dc.contributor.author |
Goel, Prakhar |
|
dc.contributor.author |
Goyal, Mihir |
|
dc.contributor.author |
Shah, Rajiv Ratn (Advisor) |
|
dc.date.accessioned |
2022-03-31T11:28:33Z |
|
dc.date.available |
2022-03-31T11:28:33Z |
|
dc.date.issued |
2020-12 |
|
dc.identifier.uri |
http://repository.iiitd.edu.in/xmlui/handle/123456789/997 |
|
dc.description.abstract |
Financial risk prediction is an essential task in today’s financial markets, especially with the disclosure of
important information such as Merger and Acquisition (M&A) calls nowadays. M&A calls often provide
key insights into the confidence levels and agreeability between the different high ranking members of
an organization concerning a merger or acquisition in the form of subtle vocal cues and verbal messages.
Variations in vocal features such as pitch can suggest doubt and a lack of confidence that may discredit
the message spoken. Additionally, information about the speaker of the message can assist the system
in making better predictions. To aid the analysis of M&A calls, we curate a dataset of conference call
transcripts and audios from the past 5 years. We propose strong baseline architectures to accompany
the model that takes advantage of the multimodal multi-speaker input to perform financial forecasting.
Empirical results show the improvement that our architecture gives over existing models. |
en_US |
dc.language.iso |
en_US |
en_US |
dc.publisher |
IIIT- Delhi |
en_US |
dc.subject |
Multi-modal models |
en_US |
dc.subject |
Financial Forecasting |
en_US |
dc.subject |
Natural Language Processing |
en_US |
dc.subject |
Audio Analysis |
en_US |
dc.title |
Multimodal multi-speaker merger & acquisition (M3A) financial forecasting: a new task, dataset, and neural baselines |
en_US |
dc.type |
Other |
en_US |