MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction

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Title: MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction
Authors: Li, JiazhengYang, LinyiSmyth, BarryDong, Ruihai
Permanent link: http://hdl.handle.net/10197/12221
Date: 23-Oct-2020
Online since: 2021-05-27T12:04:15Z
Abstract: In the area of natural language processing, various financial datasets have informed recent research and analysis including financial news, financial reports, social media, and audio data from earnings calls. We introduce a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. We describe the main features of MAEC, how it was collected and assembled, paying particular attention to the text-audio alignment process used. We present the approach used in this work as providing a suitable framework for processing similar forms of data in the future. The resulting dataset is more than six times larger than those currently available to the research community and we discuss its potential in terms of current and future research challenges and opportunities. All resources of this work are available at https://github.com/Earnings-Call-Dataset/
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2020 ACM
Keywords: Personal sensingMultimodal aligned datasetsEarnings conference callsFinancial risk prediction
DOI: 10.1145/3340531.3412879
Other versions: https://www.cikm2020.org/
Language: en
Status of Item: Peer reviewed
Is part of: CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
Conference Details: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, (CIKM '20), 19-23 October 2020
ISBN: 978-1-4503-6859-9
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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