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In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse

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In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse. / El Haj, Mahmoud; Rayson, Paul Edward; Walker, Martin et al.
In: Journal of Business Finance and Accounting, Vol. 46, No. 3-4, 30.04.2019, p. 265-306.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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El Haj M, Rayson PE, Walker M, Young SE, Simaki V. In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse. Journal of Business Finance and Accounting. 2019 Apr 30;46(3-4):265-306. Epub 2019 Apr 4. doi: 10.1111/jbfa.12378

Author

El Haj, Mahmoud ; Rayson, Paul Edward ; Walker, Martin et al. / In Search of Meaning : Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse. In: Journal of Business Finance and Accounting. 2019 ; Vol. 46, No. 3-4. pp. 265-306.

Bibtex

@article{5fc2f8bc3f714e19907abcd3344ebbd5,
title = "In Search of Meaning: Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse",
abstract = "We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four approaches are named entity recognition, summarization, semantics and corpus linguistics.",
keywords = "10‐K, annual reports, computational linguistics, conference calls, corpus linguistics, earnings announcements, machine learning, NLP, semantics",
author = "{El Haj}, Mahmoud and Rayson, {Paul Edward} and Martin Walker and Young, {Steven Eric} and Vasiliki Simaki",
year = "2019",
month = apr,
day = "30",
doi = "10.1111/jbfa.12378",
language = "English",
volume = "46",
pages = "265--306",
journal = "Journal of Business Finance and Accounting",
issn = "0306-686X",
publisher = "Wiley-Blackwell",
number = "3-4",

}

RIS

TY - JOUR

T1 - In Search of Meaning

T2 - Lessons, Resources and Next Steps for Computational Analysis of Financial Discourse

AU - El Haj, Mahmoud

AU - Rayson, Paul Edward

AU - Walker, Martin

AU - Young, Steven Eric

AU - Simaki, Vasiliki

PY - 2019/4/30

Y1 - 2019/4/30

N2 - We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four approaches are named entity recognition, summarization, semantics and corpus linguistics.

AB - We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four approaches are named entity recognition, summarization, semantics and corpus linguistics.

KW - 10‐K

KW - annual reports

KW - computational linguistics

KW - conference calls

KW - corpus linguistics

KW - earnings announcements

KW - machine learning

KW - NLP

KW - semantics

U2 - 10.1111/jbfa.12378

DO - 10.1111/jbfa.12378

M3 - Journal article

VL - 46

SP - 265

EP - 306

JO - Journal of Business Finance and Accounting

JF - Journal of Business Finance and Accounting

SN - 0306-686X

IS - 3-4

ER -