Measuring the Tone of Accounting and Financial Narrative

Measuring the Tone of Accounting and Financial Narrative

Elaine Henry (Fordham University, USA) and Andrew J. Leone (University of Miami, USA)
Copyright: © 2014 |Pages: 12
DOI: 10.4018/978-1-4666-4999-6.ch003
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Abstract

Research in accounting and finance has measured the tone of financial narrative using word frequency counts based mainly on four different wordlists: 1) a wordlist developed in Henry’s (2006, 2008) analysis of earnings announcements (Henry Wordlist); 2) a wordlist developed in Loughran and McDonald’s (2011) analysis of 10-K filings (LM Wordlist); 3) a wordlist from DICTION (DICTION Wordlist) software developed by Roderick Hart; and 4) a wordlist from the General Inquirer program (GI Wordlist) developed by social psychologist Philip Stone. This chapter examines alternative measures of the tone of narrative in earnings press releases based on these word lists, explores the statistical relations among the alternative measures, and tests whether those relations vary depending on aspects of the earnings news being announced and other factors.
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Financial Disclosure And Tone

Research employing measures of the tone of accounting and financial narrative has used word frequency counts based mainly on four different wordlists: 1) a wordlist developed in Henry’s (2006, 2008)1 analysis of earnings announcements (Henry wordlist); 2) a wordlist developed in Loughran and McDonald’s (2011) analysis of 10-K filings (LM wordlist); 3) a wordlist from DICTION (DICTION wordlist) software developed by communications expert Roderick Hart; and 4) a wordlist from the General Inquirer program (GI wordlist) developed by social psychologist Philip Stone.

Of these four wordlists used in prior research, two were developed within the domain of financial disclosure (the Henry and LM lists), and two were developed for broader contexts (the DICTION and GI wordlist). As with most domains of discourse, words used in financial disclosure can have domain-specific meanings, which give rise to measurement error when general wordlists are applied to such contexts. For example, the word “Asset” refers to items on a company’s balance sheet that are resources deemed to have some future economic benefit. The word is not expressive of a company’s sentiment but nonetheless is contained in the GI list of positive words. Similarly, the word “Division” is virtually always used in reference to a company’s subsidiary and not, for example, sentiment about relationships or economic events within the firm.

To illustrate the problem of non-domain-specific word lists, in an earnings press release issued by Pier 1 Imports on December 13, 2012 (Figure 1 presents a brief excerpt2), the following words are among those identified by the GI wordlist as being positive: “shares”, “share”, “profit”, “paid”, “outstanding”, “interest”, “consistent”, “company”, “common”, “board”, “allow”, “adjustment”, “actual”, “accrued”, “accordance”, “accepted”. In the context of financial disclosure and particularly earnings announcements, all of these words are descriptive of financial data or entities but do not convey either a positive or negative tone. In contrast, applying the Henry “Positive” wordlist to the same Pier 1 Imports press release, generates a set of words that are much more reflective of positive sentiment conveyed by the company. These words are: “up”, “strong”, “strength”, “record”, “pleased”, “opportunity”, “most”, “more”, “larger”, “increases”, “increased”, “increase”, “improvements”, “improvement”, “higher”, “growth”, “expanded”, “achieve”, and “above”.

Figure 1.

Excerpt from earnings press release issued by Pier 1 Imports on December 13, 2012

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