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Predicting risk from financial reports with regression

Authors:

Bryan R. Routledge,

Noah A. SmithAuthors Info & Claims

NAACL '09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Pages 272 - 280

Published: 31 May 2009 Publication History

Abstract

We address a text regression problem: given a piece of text, predict a real-world continuous quantity associated with the text's meaning. In this work, the text is an SEC-mandated financial report published annually by a publicly-traded company, and the quantity to be predicted is volatility of stock returns, an empirical measure of financial risk. We apply well-known regression techniques to a large corpus of freely available financial reports, constructing regression models of volatility for the period following a report. Our models rival past volatility (a strong baseline) in predicting the target variable, and a single model that uses both can significantly outperform past volatility. Interestingly, our approach is more accurate for reports after the passage of the Sarbanes-Oxley Act of 2002, giving some evidence for the success of that legislation in making financial reports more informative.

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Cao YChen ZPei QLee NSubbalakshmi KNdiaye P(2024)ECC Analyzer: Extracting Trading Signal from Earnings Conference Calls using Large Language Model for Stock Volatility PredictionProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698689(257-265)Online publication date: 14-Nov-2024
https://dl.acm.org/doi/10.1145/3677052.3698689
Mathur PNeerkaje AChhibber MSawhney RGuo FDernoncourt FDutta SManocha DMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)MONOPOLYProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548380(2276-2285)Online publication date: 10-Oct-2022
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Agrawal YAnand VGupta MArunachalam SVarma VDemartini GZuccon GCulpepper JHuang ZTong H(2021)Goal-Directed Extractive Summarization of Financial ReportsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482113(2817-2821)Online publication date: 26-Oct-2021
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Predicting risk from financial reports with regression

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Published In

cover image DL Hosted proceedings

NAACL '09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

May 2009

716 pages

ISBN:9781932432411

General Chair:
Mari Ostendorf
University of Washington
,
Program Chairs:
Michael Collins
Massachusetts Institute of Technology
,
Shri Narayanan
University of Southern California
,
Douglas W. Oard
Microsoft Research

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Association for Computational Linguistics

United States

Publication History

Published: 31 May 2009

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Overall Acceptance Rate 21 of 29 submissions, 72%

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Cao YChen ZPei QLee NSubbalakshmi KNdiaye P(2024)ECC Analyzer: Extracting Trading Signal from Earnings Conference Calls using Large Language Model for Stock Volatility PredictionProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698689(257-265)Online publication date: 14-Nov-2024
https://dl.acm.org/doi/10.1145/3677052.3698689
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