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.

Large vision-language models (LVLMs) have made significant progress in chart understanding.However, financial charts, characterized by complex temporal structures and domainspecific terminology, remain notably underexplored.We introduce FinChart-Bench, the first benchmark specifically focused on realworld financial charts.FinChart-Bench comprises 1,200 financial chart images collected from 2015 to 2024, each annotated with True/-False (TF), Multiple Choice (MC), and Question Answering (QA) questions, totaling 7,016 questions.We conducted a comprehensive evaluation of 26 state-of-the-art LVLMs on FinChart-Bench.Our evaluation reveals critical insights: (1) the performance gap between open-source and closed-source models is narrowing, (2) performance degradation occurs in upgraded models within families, (3) many models struggle with instruction following, (4) both advanced models show significant limitations in spatial reasoning abilities, and (5) current LVLMs are not reliable enough to serve as automated evaluators.These findings highlight important limitations in current LVLM capabilities for financial chart understanding.The FinChart-Bench dataset is available at https: //github.com/Tizzzzy/FinChart-Bench.