The Determinants and Information Content of the Forward-looking Statements in Corporate Filings - A Naive Bayesian Machine Learning Approach

Abstract

This paper examines the tone and content of the forward-looking statements (FLS) in corporate 10-K and 10-Q filings using a Naive Bayesian machine learning algorithm. I first manually categorize 30,000 sentences of randomly selected FLS extracted from the MD&As along two dimensions: (1) tone (i.e., positive versus negative tone); and (2) content (i.e., profitability, operations, and liquidity etc.). These manually coded sentences are then used as training data in a Naive Bayesian machine learning algorithm to classify the tone and content of about 13 million forward-looking statements from more than 140,000 corporate 10-K and 10-Q MD&As between 1994 and 2007.

I find that firms with better current performance, lower accruals, smaller size, lower market-to-book ratio, and less return volatility tend to have more positive forward-looking statements in MD&As. The average tone of the forward-looking statements in a firm's MD&A is positively associated with future earnings and liquidity, even after controlling for other determinants of future performance and there is no systematic change in the information content of MD&As over time. Finally, the evidence indicates that financial analysts do not fully understand the information content of the MD&As in making their forecasts.

Keywords: MD&A, Information content, Machine learning

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