[2603.20965] Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification

Quantitative Finance > Trading and Market Microstructure arXiv:2603.20965 (q-fin) [Submitted on 21 Mar 2026] Title:Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification Authors:Kemal Kirtac View a PDF of the paper titled Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification, by Kemal Kirtac View PDF HTML (experimental) Abstract:This paper studies whether a lightweight trained aggregator can combine diverse zero-shot large language model judgments into a stronger downstream signal for corporate disclosure classification. Zero-shot LLMs can read disclosures without task-specific fine-tuning, but their predictions often vary across prompts, reasoning styles, and model families. I address this problem with a multi-agent framework in which three zero-shot agents independently read each disclosure and output a sentiment label, a confidence score, and a short rationale. A logistic meta-classifier then aggregates these signals to predict next-day stock return direction. I use a sample of 18,420 U.S. corporate disclosures issued by Nasdaq and S&P 500 firms between 2018 and 2024, matched to next-day stock returns. Results show that the trained aggregator outperforms all single agents, majority vote, confidence-weighted voting, and a FinBERT baseline. Balanced accuracy rises from 0.561 for the best single agent to 0.612 for the trained aggregator, with the largest gains in disclosures combining strong current performance with ...