Papers

This paper develops the Municipal Equitable and Sustainable Well-being Index (MESWI) for all Italian municipalities, extending the 12-domain BES framework to the local level through 49 indicators. Results reveal substantial territorial heterogeneity that regional and provincial statistics may conceal. The North-South divide remains the dominant geographical pattern and is considerably stronger than the difference between inner and non-inner areas. At the same time, municipalities within the same regions and territorial categories display markedly different well-being profiles. Remoteness is associated with weaker services and socio-economic opportunities but also with stronger environmental performance. The MESWI provides a fine-grained picture of multidimensional well-being and a potential information base for the programming, monitoring and evaluation of place-based policies.

This data paper describes an anonymized release derived from controlled behavioral experiments on decision-making under risk (known probabilities) and ambiguous uncertainty (partially specified or unknown probabilities) in Ellsberg-type urn tasks. The release contains 4,486 decision records from 246 adult participant records across two implementations (laboratory and online). In each task, participants reported their maximum stake to enter a subsequent lottery; these stake responses can be interpreted as proxies for willingness to pay within the implemented incentive mechanism. In a subset of tasks, participants also selected the color they wished to bet on. The online implementation additionally includes ex-post evaluations of perceived uncertainty across situations. The repository further provides detailed game definitions, realized lottery outcomes, participant instructions, and a data dictionary of variables. The source export contained 4,544 decision records; 58 records belonging to five participants younger than 18 were excluded from this release because documentation of guardian permission for unrestricted public data publication was not available. The dataset supports replication studies, comparisons of ambiguity-attitude models, and analyses of behavioral stability under repeated participation.

Dual-class shares allocate control to founders whose firm-specific investments drive firm value but separate control from ownership, raising agency costs. We analyze this trade-off dynamically. Using new data on US dual-class firms spanning 52 years and difference-in-differences designs, we show that valuations rise following dual-class recapitalizations but decline over time, whereas innovative output increases persistently. These effects are concentrated in industries with greater firm-specific investments. We find corresponding results for stock unifications. Investment by mature dual-class firms is less sensitive to opportunities and voting premia increase with maturity. Our results support dynamic treatment effects and yield new policy implications.

Coupled feedback networks are often monitored channel by channel even though cross-channel paths alter both stability margins and transmitted disturbances. We study identification of a structured feedback matrix L_t = Phi diag(gamma_t) in an output-only setting: no commanded, probing, or reference input exists – only temporally separated outputs and the scheduling gains gamma_t are observed, while the coupling response Phi and the clearing-window inputs are not. Identification rests jointly on the persistent excitation of the observed pre-window output and on two structural features separating coupling from confounds: the known time variation of the gains, which modulates the closed-loop response in a predictable pattern, and a partial-reversal moment by which a known fraction of transient displacement is corrected in a subsequent window. We give a hierarchy of results: exact local identification of the coupling under a Jacobian rank condition on the gain regimes; a first-order interaction estimator whose identification strength is the minimum eigenvalue of the residualized interaction information matrix (provably unidentified under constant gains); and a characterization of the estimand as a resolvent sensitivity – the right object for screening transmitted disturbances and a first-stage input to spectral-margin recovery – with sqrt(T) asymptotics for the first-order estimator, a cross-identification theorem mapping each varying gain to exactly identified resolvent rows and columns, and bootstrap validity under consistent selection; the implemented heuristic's empirical coverage (90% at nominal 95%) quantifies the remaining gap. Simulations verify sharpness of the rank condition and quantify benchmark failures under confounding. A case study on leveraged-fund rebalancing feedback, where daily fund disclosures play the role of the known gains, illustrates the method on real data.

Artificial intelligence (AI) is increasingly embedded in scientific work, but researchers may not evaluate its use uniformly across research tasks. This study examines task-specific attitudes towards AI among an international, self-selected sample of 3,785 PhD students in STEM and medical and health sciences who participated in Nature's Graduate Survey 2025. We analyse respondents' comfort with using AI for writing a research article, collecting and analysing data, designing experiments, tracking scientific literature, and summarising it. Latent class analysis identifies four distinct attitudinal profiles. The dominant profile reflects a "division of labour," in which AI is widely accepted for literature-related tasks but resisted in activities closely associated with intellectual contribution, such as writing, data analysis, and experimental design. A "status quo" profile is broadly uncomfortable across tasks, an "all-purpose" profile is broadly comfortable, and an "undecided" profile expresses substantial uncertainty. These patterns suggest that attitudes towards AI in research are organised less around a simple acceptance-rejection divide than around task-specific boundaries, likely concerning delegation, authorship, and responsibility. Because the survey measures comfort rather than legitimacy, the profiles are best interpreted as attitudinal configurations with a normative dimension. The findings highlight the importance of task-specific approaches to AI governance, doctoral training, disclosure, and research evaluation.

Despite theory tying wealth inequality to social structure, empirical evidence has been limited to a few studies based on online social media data. This study uses a very different type of data, expands the global coverage to very different types of societies, and investigates new questions. In particular, we collect data from ~3500 sharing units (households) in 46 communities across the globe, representing considerable human social and cultural diversity. In each, we analyze the relationship between people's material wealth and the structure of social networks: borrowing money, sharing food, working together, socializing, etc. In almost all communities, a sharing unit's material wealth is positively associated with the number of other sharing units it both helps and is helped by. A sharing unit's wealth is also associated with the relative wealth of the sharing units to which it is linked—a form of economic homophily. Notably, communities with greater wealth inequality are also characterized by a network structure in which poorer sharing units are less well connected to wealthier ones. We augment our unique cross-cultural data with other community-level environmental, institutional, and economic attributes, opening new avenues for future research into the co-determination of wealth and social networks.

The computation of Black-Scholes implied volatility is a fundamental task in quantitative finance, underpinning option valuation, model calibration and risk management. Although implied volatility is routinely used in practice, the inversion of the Black-Scholes pricing formula remains a challenging numerical problem, particularly in asymptotic regimes corresponding to extreme option prices, strikes or maturities, where the inverse map becomes highly sensitive to perturbations of the price. In this paper, we introduce a new family of asymptotically-informed neural-network architectures for implied-volatility computation. Exploiting the distinct behaviours of the Black-Scholes pricing function in different volatility regimes, we propose a family of architectures that learn a trainable partition of the price-log-moneyness domain through a system of gating functions and combines specialised local approximations of the implied-volatility function within each region. Extensive numerical experiments demonstrate that the proposed models consistently outperform standard feed-forward neural networks across a wide range of parameter domains, often by several orders of magnitude in relative accuracy while maintaining excellent generalisation properties. Furthermore, the neural-network outputs provide highly accurate initial guesses for a third-order Householder scheme, allowing near machine-precision implied-volatility computations after only two refinement iterations.

We develop a PDE-based methodology for pricing and hedging European contingent claims in general one-dimensional diffusion markets characterized solely by their scale function and speed measure, possibly without a classical SDE representation, and with constant interest rate. We derive a hedging equation whose solution generates a self-financing hedging strategy and provide sufficient conditions on scale, speed, and interest rate, under which this strategy achieves the minimal hedging capital, expressed through the no free lunch with vanishing risk (NFLVR) condition. We further prove necessary and sufficient conditions for NFLVR and characterize the class of equivalent local martingale measures through an auxiliary diffusion whose scale and speed characteristics are determined by those of the real-world diffusion and by the interest rate. When the NFLVR condition fails, the framework may produce multiple hedging equations corresponding to non-minimal strategies, whose associated prices can exceed the minimal hedging capital. We illustrate both the effectiveness and limitations of the approach through numerical experiments involving diffusion models with irregular features.

We develop natural parametric (NatPar) insurance as the natural next step from natural-catastrophe (NatCat) modelling: the same hazard-exposure-vulnerability-finance machinery, with a parametric index made contractual in place of indemnity loss adjustment. Our aim is practical - a standard approach inspired by how the catastrophe-insurance industry already operates, not another optimal-contract criterion. This delivers two payoffs. First, it fixes how reporting is formulated: NatPar contracts are reported in the native NatCat language (annual average loss, EP/AEP/OEP curves, return-period levels), complemented with two-sided basis-exceedance diagnostics (BEP+/-) elevated to the central status the EP curve holds for losses - the canonical distributional view of basis risk, not a supplementary number. Second, the same standard shows how the tail is reallocated between insuree and insurer. A frost case study yields the central result: it is about time, not average. Holding a contract AAL-neutral, the bounded payout cannot follow the unbounded exposure tail, so equalising the mean separates over- and under-payment across return periods: the insuree gains at short horizons while the insurer sheds the deep tail past a crossover of several decades. This reverses under tail dependence - when regions reach extremes jointly, bounded payouts stack and the insurer reabsorbs the deep tail.

Social interaction can improve collective learning but also amplify early mistakes. We study this tension when the credibility of social information is generated by the sender's own decision process rather than fixed ex ante. Reinforcement-learning agents make binary choices through a drift–diffusion process that jointly determines choice, decision time, and confidence; decision confidence then becomes social credibility by weighting anticipatory influence and retrospective social learning. Under balanced community exposure, the anticipatory field admits an exact quotient representation. Its local Jacobian is a scalar decision-sensitivity term multiplying the community-coupling matrix, which yields a common-mode amplification threshold and an analytical role for cross-community permeability in damping relative community differences. Monte Carlo experiments show the corresponding non-monotone performance pattern: moderate transmission accelerates correction, whereas strong transmission can lock populations into wrong consensus; low permeability instead sustains disagreement. Ablations reveal a dual role for confidence: credibility-sensitive transmission amplifies social error, while confidence-dependent private learning stabilises it. The model yields testable predictions linking sender confidence to receiver behaviour conditional on accuracy.

This paper develops the first end-to-end application of cross-sectional learning-to-rank to the S&P 500 weekly options (SPXW) zero-day-to-expiration surface, integrated with margin-aware position sizing, an abstention rule driven by model uncertainty, and a strict out-of-time integrity check. A LightGBM LambdaRank ranker scores a daily nine-strategy cross-section composed of eight delta-targeted short-put positions and a SKIP candidate, trained against a path-aware Sortino-on-bars label computed at one-minute resolution. The framework is evaluated under index-option margin requirements, a tiered fee schedule, and bid-to-mid execution assumptions across a four-window walk-forward over 2021-2024 and a strictly held-out 2025 out-of-time slice. Seven sizing methods produce out-of-time annualized Sharpe ratios between 4.31 and 5.76, with the headline method reaching a Probabilistic Sharpe Ratio of 0.964 and a sample-period maximum drawdown of -2.28%, on a single hold-out year against a walk-forward range of 1.90 to 3.11. Out of time, every method exceeds three passive benchmarks (CBOE PUT, CBOE WPUT, SPX buy-and-hold) by at least 3.84 in Sharpe ratio and five internal selection baselines by at least 3.69. A two-by-two ablation of the confidence gate against the tail-risk features places 5.05 of the 5.59 out-of-time Sharpe gap over the CBOE PUT with the ranker and the selection layer, the two risk controls adding 0.54 between them. On walk-forward, where the gate binds, neither control comes close to the headline alone and their interaction supplies most of the result. A fifteen-group feature ablation shows that removing the multiplicative regime interactions collapses walk-forward statistical confidence.

Lead-lag relationships are widely used in financial time series, and many clustering algorithms based on them have been developed. The traditional DTW-KMedoids algorithm performs well both on the synthetic dataset and the real financial dataset. However, there are still several limitations to these algorithms: low efficiency caused by high time complexity, poor mathematical properties from DTW distance, the clustering effect is sensitive to the number of clusters. To solve the problems above and improve the performance, this paper introduces three clustering algorithms: MiniRocket-KMeans, KShape, Ensemble algorithm (a combination of KShape and DTW-KMedoids) and compares their performance on synthetic and real stock datasets with DTW-KMedoids algorithm under the same trade strategy. In addition, this paper also finds the best number of clusters by maximizing the silhouette coefficient in each clustering algorithm to improve the stability of the experiment results. Our main conclusions are as follows: MiniRocket-KMeans performs best under the lead strategy, achieving a Sharpe ratio of 0.866 with a maximum drawdown controlled at -63.9%; the ensemble algorithm exhibits excellent stability; the robustness is significantly improved after finding the best number of clusters; the p-values of the hypothesis test on the Sharpe ratio of all strategies are 0.0, verifying the statistical validity of the lead-lag trading strategy. Finally, future improvement directions such as customized lead-lag matrices and optimized ensemble voting mechanisms are proposed.

Artificial intelligence (AI) interacts with climate in various ways, while a unified analytical framework of this intricate interplay is lacking. To align AI investment with climate policy, we propose such a framework integrating AI's impact on emissions, output, and climate damages into the DICE model. We distinguish between ICT-like and Industrial Revolution (IR)-like AI prospects. Calibrated to the best available evidence, we find that AI development is net polluting. Under current low abatement, ICT-like AI adds 0.1 degree C to 2100 warming, while IR-like AI adds 0.8 degree C. The associated climate costs offset roughly one-fifth and one-quarter of AI's economic gains, respectively. Meeting the 2 degree C target saves the optimal ICT(IR)-like AI investment rate by 2100 from 3.3% (5.1%) under the low-abatement scenario to 3.7% (12.7%), indicating that mitigation is complementary to AI development. We further show that the investment trade-off between AI and abatement is driven primarily by AI's economic prospects, not by its emissions footprint.

Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients are easy to interpret, but it can miss nonlinear structure. Flexible models can improve prediction, but their explanations are often post-hoc and may not describe the decision rule itself. We introduce $\texttt{findr}$, short for flexible, interpretable deep regression, a semi-structured framework for binary credit risk modelling that decomposes the logit into an interpretable structured component and an orthogonal neural residual. The orthogonalisation separates coefficient-based effects from residual nonlinear variation, while an in-processing Wasserstein penalty mitigates group disparities by comparing score distributions during training. The framework also includes diagnostics that measure the structured component's contribution to logit variation, decision agreement, and local directional consistency. We evaluate $\texttt{findr}$ in a simulation study and on eight public credit datasets using score-level accuracy-fairness frontiers. The results show that $\texttt{findr}$ behaves close to logistic regression when the signal is approximately linear, while recovering much of the predictive gain of neural models when nonlinear structure is relevant. The diagnostics identify when coefficient-based explanations remain close to the full fitted model and when residual variation must also be examined. These findings support semi-structured modelling as a practical way to make performance, fairness, and interpretability trade-offs explicit in credit risk decisions.

We examine how macroeconomic and geopolitical developments in the United States, the euro area, and China affect the currency composition of central banks' foreign exchange reserves. Using an unbalanced panel of reserve shares for 53 countries over 1999-2023, we estimate a constrained system of equations that explicitly imposes the adding-up restriction on reserve shares. The results indicate substantial persistence in reserve holdings and significant cross-currency dependence, supporting a system-wide dynamics of reserve composition. In the country fixed-effects specification (1) issuer economic size, (2) uncertainty, (3) sanctions, (4) trade linkages, and (5) issuer credibility are significantly associated with reserve allocation across currencies. With the inclusion of year fixed effects, the persistence and cross-currency dependence remain, while trade linkages and sanctions emerge as the most important determinants of reserve composition. The results highlight the importance of accounting for the compositional nature and interdependence of reserve shares when examining the determinants of global reserve holdings.

We study auctions with costly entry in which bidders have incomplete information about their affiliated private valuations prior to entry. We examine whether indicative bidding–a mechanism requiring non-binding preliminary bids before entry–can stimulate participation and improve entrant selection, and we compare its performance with unrestricted and capped entry in a controlled laboratory experiment. When entry costs are high, indicative bidding generates significantly more revenue, primarily by increasing participation beyond theoretical predictions. When entry costs are low, its predicted revenue advantage is attenuated by higher-than-predicted selection inefficiency.

We show that the key optimization results of the classical Markowitz portfolio selection theory, originally formulated for variance as the risk measure, remain available in explicit closed form under a broader class of strictly convex quadratic risk measures. The proposed framework replaces the covariance matrix with an arbitrary symmetric positive definite matrix and allows additional linear and constant terms, thereby containing various models arising in transaction cost optimization, benchmark relative optimization, covariance regularization, and factor models. Closed-form formulas are obtained for the efficient frontier, the global minimum risk portfolio, the maximum Sharpe ratio portfolio, the Capital Market Curve, the tangency portfolio, and the maximum utility portfolio. In contrast to the classical Markowitz model, the tangency portfolio does not coincide with the maximum Sharpe ratio portfolio, revealing a new geometric phenomenon. A numerical example confirms the derived formulas.

This work complements our previous paper, which studies borrower-side strategies in decentralized lending markets, by focusing on lender-side capital allocation. We consider a lender who seeks to allocate a fixed budget across multiple markets sharing the same supplied asset. Accounting for the impact of supplied capital on lending rates, we derive closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). Backtests are conducted first on USDC and then on WETH Morpho lending markets on Ethereum. We also show that, under the kinked rate model, an allocation that brings a market exactly to the kink is never optimal on the lender side, whereas it can be optimal on the borrower side. This asymmetry may create tension between lenders and borrowers around the kink and thereby exacerbate rate volatility.

We study long-range correlated Wigner-type matrices built from row-independent stationary Gaussian sequences. For exponentially decaying (AR(1)) correlations, the bulk spectral density deforms from the semicircle law via an explicit combinatorial "hub" mechanism, yet we verify the flatness and decay hypotheses of the matrix-Dyson-equation framework (MDE), with numerical evidence supporting Tracy-Widom edge universality for every fixed $\rho<1$ of the exponential decay correlations; the degenerate limit $\rho\to1^-$ reduces to a symmetrized Volterra operator, connecting to the singular-value cascade identified in a companion BBP analysis. For power-law correlations $dt\sim t^{-\gamma}$, we identify $\gamma_c=1/2$ as the critical point for divergence of the bulk fourth-moment, while $\gamma=1$ marks the breakdown of the flatness condition governing the MDE edge analysis. We prove the fourth-moment transition exactly and find numerically that the self-consistent edge varies smoothly across $\gamma=1$, with no evidence of a kink or discontinuity.

We study equilibria in a closed, fee-free constant-function market maker (CFMM) economy with two assets and two traders. An interior state is a unilateral no-trade equilibrium exactly when the CFMM marginal price equals both traders' marginal rates of substitution. For an interior initial state, individually rational unilateral equilibria are Pareto optimal relative to the fixed CFMM invariant. A weak representative agent is obtained at each fixed equilibrium by weighted sup-convolution, whereas a state-independent strong representative agent exists exactly when traders share a common homothetic preference. Every interior feasible state is reachable through finitely many valid trades, and alternating utility-maximizing trades converge to a Pareto optimal unilateral equilibrium. We also derive conditions under which trading order produces a first-mover advantage or disadvantage in the first round.

This non-technical survey introduces the topic of groundwater management to the mathematics and statistics communities. We focus on the emerging marketplaces for groundwater pumping rights, outlining probabilistic and statistical approaches to groundwater dynamics and game equilibria that underpin stakeholder decisions. After discussing rationing schemes and regulatory policies we list a range of open problems that are well suited to be tackled by the mathematical sciences researchers.

Backtests of trading strategies are often selected after many parameter trials. A strong historical result can therefore reflect search luck rather than a persistent signal. Standard summaries such as return, Sharpe ratio, and drawdown do not record how many candidates were tried, whether the selected rule survives out-of-sample validation, or whether the available history is long enough to support the result. This paper describes the MinervaScore, a post-selection robustness grade for trading strategies. The score combines four established validation quantities: Deflated Sharpe Ratio, Probability of Backtest Overfitting, Superior Predictive Ability, and Minimum Track Record Length, with a regime-stability diagnostic. These components are converted into signed margins from their admissibility thresholds, aggregated into a raw score, and then mapped to a 0-100 display. The display is tied to a binary Robustness Seal: scores of 80 or higher are shown only when all five gates pass. The calibration uses 359,062 production backtest records. The score is intended to rank statistical support, not to estimate the probability of future profit. In synthetic markets with known ground truth, the MinervaScore separates true signal from lucky backtest outcomes, with an AUROC of 0.989 at the headline difficulty. Its improvement over the GT-Score proxy and the gates-passed baseline is modest, and it remains close to the corrected DSR-alone baseline. In a pre-registered test on unseen real-market data, the score showed no significant forward relationship in a population with limited surviving edge (Spearman rho_s = 0.013, one-sided permutation p = 0.40). We therefore present the MinervaScore as an auditable validation and reporting layer, rather than as evidence of demonstrated real-market predictability.

Artificial intelligence (AI) is transforming measurement in economics. AI models convert unstructured data, such as text and images, into structured variables at low cost, making previously prohibitive measurement feasible at scale. This shifts the bottleneck from finding any scalable measure of a phenomenon to choosing among many plausible ones, which may support different empirical conclusions. This review provides guidance for navigating that shift. We describe three stages at which AI enters the measurement pipeline—discovery, construct definition, and observation—and what each demands of researchers. We argue that credible inference with AI-generated variables requires appropriately designed validation: anchoring measurement to explicit criteria, rather than informal claims that a proxy is reasonable. We then examine how validation samples support valid inference even when AI predictions are arbitrarily biased, and what can be done when a random validation sample is unavailable.

Systematic trading rests on one article of faith: that regularities found in the past persist. This paper does three things. First, it states that faith as five axioms, each a commonplace practitioners already accept: (A1) a decision may use only what was known when it was made; (A2) what looks like the market changing its rules is the market changing its unobserved state, the machinery being the same in every era; (A3) the future may replay stretches of the past, though not in history's proportions; (A4) states persist for a while; (A5) whatever predictability exists is slight, even for a rule that knows the state. What turns these into axioms is quantification, and the quantities are declared rather than estimated: an invariance defect $\varepsilon_0$, a recurrence bound $\Lambda$ at a block scale $b$ (one declaration in two parts), coherence times $\ell_i$, a signal ceiling $\rho$ and an invariance ratio $\kappa$. These five declarations are the whole of the premises' empirical content. Second, it proves that the axioms force a five-stage canonical form for a quantitative investment system – a declared representation, a predictor within a capacity ceiling, contiguous purged block evaluation aggregated by $\mathrm{CVaR}_{1/\Lambda}$, a budgeted and deflated search, robust sizing at a fraction of the estimate that the budget bounds – each stage necessary: a procedure omitting it does strictly worse under a law the axioms admit. Third, it tests the axioms where they are falsifiable, each only at its declared constants, on real market series: no axiom is so far overturned.

KellyBoost is a single multi-output XGBoost model whose softmax output is the portfolio: with y the vector of per-asset holding-period returns, the training loss is - log(1 + w y), the negative log growth rate, so the fitted model is the growth-optimal (Kelly) allocation conditioned on the features. The objective is exact rather than a surrogate: we derive the gradient, the analytic diagonal Hessian and the full Hessian in closed form, verify them by finite differences, and ship a dependency-free reference engine.

Constitutions as the formal foundation of a country's legal and political system have important economic and political effects. Yet, we still know little about why constitutions set effective constraints on politicians in some societies, while being largely disregarded in others. Here, we ask if national culture matters for constitutional compliance. We study a cross-section of 115 countries, making use of novel indicators of constitutional compliance. We find that societies with a more individualistic population exhibit higher levels of compliance. These results are robust and extend to instrumental variables estimations. They imply a novel transmission channel from cultural traits to long-term economic development: individualistic national culture increases the credibility of constitutional self-commitments. Our analysis also supports the more general idea that the effects of formal institutions depend on the informal institutional environment in which they are embedded. Regarding religion, our results are consistent with past research that attributes the lack of development in the modern Muslim world to deficient institutional quality.

Physical crash probabilities recovered from option prices depend on a pricing kernel and on a risk-neutral distribution that finitely many bid and ask quotes do not identify. For a power utility investor, we characterize the pairs of physical crash probability and expected loss below the crash threshold that the quotes admit; the boundary of this set is the physical crash frontier. Both coordinates are ratios of moments, yet when the index is bounded above the closed set is convex, and under an interior regularity condition second-order cone programs compute it exactly at the benchmark risk aversion of two. In a decade of weekly S&P 500 cross sections, the quotes beyond the two puts nearest a 10 percent decline shrink the range of admissible crash probabilities by about 80 percent, yet its upper end remains two to three times its lower end. Under strict quote slack and the interior-mass condition stated below, removing the cap without further tail control drives the lower bounds on crash probability and unconditional shortfall to zero for risk aversion above one. A vanishing probability far in the right tail inflates the normalizing moment while every quote remains inside its spread. In this setting, a positive floor requires additional tail information, supplied here by the support cap.

Tick-level trade-and-quote data for the Tokyo Stock Exchange is distributed through the Nikkei NEEDS service as thousands of zipped CSV archives spanning four data types with era-dependent schemas and Japanese-language layouts. We present tse_tick, an open-source Python library that converts these raw archives into clean, typed Polars DataFrames and a Hive-partitioned Parquet store queryable through DuckDB. The library offers two access paths sharing one parse-and-clean core: a one-shot reader that returns a ticker- and time-filtered DataFrame directly from raw ZIP files, and a two-stage ingest-then-query pipeline with resume-safe, memory-aware parallel ingestion, part-pruning, and a materialized intraday time key for row-group pruning. The engineering, more than the parsing, is what the library contributes: ingestion runs in per-date atomic units whose completion is recorded by coverage markers rather than inferred from file existence, writes stream in bounded morsels so that peak memory is independent of trading-day size (24.5 GB to 2.4 GB on the worst measured day), a RAM-aware process pool sizes itself to available memory, and part-pruning opens only the archive parts a ticker can occupy. Full English and Japanese column definitions ship for all four types, and a translation layer maps yfinance, Polygon, and ccxt names onto their tse_tick equivalents. In benchmarks on a commodity 16-thread workstation, parsing a representative 4.8-million-row archive part, one of a trading day's nine parts, is 59.8x faster than the original pandas prototype (34.3x against an engine-matched pandas baseline), and a single-ticker time-window query from the store completes roughly 410x faster than a pandas scan of the equivalent CSV. tse_tick is available on PyPI (pip install tse-tick) under the MIT license.

What happens to a party's vote share when it nominates a woman for executive office? The evidence on female candidates is dominated by legislative races and by designs that pool parties and periods into a single average effect, which is typically null. This paper builds a new county-level panel of US gubernatorial elections - 578 races in 49 states from 1980 to 2024, reconstructed and audited from primary sources - and estimates party-specific, time-varying nomination effects with a stacked difference-in-differences design around within-state transitions from male to female nominees. The average null conceals a sharp asymmetry. Republican transitions to a female nominee since 2016 are followed by a county-level vote-share decline of 6.5 percentage points, robust across cohort, weighting and inference checks. No comparable average differential is detected for Democratic nominees. Survey-ballot diagnostics are compatible with demand-side discrimination but cannot identify that channel.

In this paper we propose a new formulation of the Bayesian Filter as used in the discrete-time Markov-Switching-Multifractal (MSM) model of volatility based on existing permutation symmetry within the likelihood structure. We show both analytically and empirically that such a formulation leads to a reduction in time complexity from $O(D^k)$ to $O(k^D)$ thereby significantly reducing the computational bottleneck associated with dimensionality. We compare the agreement between the naive and sector filters and find that while there are significant disagreements, the ground-truth recovery of the latter seems to improve on the former.