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 MDA 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.