@inproceedings{koval-etal-2024-financial,
    abstract = {There is a variety of multimodal data pertinent to public companies, spanning from accounting statements, macroeconomic statistics, earnings conference calls, and financial reports. These diverse modalities capture the state of firms from a variety of different perspectives but requires complex interactions to reconcile in the formation of accurate financial predictions. The commonality between these different modalities is that they all represent a time series, typically observed for a particular firm at a quarterly horizon, providing the ability to model trends and variations of company data over time. However, the time series of these diverse modalities contains varying temporal and cross-channel patterns that are challenging to model without the appropriate inductive biases. In this work, we design a novel multimodal time series prediction task that includes numerical financial results, macroeconomic states, and long financial documents to predict next quarter's company earnings relative to analyst expectations. We explore a variety of approaches for this novel setting, establish strong unimodal baselines, and propose a multimodal model that exhibits state-of-the-art performance on this unique task. We demonstrate that each modality contains unique information and that the best performing model requires careful fusion of the different modalities in a multi-stage training approach. To better understand model behavior, we conduct a variety of probing experiments, reveal insights into the value of different modalities, and demonstrate the practical utility of our proposed method in a simulated trading setting.},
    address = {Miami, Florida, USA},
    author = {Koval, Ross and Andrews, Nicholas and Yan, Xifeng},
    booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
    doi = {10.18653/v1/2024.findings-emnlp.486},
    editor = {{'name': 'Al-Onaizan, Yaser', 'ID': 'Al-OnaizanYaser'} and {'name': 'Bansal, Mohit', 'ID': 'BansalMohit'} and {'name': 'Chen, Yun-Nung', 'ID': 'ChenYun-Nung'}},
    link = {[{'url': 'https://doi.org/10.18653/v1/2024.findings-emnlp.486', 'anchor': 'doi'}]},
    month = {November},
    pages = {8289--8300},
    publisher = {Association for Computational Linguistics},
    title = {Financial Forecasting from Textual and Tabular Time Series},
    url = {https://aclanthology.org/2024.findings-emnlp.486/},
    year = {2024}
}