Off-policy evaluation (OPE) estimates the value a candidate decision policy would achieve, using only data logged by the policy already in production. In debt collections, unrecorded factors can influence both actions and outcomes. This paper presents JUBILEE, a benchmark environment built around a debt-collections simulator that generates million-loan panels with competing risks, rare recovery events, and hidden factors driving both outcomes and assignment. The evaluation includes an 80-run comparison of tabular and gradient-boosted estimators under two synthetic outcome mechanisms and a 190-run parameter grid. Independent policy simulations provide reference values with estimated Monte Carlo uncertainty for a synthetic reward score and for simulated cash. With hidden factors influencing action assignment, several tabular methods agree closely while underestimating the field-visit policy's score, an error that also appears with boosted models. Intervals from tabular doubly robust estimation usually contain the reference without hidden assignment dependence but mostly miss it when that dependence is present. Estimates from randomized decisions do not show the same large error, although their limited precision leaves policy choice uncertain. The decision consequences depend on how policy value is defined. The field-visit policy ranks last under the score and first under simulated cash, which excludes channel costs. Under cash, log-based estimators miss this best candidate in nearly every run with hidden assignment dependence in the paired study. JUBILEE supports reproducible study of these failure modes under specified collections mechanisms.
Options market makers absorb client order flow and must hedge the resulting inventory risk under trading frictions. We study whether an option hedging reinforcement learning (RL) agent trained on historical market data outperforms three alternatives in tail-risk protection: (i) traditional delta hedging, (ii) simple hedge-ratio rules, and (iii) the same agent trained in simulated markets. Most RL hedging agents are trained in model-generated markets that do not capture the crashes and volatility regimes present in real data. We introduce an empirical replay environment using S&P 500 option data from 1996 to 2017 and compare against a matched geometric Brownian motion (GBM) environment, a stochastic-volatility (SABR) simulator, and a hybrid that values historical paths with Black-Scholes, all sharing the same portfolio mechanics, stochastic arrivals, costs, and test period. We train a distributional Twin Delayed Deep Deterministic Policy Gradient (TD3) agent and evaluate it out of sample over 2018-2023. The agent trained on empirical data reduces Conditional Value-at-Risk (CVaR) tail loss by 3.1% compared to delta hedging and outperforms every hedge-ratio rule chosen during the training period and every alternative training environment. The reduction reaches 8.9% in volatile and falling markets, but reverses to a cost of 7-10% in rising markets. Empirical training induces a state-dependent short net delta: the policy under-hedges negative-delta books, most strongly after prices have fallen. This tilt disappears with GBM training and is weaker with SABR or hybrid training. These findings suggest that exposure to historical market dynamics can materially change a tail-risk hedging policy, although the evidence rests on a single test path and the edge is regime-conditional.
A hedging objective reflects the risk preference of the hedger, so the conventional approach is to estimate the hedge ratio directly under the chosen objective. This paper asks whether that estimate should be used alone. We construct five objective-specific rolling hedge-ratio estimators---minimum variance, least absolute deviation (LAD), downside semivariance, lower-tail quantile, and Expected Shortfall (ES)---from the same historical stock--market returns, and combine the resulting estimates under a fixed final evaluation objective. Separate multi-output ExtraTrees models predict the within-stock-date ranks of the five candidates under MSE, LAD, downside semivariance, and 10\% quantile loss. The design uses pre-specified 60-, 90-, 120-, and 180-day windows with objective-matched rolling estimators as benchmarks. Adaptive weighting improves the corresponding loss in all sixteen objective--window comparisons. Equal weighting, which uses the same objective-specific hedge ratios without state variables or learned reweighting, already improves downside and quantile hedging. The results therefore suggest that estimates generated under other objectives can be useful even when the final hedging preference and evaluation objective are fixed, while adaptive weighting can provide a further improvement.
Corporate annual reports describe hedging through transactional, policy, accounting, and explicit non-use language. This study presents AWARE-FX, the foreign-exchange application of the Auditable Weak-supervision and Aggregation framework, for constructing traceable firm-year disclosure measures. Professional-source retrieval, evidence-state rules, channel classifiers, and top-three aggregation link each score to supporting passages. The application covers 24,909 Hong Kong firm-years from 2008-2025 and 543,527 candidate snippets. Evaluation combines weak-label held-out tests, three-seed encoder comparisons, a 2023-2025 temporal test, calibration diagnostics, selective prediction, and a stratified 300-snippet audit coded by one researcher. The paired temporal tests do not detect statistically significant differences between FinBERT and ModernBERT. The negation-aware lexicon has higher broad-task audit F1 than strict-gate FinBERT and Qwen3-8B, demonstrating that learned models do not uniformly improve on domain rules. At 80% temporal coverage, retained-sample F1 increases by 0.050-0.077, although the study does not measure human review time or deployment impact. Reproducible Qwen3-8B results show substantial variation across evidence channels. Linked exposure associations provide exploratory external validation, with sensitivity to the multiple-testing family. The scores measure detected hedging disclosure rather than hedge notional or causal effectiveness. The contribution is an evaluated measurement workflow with inspectable evidence and explicit validation limits. Independent duplicate annotation and retrieval-recall assessment remain necessary for stronger reliability claims.
In insurance, experience rating plays a crucial role in mitigating adverse selection by adjusting premiums according to historical claims experience. A key step in this process is the classification of insured collectives into homogeneous risk groups. Within group life insurance, this classification traditionally relies on manual decisions and arbitrary thresholds, which may lead to inefficiencies and inconsistent outcomes. This paper proposes a data-driven methodology for automating risk group allocation based on a combination of the multidimensional B\"uhlmann-Straub model and k-means clustering -- referred to as credibility clustering. By incorporating credibility weights, the method accounts for the varying statistical significance of experience data and ensuresthat risk classifications are both actuarially sound and practically implementable. Empirical results based on real-world group life insurance data from a major European insurer demonstrate that the proposed framework outperforms the two k-means benchmark variants considered in the empirical application in terms of viability, temporal stability and accounting for statistical reliability.
Recurring cash flows are the backbone of cash-flow underwriting: they separate durable revenue from one-off deposits, expose standing obligations, and ultimately determine ability to pay. For small businesses, recurrence is both indispensable and hard to estimate, because much of their income arrives as ambiguous transfers, most payment streams are short, and schedules follow calendar conventions that frequency-domain methods treat as noise. We present CADENCE, Calendar-Aware Distributional Event Network for Cash-Flow Estimation, a compact causal Transformer that treats each transaction as a token and predicts the amount and a full, nonparametric timing distribution of every cash flow's next payment. Inheriting transaction semantics from an external labeler and a frozen text encoder keeps CADENCE compact yet general across the long tail of small-business counterparties, while predicting a calibrated distribution rather than an opaque embedding makes its outputs interpretable enough to feed existing underwriting models and power credit decisions. Ablations confirm the role of each design choice, with counterparty semantics driving recall on short histories, calendar inputs driving timing accuracy, and no-event supervision driving precision. Against the production FFT baseline, CADENCE improves one-day timing accuracy by 93.3% and next-event F1 by 6.7%, while producing calibrated distributions that serve directly as a confidence gate for downstream decisions. In an end-to-end credit evaluation on production applications, replacing FFT recurrence features with CADENCE features raises KS by 4.8%, raises the approval rate at a fixed loss rate by 18.2%, and lowers the loss rate at a fixed approval rate by 13.1%.
Trading discipline often fails people. Our study tested whether four AI trading agents followed their strategies and risk rules. Offering traders the evidence and an agent evaluation toolkit to separate luck from following the rules, before giving agents meaningful autonomy or capital. Our study analysed 2,627 closed positions from 12 February to 27 August 2026 across the same asset universe and execution conditions. Most positions were simulated against live prices with a 0.09% round-trip fee; 18% involved real Hyperliquid orders. Each position declared a strategy, stop and target, with a limit of five open positions per agent. The fleet finished net positive after fees. Rule adherence was high across all four agents, including the unprofitable one. Profitability tracked how often agents won relative to their break-even win rates. Institutional-style controls ran by default and contained losses through a 29% market drawdown and recovery, with documented exceptions. The discipline held for all four agents. Agents kept 97.5% of gross loss within declared stops, and typical losses closed at declared levels. Declared risk remained stable after losses and broadly stable across calm and volatile months. Twenty-three of 24 runs respected the position limit; logs identify the exception's size and duration. The same live data produced different results across assigned strategies. The trade-log tests can be rerun from the published trade snapshot; regime tests also require the published Bitcoin prices and regime labels. Section 3.5 specifies the stop-breach tolerance. These trade-level results are not account-level returns or proof of a lasting edge. Our study provides an evaluation toolkit to help traders distinguish rule-following from luck before committing capital. For anyone considering allocating capital to agents to trade, discipline should be the entry test, not the only test.