Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines

Abstract

Risk prediction is an essential task in financial markets. Merger and Acquisition (M&A) calls provide key insights into the claims made by company executives about the restructuring of the financial firms. Extracting vocal and textual cues from M&A calls can help model the risk associated with such financial activities. To aid the analysis of M&A calls, we curate a dataset of conference call transcripts and their corresponding audio recordings for the time period ranging from 2016 to 2020. We introduce M3ANet, a baseline architecture that takes advantage of the multimodal multispeaker input to forecast the financial risk associated with the M&A calls. Empirical results prove that the task is challenging, with the proposed architecture performing marginally better than strong BERT-based baselines. We release the M3A dataset and benchmark models to motivate future research on this challenging problem domain.

Multimodal Learning With Transformers: A Survey

“…(3) More scenarios. In addition to common caption and QA datasets, more applications and scenarios have been studied, e.g., CIRR [149] (real-life images), Product1M [137], Bed and Breakfast (BnB) [150] (vision-and-language navigation), M3A [151] (financial dataset), X-World [152] (autonomous drive).…”