arXiv Machine Learning
What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk
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arXiv Machine Learning
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Temporal Leakage in Financial News NLP: A Multi-Architecture Audit with a Regime-Specific M&A Signal
The paper audits the impact of temporal leakage on financial-news direction prediction across 49,799 articles and 16 feature-model combinations, including TF‑IDF, MiniLM, FinBERT, and fine‑tuned RoBERTa‑large / DeBERTa‑v3‑large, as well as zero/few‑shot and LoRA probes of Llama‑3 and Qwen2.5. Random train‑test splits inflate MCC scores by 1.1× to 6.5×, with larger models and richer features showing greater gains, while end‑to‑end FinBERT fine‑tuning actually increases the gap. Only the mergers and acquisitions (M&A) category shows a positive locked‑test signal under near‑temporal chronological evaluation, with the signal localized to 2024‑2025 European‑tilted M&A semantics and not transferring to a 2009‑2020 U.S. corpus.
arXiv Computation and Language
Sep 30
Can Language Models Learn to Forecast Stock Prices
arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...
arXiv AI
Jul 21
FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches
arXiv:2607. 17765v1 Announce Type: cross Abstract: We introduce WC2026-Agents, a benchmark and dataset for evaluating large language models (LLMs) as autonomous forecasting agents on real, future events.
arXiv AI
Jul 7
Shortcut Learning in Legal Judgment Prediction: Empirical Evidence from the UK Employment Tribunal
arXiv:2607. 04261v1 Announce Type: new Abstract: Current Legal Judgment Prediction (LJP) is constrained by its reliance on post-hoc judicial materials, increasing the likelihood that models perform retrospective classification rather than true forecasting.
arXiv Machine Learning
Oct 16
When Does "Post-Call" Begin? An Availability Contract for Earnings-Call NLP Benchmarks
The paper introduces an Availability Contract to address timing mismatches in financial NLP benchmarks, requiring explicit declaration of input boundaries, evidence of availability, outcome start, and supported claims. Applying this framework to 552 earnings calls, the authors show that shifting from a call‑start proxy to a completion proxy changes return signs in 9.4% of calls and alters quintile rankings in 28.3%. In a volatility benchmark, moving target windows forward by one session increases mean squared error by 18.1‑21.4% while leaving point rankings unchanged, illustrating that labels and losses can be sensitive to the declared outcome clock.
arXiv Machine Learning
Aug 13
TradingMoE: Routing the Right Experts in Evolving Markets
arXiv:2608. 11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions.