When Alpha Breaks: Two-Level Uncertainty for Safe Deployment of Cross-Sectional Stock Rankers

• URL: http://arxiv.org/abs/2603.13252v1
• Date: Tue, 24 Feb 2026 14:02:24 GMT
• Title: When Alpha Breaks: Two-Level Uncertainty for Safe Deployment of Cross-Sectional Stock Rankers
• Abstract summary: Cross-sectional ranking models are often deployed as if point predictions were sufficient.<n>Under non-stationarity, rankers can fail during regime shifts.<n>We adapt Direct Epistemic Uncertainty Prediction to ranking by predicting rank displacement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-sectional ranking models are often deployed as if point predictions were sufficient: the model outputs scores and the portfolio follows the induced ordering. Under non-stationarity, rankers can fail during regime shifts. In the AI Stock Forecaster, a LightGBM ranker performs well overall at a 20-day horizon, yet the 2024 holdout coincides with an AI thematic rally and sector rotation that breaks the signal at longer horizons and weakens 20d. This motivates treating deployment as two decisions: (i) whether the strategy should trade at all, and (ii) how to control risk within active trades. We adapt Direct Epistemic Uncertainty Prediction (DEUP) to ranking by predicting rank displacement and defining an epistemic uncertainty signal ehat relative to a point-in-time (PIT-safe) baseline. Empirically, ehat is structurally coupled with signal strength (median correlation between ehat and absolute score is about 0.6 across 1,865 dates), so inverse-uncertainty sizing de-levers the strongest signals and degrades performance. To address this, we propose a two-level deployment policy: a strategy-level regime-trust gate G(t) that decides whether to trade (AUROC around 0.72 overall and 0.75 in FINAL) and a position-level epistemic tail-risk cap that reduces exposure only for the most uncertain predictions. The operational policy, trade only when G(t) is at least 0.2, apply volatility sizing on active dates, and cap the top epistemic tail, improves risk-adjusted performance in the 20d policy comparison and indicates DEUP adds value mainly as a tail-risk guard rather than a continuous sizing denominator.

Related papers

• The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models [47.471225029744424]
  Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models.<n>We show that the choice of anti-collapse regulariser determines whether a JEPA's training objective, a prediction loss plus a weighted embedding regulariser, is a valid Active Inference (AIF) variational free energy.
  arXiv  Detail & Related papers  (2026-07-15T08:59:36Z)
• Momentum for Reasoning: Dense Intrinsic Signals in Policy Optimization [31.547614109943158]
  Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for eliciting long-chain reasoning in large language models.<n>We propose ISPO (Intrinsic Signal Policy Optimization), which combines a sequence-level signal measuring how informative the thinking trajectory is for the final answer, with a token-level directional reward whose hallucinated-certainty hinge penalizes confidently-wrong predictions at critical decision tokens.<n>ISPO consistently outperforms competitive baselines, with the largest gains on the hardest benchmarks where zero-advantage collapse is most frequent, and training-dynamics diagnostics confirm that both failure modes are decreased.
  arXiv  Detail & Related papers  (2026-06-07T20:08:36Z)
• Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval [0.0]
  Financial market forecasting is inherently uncertain, yet most deep learning approaches rely on single estimates without uncertainty.<n>The problem can be solved using prediction intervals, which allow obtaining an uncertainty and lower bound for the prediction.<n>Yet, the current methods tend to disregard relationships between assets or cannot simultaneously ensure good calibration sharpness.
  arXiv  Detail & Related papers  (2026-05-05T05:06:42Z)
• EvoNash-MARL: A Closed-Loop Multi-Agent Reinforcement Learning Framework for Medium-Horizon Equity Allocation [13.877325729509058]
  EvoNash-MARL is a closed-loop framework that integrates reinforcement learning with population-based policy optimization and execution-aware selection.<n>On out-of-sample data from 2014 to 2024, it delivers a 19.6% annualized return, compared to 11.7% for SPY, and remains stable under extended evaluation through 2026.
  arXiv  Detail & Related papers  (2026-04-13T02:24:32Z)
• Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling [3.1262100364392205]
  High-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk.<n>Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability.<n>To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis.
  arXiv  Detail & Related papers  (2026-03-14T08:25:42Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Explainable Regime Aware Investing [0.0]
  We propose an explainable regime-aware portfolio construction framework based on a strictly causal Wasserstein Hidden Markov Model.<n>Relative to equal-weight and SPX buy-and-hold benchmarks, the Wasserstein HMM Regime achieves materially higher risk-adjusted performance.
  arXiv  Detail & Related papers  (2026-02-21T00:33:16Z)
• Stablecoin Design with Adversarial-Robust Multi-Agent Systems via Trust-Weighted Signal Aggregation [5.151910664667141]
  We present MVF-Composer, a trust-weighted Mean-Variance Frontier reserve controller incorporating a novel Stress Harness for risk-state estimation.<n>Our key insight is deploying multi-agent simulations as adversarial stress-testers, exposing reserve vulnerabilities before they manifest on-chain.<n>Across 1,200 randomized scenarios with injected Black-Swan, MVF-Composer reduces peak peg deviation by 57% and mean recovery time by 3.1x relative to SAS baselines.
  arXiv  Detail & Related papers  (2026-01-18T14:21:25Z)
• Ratio-Variance Regularized Policy Optimization for Efficient LLM Fine-tuning [48.34492357368989]
  We propose a primal-dual framework that supports stable on-policy learning and enables principled off-policy data reuse.<n>$R2VPO$ achieves superior performance with average relative gains of up to 17% over strong clipping-based baselines.
  arXiv  Detail & Related papers  (2026-01-06T14:01:42Z)
• Bayesian Modeling for Uncertainty Management in Financial Risk Forecasting and Compliance [0.0]
  We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring.<n>We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024.<n>Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations.
  arXiv  Detail & Related papers  (2025-12-06T23:00:19Z)
• Monitoring State Transitions in Markovian Systems with Sampling Cost [65.4151496405543]
  A natural approach is a greedy policy that predicts when the expected prediction loss is below the query cost and queries otherwise.<n>We analyze this policy in a Markovian setting, where the optimal (OPT) strategy is a state-dependent threshold policy.<n>For the case of unknown transition probabilities, we propose a projected gradient descent (PSGD)-based learning variant of the greedy policy.
  arXiv  Detail & Related papers  (2025-10-25T15:07:37Z)
• Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning [41.52380204321823]
  This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning.<n>We evaluate UAMDP in two domains-high-frequency equity trading and retail inventory control-both marked by structural uncertainty and economic volatility.
  arXiv  Detail & Related papers  (2025-10-09T13:46:32Z)
• Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization [63.93275508300137]
  We introduce a novel risk-aware Counterfactual Learning To Rank method with theoretical guarantees for safe deployment. Our experimental results demonstrate the efficacy of our proposed method, which is effective at avoiding initial periods of bad performance when little data is available.
  arXiv  Detail & Related papers  (2023-04-26T15:54:23Z)
• Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity [55.29408396918968]
  We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification. Our contributions include both consistency and robustness by establishing top-$k$ consistency of LDR losses for multi-class classification. We propose a new adaptive LDR loss that automatically adapts the individualized temperature parameter to the noise degree of class label of each instance.
  arXiv  Detail & Related papers  (2021-12-30T00:27:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.