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新论文 · v1arXiv:2611.14699

Conformal Risk Certificates for Portfolio Optimization Under Uncertainty: A Gated Reinforcement-Learning Approach

Mohammad Zarei, Amirhossein MohammadpourAzari, Moslem Habibi

原文摘要

Reinforcement-learning portfolio policies can optimize allocations from prices, covariances, and prior positions, yet they lack a calibrated, replayable mechanism for certifying that a proposed allocation respects a bounded risk envelope before execution. Regulators increasingly expect automated portfolio systems to provide exactly this kind of pre-trade audit trail, interpretable risk limit, and human-oversight mechanism. Gated Conformal Semantic Risk Control addresses this gap by separating return-seeking allocation from text-conditioned downside-risk verification. An instruction-tuned language model maps timestamped disclosures into schema-validated semantic risk states over macro, liquidity, earnings, and sector channels. These states induce correlated ellipsoidal factor-shock uncertainty sets whose conformal radii are calibrated by rolling finite-sample prediction over localized semantic nonconformity buckets. An asymmetric second-order cone gate then checks each raw PPO proposal against a certified worst-case semantic loss budget. The gate stays inactive for compliant proposals and intervenes only when that budget is exceeded. In walk-forward out-of-sample tests on nine liquid U.S. Select Sector SPDR ETFs, Raw PPO earns a 12.7% annual return while incurring a 4.05% semantic violation rate. Gated-CSRC removes all observed out-of-sample semantic limit breaches while retaining an 11.7% annual return. This 100-basis-point auditable safety premium is smaller than the yield loss imposed by unconditional hard projection, and exact McNemar testing confirms the compliance gain at $p < 10^{-15}$. Loughran-McDonald lexicon controls and time-shuffled text controls fail to reproduce this certificate.

q-fin.PM · cs.LG · math.OC · q-fin.RM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.27611

AWARE-FX: Auditable measurement of corporate foreign-exchange hedging disclosure

Qi Wang

原文摘要

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.

cs.CL · q-fin.RM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10972

JUBILEE: A Benchmark Environment for Off-Policy Evaluation in Debt Collections

Diego Silva Prado, Tiago Amaral Penteado de Jesus, Bruno Caldas de Souza, Cristiano Bevitori Maffia De Oliveira

原文摘要

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.

cs.LG · q-fin.RM · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10956

Risk-Sensitive Reinforcement Learning for an Options Market Maker: An Empirical Study with Real S&P 500 Options

Jacky Chen, Ing-Haw Cheng, John Hull, Landis Li, Zissis Poulos, Justin Shi, Andreas Veneris, Zeyu Wang

原文摘要

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.

q-fin.RM · cs.LG · q-fin.CP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10607

Worst-Case Contagion in Financial Networks: MILP Clearing and Robust Loss Analysis

G. C. Calafiore, M. Della Rossa, A. Proskurnikov

原文摘要

We study worst-case contagion losses in financial networks with interbank liabilities and common exposures to marketable assets. Existing linear-programming approaches provide exact stress-test values only within an insolvency-resilience margin, where all institutions remain solvent with respect to the external sector. We show that this margin is computed exactly by a finite family of linear programs, one per vertex of the uncertainty ball, whereas a single linear program based on the componentwise worst deterioration of external resources yields only a certified lower bound. We extend this framework beyond that margin in three directions. First, we derive an exact mixed-integer linear programming (MILP) formulation for the maximal clearing vector under arbitrary signed external net positions, providing a clearing oracle for robust stress testing when insolvency can occur. Second, we develop certified bounds on the worst-case loss beyond the margin, including insolvency-pattern enumeration, scenario lower bounds, fixed-pattern dual upper bounds, localized certificates, and a regime-generation procedure with monotone gap certificates. Third, we analyze the worst-case loss curve as a function of the perturbation radius. For $\ell_1$ and $\ell_\infty$ price-perturbation sets, the curve is convex, nondecreasing and piecewise linear up to the margin, with breakpoints generated by switches in worst-case shock directions, LP bases, and bank-level activity patterns. Beyond the margin, it remains nondecreasing and piecewise affine, but convexity may fail, the curve may jump upward, and the worst case need not be attained. The framework provides a certifiable MILP/LP methodology for parametric stress testing and interpretable diagnostics identifying the banks responsible for changes in systemic loss.

q-fin.RM · cs.CE · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07110

Credibility Clustering for Experience Rating: An Automated Approach to Risk Group Allocation in Group Life Insurance

Thom van Rijn, Frank Weber, Michael Merz

原文摘要

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 Bühlmann-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.

q-fin.RM · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03022

Interpretable Allocation and Model-Predictive Rebalancing of Equity Portfolios, Tested on Real and Synthetic Markets

Jeffrey D. Varner

原文摘要

A live equity portfolio poses three problems: which stocks to hold, when to trade, and whether the strategy holds up beyond the observed market. We built one system for all three and tested it on 373 US equities, calibrated on 2014-2024 and traded from January 2025 to September 2026. A selection rule chose stocks by score, by a bandit, or at random, and one of seven weighting rules, four consumer-theory utilities, and three standard portfolios, set share counts under limits on budget, single-stock weight, portfolio volatility, and turnover, with each stock's preference weight a bounded score of its trend, market beta, and the market's recent direction. A model-predictive controller forecasts portfolio value with a multi-asset geometric Brownian motion and re-planned at its horizon, when the value left its forecast band, or at a drawdown stop. To probe deeper declines than the real window held, we also ran the system on simulated markets from per-asset hidden Markov generators with jumps, linked by market and sector factors. On real prices, the stocks chosen mattered more to terminal wealth than the weighting rule, and the early re-plans shrank the worst drawdown in most rule combinations against a fixed calendar without consistently raising wealth. In the deepest simulated declines, every combination, random picks included, ended above simulated SPY and the equal-weight universe in the median, even without early re-plans, so the margin came from shared parts of the system, not from stock selection or re-plan timing. A market hedge that tightened a beta cap as the market weakened cost wealth on real prices but paid off in the most sustained simulated declines, and the generator's persistent trends favored score-based selection more than the real market did, which suggests a synthetic test judges stock selection fairly only if its generator does not build in that persistence.

q-fin.PM · cs.CE · q-fin.RM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.22522

Agentic AI in Automated Markets

Irene Aldridge, Lourenco Belleau-Bhowmik, James Chan, Josh Chang, Kelly Deng, Pranav Dhingra, Xinda Ding, Haodong Fang 等 33 位作者

原文摘要

Automated markets increasingly host autonomous, learning trading agents and are powered by formulaic automated market makers (AMMs). We study how heterogeneous agent behavior shapes stress, risk-adjusted performance, liquidity, and volatility in these types of markets. Three behavioral archetypes are modeled as agentic reinforcement learning (RL) systems: a rational arbitrageur, a manipulator, and a retail herd-follower. Each archetype is defined by its reward function within a common, asset-agnostic exposure-control environment, and each is trained with a value-based Deep Q-Network (DQN) and with REINFORCE using a learned baseline. A pilot on hourly cryptocurrency data (AAVE, ETC, SUI) motivates the hypotheses. Herd-followers produce the most frequent but shortest stress episodes, manipulators the fewest but most persistent, and rational agents the most stable rolling Sharpe ratios despite higher return volatility. SHAP attributions tie these patterns to drawdown and cross-asset signals, short-term volatility and position state, and momentum, respectively. The results motivate behavior-specific regulation rather than uniform limits on volatility or turnover.

econ.EM · cs.ET · q-fin.RM · q-fin.TR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26324

The Control Layer Behind Rule-Following Agents Trading Across Market Conditions

Saad Naja, Nasser Odtallah, Jake Van-Praag, Sarah May

原文摘要

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.

cs.AI · cs.LG · cs.MA · q-fin.RM · q-fin.TR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38084

CADENCE: Distributional Next-Transaction Forecasting for Business Cash-Flow Underwriting

Alexander Wu, Jingchao Huang

原文摘要

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%.

cs.LG · q-fin.RM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.07867

LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline

Masoud Soleimani

原文摘要

We develop a transparent, auditable large language model (LLM) pipeline for macro-financial stress testing. Structured prompting, with optional retrieval of country fundamentals and news, emits G7 macroeconomic scenarios (GDP growth, inflation, policy rate), which are converted into deviations from an IMF World Economic Outlook baseline and translated into portfolio losses through a three-channel PCA factor model. The macro-to-factor mapping is stipulated rather than estimated, so every tail-risk multiple is conditional on stated assumptions. Across 2,420 accepted scenarios from two model families and three runs, plus an unanchored-severity run and eight risk-layer ablations, we find three things. First, severity is inherited from scenario design, not generated by the model: prompt variant dominates every variance decomposition, removing the prompted severity bands makes GPT-5.6-luna's recession 1.6 pp milder, apparent country heterogeneity largely reflects the institutional baseline, and retrieval and news matter only where the prompt leaves magnitude free. Second, numerical severity and narrative severity are only weakly coupled. Third, GPT-5.6-luna follows its own arithmetic but anchors on prompted bands, whereas Llama-3.1-8B is four to seven times more dispersed and contradicts its own stated changes in 90% of cases. A repeat run differs at the scenario level yet agrees on aggregate multiples to within 0.007 times. LLM scenario generators are best used for narrative and sector elicitation around a prescribed severity path, not as autonomous calibrators of stress magnitude.

q-fin.RM · cs.AI · econ.EM

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arXiv ↗PDF ↗