DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents
Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha
Abstract
Financial prediction is complex due to the stochastic nature of the stock market. Semi-structured financial documents present comprehensive financial data in tabular formats, such as earnings, profit-loss statements, and balance sheets, and can often contain rich technical analysis along with a textual discussion of corporate history, and management analysis, compliance, and risks. Existing research focuses on the textual and audio modalities of financial disclosures from company conference calls to forecast stock volatility and price movement, but ignores the rich tabular data available in financial reports. Moreover, the economic realm is still plagued with a severe under-representation of various communities spanning diverse demographics, gender, and native speakers. In this work, we show that combining tabular data from financial semi-structured documents with text transcripts and audio recordings not only improves stock volatility and price movement prediction by 5-12% but also reduces gender bias caused due to audio-based neural networks by over 30%.
BibTeX
@inproceedings{mathur-etal-2022-docfin,
title = "{D}oc{F}in: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents",
author = "Mathur, Puneet and
Goyal, Mihir and
Sawhney, Ramit and
Mathur, Ritik and
Leidner, Jochen and
Dernoncourt, Franck and
Manocha, Dinesh",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.139/",
doi = "10.18653/v1/2022.findings-emnlp.139",
pages = "1933--1940"
}