Elaine Henry

Bio: Elaine Henry is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Earnings & International Financial Reporting Standards. The author has an hindex of 22, co-authored 65 publications receiving 2469 citations. Previous affiliations of Elaine Henry include University of Miami & Fordham University.

Are Investors Influenced by How Earnings Press Releases are Written

Abstract: Earnings press releases are an important means by which many firms communicate to investors. This study examines whether investors are influenced by how earnings press releases are written - the tone and other stylistic attributes - using actual earnings press releases and archival capital markets data in a standard short-window event study. To measure the tone and other stylistic aspects of press releases, I use elementary computer-based content analysis. Tone is measured using a frequency count of positive or negative words. Other stylistic aspects are the overall length of the press release, the overall percentage of numbers versus words, and the complexity of the words used. Results suggest that tone of earnings press releases influences investors' reactions to earnings. An explanation for this result is provided by prospect theory (Tversky and Kahneman, 1981, 1986), which predicts that framing financial performance in positive terms, will cause investors to think about the results in terms of increases relative to reference points. Limited evidence is presented that other stylistic attributes of earnings press releases affect investors' reactions to earnings.

Are Investors Influenced By How Earnings Press Releases Are Written

Abstract: This two-part study begins with a rhetorical analysis of the genre of earnings press releases. Then, a quantitative analysis uses capital markets data to assess the investor impact of tone and othe...

Measuring Qualitative Information in Capital Markets Research: Comparison of Alternative Methodologies to Measure Disclosure Tone

Abstract: This study evaluates alternative measures of the tone of financial narrative. We present evidence that word-frequency tone measures based on domain-specific wordlists—compared to general wordlists—better predict the market reaction to earnings announcements, have greater statistical power in short-window event studies, and exhibit more economically consistent post-announcement drift. Further, inverse document frequency weighting, advocated in Loughran and McDonald (2011), provides little improvement to the alternative approach of equal weighting. We also provide evidence that word-frequency tone measures are as powerful as the Naive Bayesian machine-learning tone measure from Li (2010) in a regression of future earnings on MD&A tone. Overall, although more complex techniques are potentially advantageous in certain contexts, equal-weighted, domain-specific, word-frequency tone measures are generally just as powerful in the context of financial disclosure and capital markets. Such measures are als...

Related party transactions and corporate governance

Abstract: Transactions between a firm and its own managers, directors, principal owners or affiliates are known as related party transactions. Such transactions, which are diverse and often complex, represent a corporate governance challenge. This paper initiates research in finance on related party transactions, which have implications for agency literature. We first explore two alternative perspectives of related party transactions: the view that such transactions are conflicts of interest which compromise management’s agency responsibility to shareholders as well as directors’ monitoring functions; and the view that such transactions are efficient transactions that fulfill rational economic demands of a firm such as the need for service providers with in-depth firm-specific knowledge. We describe related party transactions for a sample of 112 publicly-traded companies, including the types of transactions and parties involved. This paper provides a starting point in related party transactions research.

Market Reaction to Verbal Components of Earnings Press Releases: Event Study Using a Predictive Algorithm

Abstract: Similar to a classic event study, this study examines market reaction to firms' earnings announcements. This study extends the examination to include a broad range of concurrent disclosure contained in earnings press releases: financial disclosure captured as accounting ratios; and verbal components of disclosure, both content and style, which are captured using elementary computer-based content analysis. Extending the analysis to such a broad range of concurrent disclosures requires a methodology designed to utilize a large number of predictor variables, and predictive data mining algorithms are specifically designed to do so. Therefore, this study employs a widely used data-mining algorithm - classification and regression trees (CART). Results of the study show that inclusion of predictor variables capturing verbal content and writing style of earnings-press releases results in more accurate predictions of market response.

When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks

Abstract: Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10 Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10 K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.

When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks

Abstract: Previous research uses negative word counts to measure the tone of a text. We show that word lists developed for other disciplines misclassify common words in financial text. In a large sample of 10-Ks during 1994 to 2008, almost three-fourths of the words identified as negative by the widely used Harvard Dictionary are words typically not considered negative in financial contexts. We develop an alternative negative word list, along with five other word lists, that better reflect tone in financial text. We link the word lists to 10-K filing returns, trading volume, return volatility, fraud, material weakness, and unexpected earnings.

The Information Content of Forward-Looking Statements in Corporate Filings-A Naïve Bayesian Machine Learning Approach: the information content of corporate filings

Abstract: This paper examines the information content of the forward-looking statements (FLS) 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 FLSs. The average tone of the FLS 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.

Textual Analysis in Accounting and Finance: A Survey

Abstract: Relative to quantitative methods traditionally used in accounting and finance, textual analysis is substantially less precise. Thus, understanding the art is of equal importance to understanding the science. In this survey, we describe the nuances of the method and, as users of textual analysis, some of the tripwires in implementation. We also review the contemporary textual analysis literature and highlight areas of future research.

Economic Consequences of the Sarbanes-Oxley Act of 2002

Abstract: This paper investigates the economic consequences of the Sarbanes-Oxley Act (SOX) by examining market reactions to related legislative events. Using concurrent stock returns of non-U.S.-traded foreign firms to estimate normal U.S. returns, I find that U.S. firms experienced a statistically significant negative cumulative abnormal return around key SOX events. I then examine the cross-sectional variation of U.S. firms' returns around these events. Regression results are consistent with the nonaudit services and governance provisions imposing net costs. Additional tests show that deferring the compliance of Section 404, which mandates an internal control test, resulted in significant cost savings for nonaccelerated filers.