware similarity metric from importance-weighted leaf-node co-occurrences across the ensemble. The similarity metric captures shared valuation drivers while accommodating nonlinear relationships, mixed data types, and pervasive missing data common in private markets. Using a global private-market universe of approximately 270,000 companies, including more than 53,000 firms with observed or derivable post-money valuations spanning multiple industries, geographies, and deal stages, we demonstrate that the proposed similarity framework improves upon traditional distance-based and text-embedding-based approaches in downstream k-nearest-neighbor valuation tasks in the evaluated industry groups, while retaining case-based explainability.

This article presents with DYSANOS the first generative market model for smooth SANOS option surfaces for all strikes and expiries which are free of static arbitrage. Our model is designed to generate entire paths of daily spot and option prices for years in the future. We present a robust and useful if somewhat simplistic baseline in the form of an AR(1) model. We discuss model setup, data pipeline, and training and investigate market reconstruction, stability, and tail behavior. We illustrate model performance on 891 Option Metrics IvyDB S&P Index surfaces from 2022-01-03 through to 2025-08-29. We also demonstrate how to construct numerically a risk-neutral density. As part of this we develop a new test for zero conditional means under a given measure. We show that for a set of 100,000 simulated paths a trading universe of 48 options and spot is numerically free of dynamic arbitrage.

Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data. Their appeal is both structural and practical: they offer stable likelihood-based training, strong mode coverage, flexible conditioning, and a stochastic-differential-equation formulation that aligns naturally with the Itô calculus and stochastic control frameworks widely used in finance. This survey reviews the growing literature on diffusion-family generative models for financial applications. We organize prior work primarily by financial data type, covering time series, limit order books, tabular data, and other structured financial objects, while discussing the modeling goals and application contexts that arise within each category. To the best of our knowledge, this is the first survey dedicated specifically to diffusion-family models for financial data. For more detailed information, we have open-sourced a repository https://github.com/ZhuoHan1998/Diffusion-Models-In-Finance.

The dynamics of an implied volatility surface to movements of the underlying is classically described by separate rules: sticky strike, sticky delta and Bergomi's skew-stickiness ratio (SSR). We unify these as a transport of the total-variance surface along a velocity field, $\partial_u w = v\,\partial_k w$, on the class of static-arbitrage-free surfaces. The SSR is the zeroth-order term of a Taylor (jet) hierarchy that also governs skew and curvature dynamics, making the velocity profile $v(k)$ estimable from a time series of surfaces rather than a single scalar. We give explicit conditions on v under which the flow preserves butterfly and calendar arbitrage-freeness over a finite spot move, with an admissibility radius in closed form; sticky delta, sticky strike and the SSR are the constant-velocity transports, while local and rough volatility are not special cases and are reproduced only in their short-maturity at-the-money velocity. The transport representation restricts nothing unless v is bounded across smile extrema, and we turn this into a test. Writing the response as a transport part $v\,\partial_k w$ plus a residual source s, at a smile extremum, where the skew vanishes—bounded transport predicts no response at its speed, so any response measured there is pure source. This gives a joint estimator of velocity and source that never divides by a near-zero skew, with an arbitrage theorem for the source term. On five years of SPX surfaces across seven tenors we reject self-similar transport at every tenor and a strike-constant velocity at intermediate tenors; at the two shortest tenors, where the smile minimum lies within the quoted strike range, the response is pure transport at the money but carries a genuine source at the minimum. Out of sample, the extended model improves curvature dynamics over the scalar SSR, the regime most relevant for positions carrying vanna and volga.