Multimodal Multi-Speaker Merger & Acquisition Financial Modeling: A New Task, Dataset, and Neural Baselines (2021)

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 multi-speaker input to forecast the financial risk associated with the M&A calls. Empirical results prove that the task is challenging, with the pro-posed 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.

## introduction mergers and acquisitions (m&as) 1 conference calls are events preceding financial transactions involving two or more entities such that either one of the participant companies takes over the other(s) and establishes itself as the owner (termed as "acquisition") or when one company combines with another to become a joint entity (termed as "merger"). in these m&a conference calls, the participating companies' management makes a presentation to the call participants, such as market analysts, media personnel, and other stakeholders, explaining the rationale for the deal and possible roadblocks to deal completion @xcite . following the presentation segment, there is a q&a segment in which the call participants ask questions to which the management responds. building on the important information that m&as provide, academic research, the financial press, and other media give a great deal of attention. one of these discussions' principal aspects lies in how the deals may affect the company's valuation @xcite and future growth. a significant focus in financial and economic literature has been on understanding whether m&as create or destroy value. consequently, shareholders critically analyze the deals to estimate the potential stock price and stock price volatility post the m&a conference call. identifying the gap in natural language processing (nlp) literature on the lack of resources to study m&a conference calls with their text transcripts and audio recordings, we take the first step in multimodal financial modeling in the m&a space. such data can allow academicians to study m&a calls further, especially with the rich multimodal data. it shall enable studies that focus not only on the words spoken in the call but also in the manner they were spoken, a relatively unexplored field in financial forecasting, as shown in figure 1 . a salient aspect of conference calls is that, unlike text reports, the company's management interacts with external stakeholders and asks questions. this figure 2 : m&a calls have a q&a session where financial stakeholders can ask questions to the company executives. in such sessions, company executives have to be impromptu with their responses, allowing informal words to seep in. this example q&a session is from the call regarding the acquisition of 21st century fox by disney, dated june 20, 2018. in the example, an analyst poses a few questions to the company executives (depicted in yellow). the ceo of disney responds to these questions, where we notice some cases of informal speech (depicted in purple). the executive's response however mainly focused on specific objects or entities (depicted in red) intermixed with some time-based information (depicted in green). vocal cues play a critical role in verbal communication as they can provide support or discredit the verbal message that is being spoken @xcite . for example, consider if the ceo of the acquiring company exhibits confidence in the statement -"we are confident that this acquisition will bring us profits," however, displays nervousness while justifying technical details of the deal, we may infer contradiction in the claims of a successful m&a. vocal cues have been proven indicators of emotions like deceit and nervousness @xcite . past research @xcite shows that the addition of vocal cues has helped with the task of financial predictions and enrich the learned representations. our contributions can be summarized as: the source code, processed features, and details on acquiring raw data are available at https://github.com/ midas-research/m3a-acl nancial forecasting dataset) that consists of 816 m&a conference calls spanning over 545 hours between 2016 to 2020 with their transcripts and audio recordings, segmented by utterances and aligned with the audio. studies have also been carried out specifically for m&a calls, showing their effect on the market @xcite . however, there exists a gap in leveraging neural predictive modeling on using verbal and vocal cues pertaining to m&a calls for financial forecasting. financial forecasting research has shown historical pricing data to be useful in predicting financial risk modeling @xcite @xcite . it also considers volatility as an indicator of uncertainty, which helps make decisions regarding investments @xcite @xcite . previous work often use numerical features @xcite in approaches like neural networks @xcite , graph neural networks @xcite , and time-series models @xcite . on the other hand, we are interested in analyzing multimodal data like text and audio, which can hold completely different information for predictive models. ## problem formulation consider an m&a call χ ∈ {χ 1 , χ 2 , . measuring stock volatility following @xcite , we formulate stock volatility as a regression problem. for a given stock with a close price of p k on the trading day k, we calculate the average log volatility as the natural log of the standard deviation of return prices r in a window of τ days as: given an acquisition conference call χ, our learning objective is to predict the average negative log volatility v [0,τ ] and price movement y [d-τ,d] using the conference call data χ = [t; a]. ## conclusion we present a dataset of m&a calls that can be utilized to predict financial risk following m&a calls. we also present a strong baseline model using multimodal multi-speaker inputs from the m&a calls to perform financial forecasting. m3anet uses attention-based fusion to leverage the interdependency between the verbal message and the vocal cues. further, the approach uses speaker information to enrich the input data to determine if the speakers' vocal cues or verbal messages conflict with others and accounts for the same. experiments on m3a display the effectiveness of m3anet. we hope our m3a can enable more academic progress in the field of financial forecasting.