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Cited by 6,015Auditing, Earnings Management, GovernanceFinancial Markets and Investment StrategiesComputational and Text Analysis Methods

When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks

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LOUGHRAN, TIM, MCDONALD, BILL·2011·The Journal of Finance
DOI 10.1111/j.1540-6261.2010.01625.x
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Abstract

Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10‐Ks during 1994 to 2008, almost three‐fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10‐K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.

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  • Using the Loughran and McDonald (2011) word lists, the bad news ratio has a statistically significant negative contemporaneous (h=0) relation with S&P 500 excess returns (slope -0.77%, Hodrick t = -2.68, R2 = 2.90%).

    “| ℎ = 0 | - 0.77 ∗∗∗ | - 2.68 | 2.90 | 0.24 | 1.05 | 0.27 |”

    From ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? (2025) · cites this paper as Loughran and McDonald (2011)

  • Many words that have a negative connotation in other contexts may have a positive connotation in corporate earning reports.

    “They point out that many words that have a negative connotation in other contexts, like tax, cost, crude (oil), or cancer, may have a positive connotation in earning reports.”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Loughran and McDonald (2011)

  • Words not identified as negative in off-the-shelf dictionaries may have quite negative connotation in earning reports.

    “And words that are not identified as negative in off-the-shelf dictionaries may have quite negative connotation in earning reports (e.g., unanticipated).”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Loughran and McDonald (2011)

  • When dictionaries are created in one substantive area and then applied to another, serious errors can occur.

    “But when dictionaries are created in one substantive area and then applied to another, serious errors can occur.”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Loughran and McDonald (2011)

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Authors
LOUGHRAN, T., & MCDONALD, B.
Affiliations
University of Notre Dame (US)
Venue
The Journal of Finance
Publisher
Wiley
Published
2011-01-06
Volume
66
Issue
1
Pages
35-65
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
DOI
10.1111/j.1540-6261.2010.01625.x
OpenAlex
W2152136804
Cited by
6,015
References
3
Citations by year
2026: 947 · 2025: 912 · 2024: 684 · 2023: 584 · 2022: 465 · 2021: 525 · 2020: 382 · 2019: 304 · 2018: 299 · 2017: 218 · 2016: 189 · 2015: 164 · 2014: 137 · 2013: 93 · 2012: 64
FWCI
371.02
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • The Journal of Finance (publishedVersion)
  • SSRN Electronic Journal (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Accounting › Auditing, Earnings Management, Governance
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Keywords
sentiment analysis, financial text, 10-K filings, word lists, fraud, material weaknesses, earnings surprises, trading volume, return volatility

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