The Incremental Information Content of Tone Change in Management Discussion and Analysis

Abstract

This study explores whether the Management Discussion and Analysis (MD&A) section of Form 10-Q and 10-K has incremental information content beyond financial measures such as earnings surprises, accruals and operating cash flows (OCF). It uses a well-established classification scheme of words into positive and negative categories to measure the tone change in a specific MD&A section as compared to those of the prior four filings. Our results indicate that short window market reactions around the SEC filing are significantly associated with the tone of the MD&A section, even after controlling for accruals, OCF and earnings surprises. We also show that the tone of the MD&A section adds significantly to portfolio drift returns in the window of two days after the SEC filing date through one day after the subsequent quarter's preliminary earnings announcement, beyond financial information conveyed by accruals, OCF and earnings surprises. The incremental information of tone change is larger the weaker is the firm's information environment. The Incremental Information Content of Tone Change in Management Discussion and AnalysisThere is a substantial body of literature in financial economics and accounting that examines the value relevance and information content of quantitative factors in the pricing of stocks. While economic and statistical modeling has become more sophisticated over the years, the somewhat disconcerting conclusion that seems to have emerged is that these quantitative factors inadequately explain movement of stock prices.Persuasive evidence of this is provided by Shiller (1981), Roll (1988), and Cutler et al. (1989), and others in the finance literature, who demonstrate that stock prices do not respond to change in quantitative measures of firm fundamentals as would be expected from models incorporating only quantitative variables of firm performance. In the accounting literature, Thiagarajan (1993), and Lev (1996) earnings and stock returns on the day after the publication of these news articles. They find that the proportion of negative words in these news stories (especially, negative words about a firm's fundamentals) do provide information about future earnings even after controlling for other factors; the higher the proportion of negative words the larger are the negative shocks to future earnings. In addition, they provide persuasive evidence that potential profits could be made by trading on negative words from DJNS, a timely news service (but not from the one day old information published in the WSJ). 2The two Tetlock papers remain among the first of their kind to assess the predictive content of non-quantitative verbal information and are the main motivators of our work. 3 By focusing on news stories in media, their work is more concerned with 1 Following the initial impact on stock prices due to the media pessimism factor, the prices of indexes of smaller stocks reverse more slowly than those of large firms. In addition, he also provides persuasive evidence to show that pessimism i...

Word Power: A New Approach for Content Analysis

“…In many finance and accounting applications, content analysis examines how the market reacts to such qualitative information by quantifying document tone. For example, Tetlock (2007), Tetlock et al (2008), Feldman et al (2010 and Loughran and McDonald (2011) examine how the market reacts to the tone of newspaper articles and statutory filings. Das and Chen (2007) and Antweiler and Frank (2004) employ several alternative classifiers, such as Naïve Bayes and Vector Distance, to extract investor sentiment from posts on Yahoo!…”

“…For example, Tetlock (2007) and Feldman et al (2010) hypothesize a linear relation between returns and proportion of positive and negative words. Li (2006) find similar relations in 10-K filings by focusing on two root words: risk and uncertainty.…”

Generating Domain-Specific Dictionaries Using Bayesian Learning

“…Various studies have shown that the linguistic content of a document is useful in explaining stock market returns. In this context, dictionary-based methods for sentiment analysis are used to explain stock returns, stock volatility and firm earnings by the tone of newspapers (e. g. Tetlock, 2007;Tetlock, Saar-Tsechansky, and Macskassy, 2008), company press releases (Demers and Vega, 2010;Engelberg, 2008;Henry, 2008), regulated ad hoc announcements (Feuerriegel, Ratku, and Neumann, 2015;Groth and Muntermann, 2011; Twenty-Third European Conference on Information Systems, Münster, Germany, 2015 Muntermann and Guettler, 2007) and 10-K reports (Feldman, Govindaraj, Livnat, and Segal, 2008;Hanley and Hoberg, 2008;Li, 2008).…”

CEO-Speeches and Stock Returns

“…Early content analyses of financial texts Davis & TamaSweet, 2012;Feldman et al, 2008;Ferris et al, 2013;Henry & Leone, 2016;Kothari et al, 2009;Larcker & Zakolyukina, 2012;Tetlock, 2007;Tetlock et al, 2008) utilized general English dictionaries such as the Harvard University's General Inquirer IV-4 4 dictionary, the dictionaries included in the Diction 5 software, or the Linguistic Inquiry Word Count 6 software. Henry (2008) is the first to compose a dictionary explicitly designed to examine the tone of financial documents.…”

“…Recent research has acknowledged the value not only of quantitative data disclosures but also of qualitative information, predominantly in the form of the textual sentiment of business communication. Sentiment is typically examined via content analyses which have been applied on several types of business communication such as annual reports (Feldman et al, 2008;Jegadeesh & Wu, 2013;Loughran & McDonald, 2011, 2015, earnings press releases Davis & Tama-Sweet, 2012;Henry, 2008;Henry & Leone, 2016;Huang et al, 2014), IPO prospectuses (Demers & Vega, 2008;Ferris et al, 2013;Jegadeesh & Wu, 2013), CEO letters (Boudt & Thewissen, 2016), and earnings conference calls (Davis et al, 2015;Doran et al, 2012;Larcker & Zakolyukina, 2012;Price et al, 2012). In general, these studies find that qualitative information is indeed processed by investors and helps to predict future accounting returns, stock returns, stock volatility, and stock trading volume.…”