Kemal Kirtac

A new paper demonstrates that a lightweight supervised aggregator can effectively combine outputs from multiple zero-shot LLMs to improve corporate disclosure classification and stock return prediction. Researchers tested three fixed zero-shot classifiers reading financial disclosures from different perspectives, then trained a logistic meta-classifier to aggregate their outputs. Using 9,860 U.S. corporate disclosures from January 2025 to March 2026, the trained aggregator achieved 60.6% balanced accuracy compared to 56.6% for the best single classifier, with the largest gains appearing in mixed-signal cases where classifiers disagreed.