The Information Content of Forward-Looking Statements in Corporate Filings – A Naive Bayesian Machine Learning Approach

Journal of Accounting Research, Forthcoming

Posted: 13 Jun 2010

See all articles by Feng Li

Feng Li

Shanghai Advanced Institute of Finance, Shanghai Jiaotong University

Date Written: June 11, 2010

Abstract

This paper examines the information content of the forward-looking statements in the Management Discussion and Analysis section (MD&A) of 10-K and 10-Q filings using a Naive Bayesian machine learning algorithm. I find that firms with better current performance, lower accruals, smaller size, lower market-to-book ratio, less return volatility, lower MD&A Fog index, and longer history tend to have more positive forward-looking statements. The average tone of the forward-looking statements is positively associated with future earnings even after controlling for other determinants of future performance. The results also show that, despite increased regulations aimed at strengthening MD&A disclosures, there is no systematic change in the information content of MD&As over time. In addition, the tone in MD&As seems to mitigate the mispricing of accruals. When managers “warn” about the future performance implications of accruals (i.e., the MD&A tone is positive (negative) when accruals are negative (positive)), accruals are not associated with future returns. The tone measures based on three commonly used dictionaries (Diction, General Inquirer, and the Linguistic Inquiry and Word Count) do not positively predict future performance. This result suggests that these dictionaries might not work well for analyzing corporate filings.

Keywords: MD&A, Information content, Machine learning, Dictionary approach, Accrual anomaly

JEL Classification: G12, M41, M45, G29, G38

Suggested Citation

Li, Feng, The Information Content of Forward-Looking Statements in Corporate Filings – A Naive Bayesian Machine Learning Approach (June 11, 2010). Journal of Accounting Research, Forthcoming. Available at SSRN: https://ssrn.com/abstract=1623943

Feng Li (Contact Author)

Shanghai Advanced Institute of Finance, Shanghai Jiaotong University ( email )

211 West Huaihai Road
Shanghai, Shanghai 200030
China

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
2,037
PlumX Metrics