Earnings management and annual report readability

By Kin Lo, Felipe Ramos, Rafael Rogo. 2017

637 citations recorded by the source corpus.

We explore how the readability of annual reports varies with earnings management. Using the Fog Index to measure readability (Li, 2008), and focusing on the management discussion and analysis section of the annual report (MD&A), we predict and find that firms most likely to have managed earnings to beat the prior year's earnings have MD&As that are more complex. This disruption of the overall pattern of readability increasing with the level of earnings found in Li (2008) challenges the ontological explanation that good news is inherently easier to communicate, and shows that obfuscation contributes to making disclosures more complex.

Subfields: Accounting, Finance, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/dd9d9284a15430aff9607593c3b536f05daad77c

How researchers see this paper

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In Accounting

1. Chansog Kim
2. Ke Wang
3. E. D. Toit
4. Liandong Zhang
5. Camila C. Pereira
6. Jeong‐Bon Kim
7. Ahsan Habib
8. Mostafa Monzur Hasan

In Finance

1. Ke Wang
2. Chansog Kim
3. E. D. Toit
4. Liandong Zhang
5. Camila C. Pereira
6. Jeong‐Bon Kim
7. Ahsan Habib
8. Mostafa Monzur Hasan

In Management Information Systems

1. Ke Wang
2. E. D. Toit
3. Chansog Kim
4. Liandong Zhang
5. Camila C. Pereira

Citations in the corpus

• Readability of 10‐K Reports and Stock Price Crash Risk (2019, positive): “K reports to mask earnings manipulation, which makes it difficult for investors to detect earnings manipulation (e.g., Lo et al. 2017).”
• The readability of integrated reports (2017, background): “K documents from 1994 to 2012 with some data restrictions Plain English attributes, e.g. passive voice, long sentences, etc. Bog Index Very difficult Lo et al. (2017) US Reports available on the SEC EDGAR filings database from 2000 to 2012, with some restrictions Fog Index Very difficult Melloni et…”
• Textual analysis in finance (2019, methodology): “We also estimate an index based on word count with a term weighting scheme as in Loughran and McDonald (2011) using the uncertainty list from Loughran-McDonald master dictionary and find that it performs quite similar to our measure in predicting returns.”