short-paper

Distilling Numeral Information for Volatility Forecasting

Authors:

Chung-Chi Chen,

Hen-Hsen Huang,

Hsin-Hsi ChenAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 2920 - 2924

https://doi.org/10.1145/3459637.3482089

Published: 30 October 2021 Publication History

Abstract

The volatility of stock price reflects the risk of stock and influences the risk of investor's portfolio. It is also a crucial part of pricing derivative securities. Researchers have paid their attention to predict the stock volatility with different kinds of textual data. However, most of them focus on using word information only. Few touch on capturing the numeral information in textual data, providing fine-grained clues for financial document understanding. In this paper, we present a novel dataset, ECNum, for understanding the numerals in the transcript of earnings conference calls. We propose a simple but efficient method, Numeral-Aware Model (NAM), for enhancing the capacity of numeral understanding of neural network models. We employ the distilled information in the stock volatility forecasting task and achieve the best performance compared to the previous works in short-term scenarios.

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The volatility of stock price reflects the risk of stock and influences the risk of investor's portfolio. It is also a crucial part of pricing derivative securities. Researchers have paid their attention to predict the stock volatility with different kinds of textual data. However, most of them focus on using word information only. Few touch on capturing the numeral information in textual data, providing fine-grained clues for financial document understanding. In this paper, we present a novel dataset, ECNum, for understanding the numerals in the transcript of earnings conference calls. We propose a simple but efficient method, Numeral-Aware Model (NAM), for enhancing the capacity of numeral understanding of neural network models. We employ the distilled information in the stock volatility forecasting task and achieve the best performance compared to the previous works in short-term scenarios.

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References

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Cited By

Qin CYu CMeng YChang J(2023)A Numeral and Affective Knowledge Enhanced Network for Aspect-based Financial Sentiment Analysis2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI59109.2023.00139(926-933)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICTAI59109.2023.00139

Index Terms

Distilling Numeral Information for Volatility Forecasting
1. Applied computing
  1. Operations research
    1. Forecasting

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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Ministry of Science and Technology, Taiwan

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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Overall Acceptance Rate 520 of 2,712 submissions, 19%

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Cited By

Qin CYu CMeng YChang J(2023)A Numeral and Affective Knowledge Enhanced Network for Aspect-based Financial Sentiment Analysis2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI59109.2023.00139(926-933)Online publication date: 6-Nov-2023
https://doi.org/10.1109/ICTAI59109.2023.00139

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