Year: 2026
Type: conference-paper
Abstract: Predicting corporate earnings surprises is a profitable yet challenging task, as accurate forecasts can inform significant investment decisions.However, progress in this domain has been constrained by a reliance on expensive, proprietary, and text-only data, limiting the development of advanced models.To address this gap, we introduce FinCall-Surprise (Financial Conference Call for Earning Surprise Prediction), the first large-scale, open-source, and multi-modal dataset for earnings surprise prediction.Comprising 2,688 unique corporate conference calls from 2019 to 2021, our dataset features word-to-word conference call textual transcripts, full audio recordings, and corresponding presentation slides.We establish a comprehensive benchmark by evaluating 26 state-of-the-art unimodal and multimodal LLMs.Our findings reveal that (1) while many models achieve high accuracy, this performance is often an illusion caused by significant class imbalance in the realworld data.(2) Some specialized financial models demonstrate unexpected weaknesses in instruction-following and language generation.(3) Although incorporating audio and visual modalities provides some performance gains, current models still struggle to leverage these signals effectively.These results highlight critical limitations in the financial reasoning capabilities of existing LLMs and establish a challenging new baseline for future research.The FinCall-Surprise dataset is available at https://github.com/Tizzzzy/ FinCall-Surprise.
Authors: Dong Shu, Yanguang Liu, Huopu Zhang, Mengnan Du
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4414971017