TY - CHAP
T1 - MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction
AU - Li, Jiazheng
AU - Yang, Linyi
AU - Smyth, Barry
AU - Dong, Ruihai
PY - 2020
Y1 - 2020
N2 - 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/
AB - 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/
KW - earnings conference calls
KW - multimodal aligned datasets
KW - financial risk prediction
U2 - 10.1145/3340531.3412879
DO - 10.1145/3340531.3412879
M3 - Conference paper
SN - 9781450368599
T3 - CIKM '20
SP - 3063
EP - 3070
BT - Proceedings of the 29th ACM International Conference on Information & Knowledge Management
PB - Association for Computing Machinery
CY - New York, NY, USA
ER -