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arXiv preprints from January 1, 2026 through September 23, 2026 — 05:28:12 EST

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Posted in math.AG · 2026-08-18 · Avik Chakravarty, Daeboem Choi, Shengjing Xu

Counterexamples to Sato's Weak F-Equivalence Conjecture and a Gorenstein Refinement

We disprove Sato's weak \(F\)-equivalence conjecture for nonsingular projective toric weak Fano varieties in every dimension \(d \geq 3\). Our counterexamples are smooth projective crepant models of centered reflexive simplices. The key input is a rigidity property of ray polytopes: if \(X_Σ\) is nonsingular and complete and...

💬 0 commentsarXiv:2608.18054v1PDF
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Posted in quant-ph · 2026-08-18 · Mateo Cárdenes Wuttig, Joseph Tindall

A Complete Classification of Complex Hadamard Matrices of Order Six

Complex Hadamard matrices encode perfectly balanced unitary transformations. They underlie mutually unbiased quantum measurements and multiphoton interferometry. Their classification is complete through order five, but order six -- the first dimension in which several continuous families coexist with an isolated solution -- has...

💬 0 commentsarXiv:2608.18053v1PDF
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Posted in q-fin.PR · 2026-08-17 · Li Chen, Liang Wang, Weixuan Xia

When ratios fall: A dynamic approach to contingent convertibles

We propose a novel valuation framework for contingent convertible (CoCo) bonds based on the issuing bank's Common Equity Tier 1 (CET1) ratio, which is widely acknowledged as an indicator of a bank's solvency. Our approach develops a bivariate jump-diffusion model that captures the dynamic relationship linking the CET1 ratios, share...

💬 0 commentsarXiv:2608.16842v1PDF
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Posted in q-fin.MF · 2026-08-17 · Saad Mouti

Rough Volatility Across Assets

We measure volatility roughness across asset classes using a common data infrastructure and pipeline. Our data covers 3,926 United States equities, 34 CME futures roots, rates, FX, and commodities, and options on 44 underlyings over 2010-2025. Realized volatility is rough everywhere. The class-median Hurst estimate ranges from $0.05$...

💬 0 commentsarXiv:2608.16749v1PDF
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Posted in cs.LG · 2026-08-16 · Arishi Orra, Himanshu Choudhary, Manoj Thakur

Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading

Reinforcement learning has gained increasing attention as a data-driven approach for stock trading. However, learning a policy that is both profitable and stable remains challenging due to non-stationary market behaviour and noisy reward signals. Auxiliary tasks are often used to improve representation learning and stabilize training,...

💬 0 commentsarXiv:2608.15841v1PDF
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Posted in q-fin.MF · 2026-08-16 · Hao Liu, Yang Liu, Zhenyu Shen

Behavioral Participating Insurance: Optimal Investment under Probability Distortion and Aspiration Constraints

We study optimal investment for insurers managing participating (profit-sharing) contracts under probability distortion and probability benchmark (aspiration) constraints. The problem combines three theoretical complexities: (i) nonconcave effective utilities induced by embedded guarantees and surplus-sharing rules, (ii) probability...

💬 0 commentsarXiv:2608.15743v1PDF
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Posted in q-fin.PM · 2026-08-16 · Jaegi Jeon, Jeonggyu Huh, Hyeng Keun Koo, Byung Hwa Lim

Scalable Pontryagin-Guided Adjoint-to-Control Recovery for Constrained Dynamic Portfolio Choice

We develop a scalable adjoint-to-control framework for continuous-time portfolio choice under smooth pointwise constraints. A feasible direct-policy-optimization (DPO) policy supplies rollouts; after training, fixed-latent open-loop BPTT (OL-BPTT) yields first- and second-order pathwise sensitivities, whose conditional projections...

💬 0 commentsarXiv:2608.15667v1PDF
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Posted in cs.CR · 2026-08-16 · Ruichao Jiang, Michelle Yeo, Long Wen

A contribution to the critique of blockchain censorship

We study the blockchain censorship attack introduced in [21], which shows that joining the attack is a dominant strategy. We show that, by introducing certain detectability threshold, joining the attack can lead to strictly less reward for whales, which are defined to be a small number of validators that hold significantly more voting...

💬 0 commentsarXiv:2608.15640v1PDF
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Posted in cs.CE · 2026-08-16 · Rischan Mafrur, Fadli Ikhsan Pratama, Khadijah

Toward Decentralized Carbon Trading in Indonesia: A Public-Blockchain Architecture for Tokenized Real-World Assets

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...

💬 0 commentsarXiv:2608.15597v1PDF
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Posted in cs.LG · 2026-08-15 · Emmanuel Nahimana, Yaé Ulrich Gaba

Detecting Money Laundering in Rwandan Mobile Money: A Machine Learning Framework

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...

💬 0 commentsarXiv:2608.15447v1PDF
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Posted in q-fin.RM · 2026-08-15 · Andreas G. F. Hoepner, Blerita Korca, Frank Schiemann, Fabiola I. Schneider

Is the medium the message? Social disclosure channels and firm risk

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...

💬 0 commentsarXiv:2608.15212v1PDF
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Posted in q-fin.RM · 2026-08-15 · Nader Karimi, Foad Shokrollahi

Pricing Temperature-Index Insurance under Long Memory and Stochastic Time Change

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...

💬 0 commentsarXiv:2608.15097v1PDF
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Posted in q-fin.ST · 2026-08-14 · Ang Zhang

Disclosed Human-Capital Disruption and Firm-Specific Risk

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...

💬 0 commentsarXiv:2608.14859v1PDF
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Posted in econ.GN · 2026-08-17 · Bruno Crépon, Aurélien Frot, Christophe Gaillac

Targeting Support Using Job Seekers' Biases: A Randomized Experiment

Most digital job-search assistance encourages unemployed workers to broaden their search toward related occupations, targeting one important source of search inefficiency: insufficient occupational diversification. Our analysis suggests that the relevant margin of adjustment depends on the underlying search problem. Building on a...

💬 0 commentsarXiv:2608.16849v1PDF
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Posted in econ.GN · 2026-08-17 · Bruno Crépon, Aurélien Frot, Christophe Gaillac

Biases-Informed Job Search Guidance: Characterization, Implications, and Targeting Support

Job seekers' expectations about reemployment are increasingly used to study job search, but what their biases reveal about underlying beliefs and preferences is ambiguous. We combine new survey data, structural modeling, and machine learning to uncover the informational content of these expectations and show how they can be used to...

💬 0 commentsarXiv:2608.16827v1PDF
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Posted in econ.EM · 2026-08-17 · Tatiana Komarova

Quantile restrictions, revealed rankings, and the limits of multinomial choice

This paper analyzes when choice probabilities reveal rankings of deterministic utility indices in semiparametric discrete choice models. It begins with binary choice, where quantile thresholds guarantee ranking recovery, and shows that such thresholds can arise either from behavioral departures from utility maximization (e.g., limited...

💬 0 commentsarXiv:2608.16708v1PDF
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Posted in cs.GT · 2026-08-17 · Maria-Florina Balcan, Tejas Pagare, Karan Singh

Learning to Price with Persuasion

Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design. Specifically, we consider the economic setting recently introduced by Bergemann et al. (2022), where in addition to the...

💬 0 commentsarXiv:2608.16699v1PDF
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Posted in econ.TH · 2026-08-17 · Zihan Zhao

Social Learning with Selective Sampling

This paper studies how robust social learning is when sampling is selective, i.e., some types of actions are more likely to be sampled by successors. We show that Bayesian agents can achieve asymptotic learning despite non-expanding observations, because the endogenous observation network itself carries information and agents have...

💬 0 commentsarXiv:2608.16599v1PDF
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Posted in econ.EM · 2026-08-17 · Ying Zeng

Estimation and Inference for Peer Effects under Conditional Random Assignment

Empirical studies of peer effects often exploit conditional random assignment to peer groups within urns. We develop a GMM framework for estimation and inference in this setting. The framework separately identifies endogenous and contextual peer effects and nests tests of random peer-group assignment as a special case. It permits...

💬 0 commentsarXiv:2608.16468v1PDF
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Posted in econ.EM · 2026-08-17 · Fernando Delbianco, Federico Fioravanti, Fernando Tohmé

The Best Are Always the Best: COVID-19 Lockdown Stringency and the Dispersion of Olympic Medal Outcomes

We ask whether COVID-19 lockdown stringency altered national Olympic performance between Rio 2016 and Tokyo 2020, using the Oxford Stringency Index and the 99 countries that won a medal in either edition. As in \citet{liu2024}, mean performance is unaffected: stringency is insignificant in every OLS and ANOVA specification. The...

💬 0 commentsarXiv:2608.16325v1PDF
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Posted in econ.EM · 2026-08-17 · Fernando Delbianco, Federico Fioravanti, Fernando Tohmé, Martín Trombetta

Regional advantage in rugby sevens: Is there a home effect when nobody is home?

We study the existence of a \emph{Regional Differential} in rugby sevens: whether, in tournaments where no competing team enjoys formal home status, some national sides systematically over- or under-perform depending on \emph{where} the event is staged. Using the universe of 2672 men's and women's matches from international rugby...

💬 0 commentsarXiv:2608.16312v1PDF
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Posted in econ.GN · 2026-08-17 · Sitian Liu, Yichen Su

The Geography of Research: The Trade-Off Between Knowledge Production and Access

Research activity generates highly localized positive spillovers, yet in the U.S. it has become increasingly spatially misaligned with population and economic activity as people moved away from legacy cities where major research institutions remain anchored. Reallocating researchers toward population centers could broaden local access...

💬 0 commentsarXiv:2608.15981v1PDF
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Posted in econ.GN · 2026-08-16 · Remy Levin, Daniela Vidart

The Yeoman's Portfolio: Measuring Historical Risk Preferences Using Crop Choice

We design a method for measuring the risk preferences of agents in the deep past. The method combines a structural model of crop choice as a portfolio allocation with machine-learning prediction of expected crop returns, using historic agronomic and climate data. We estimate county-level risk preferences for the United States and...

💬 0 commentsarXiv:2608.15876v1PDF
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Posted in math.ST · 2026-08-16 · Jikai Jin

How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM

We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that independent non-Gaussian disturbances identify the direction, but does not quantify the difficulty when the causal effect is weak or the disturbances are nearly Gaussian. Let $β$ bound...

💬 0 commentsarXiv:2608.15840v1PDF
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Posted in cs.GT · 2026-08-16 · Louise Demoor, Martí Jané-Ballarín, Pierre Nunn, Subhajit Pramanik, Antoine Prévotat, Makoto Yokoo

Non-obvious Manipulability with Groups in Shapley-Scarf Housing Markets

In Shapley-Scarf housing markets, Ma (1994) shows that top trading cycles (TTC) is the unique mechanism satisfying individual rationality (IR), Pareto efficiency (PE), and strategy-proofness. We ask what other mechanisms become possible when strategy-proofness is replaced by a weaker condition called non-obvious manipulability (NOM),...

💬 0 commentsarXiv:2608.15631v1PDF