A review on textual analysis of corporate disclosure according to the evolution of different automated methods

Abstract

Purpose The purpose of this paper is to give a comprehensive review and synthesis of automated textual analysis of corporate disclosure to show how the accuracy of disclosure tone has been incremented with the evolution of developed automated methods that have been used to calculate tone in prior studies. Design/methodology/approach This study have conducted the survey on “automated textual analysis of corporate disclosure and its impact” by searching at Google Scholar and Scopus research database after the year 2000 to prepare the list of papers. After classifying the prior literature into a dictionary-based and machine learning-based approach, this study have again sub-classified those papers according to two other dimensions, namely, information sources of disclosure and the impact of tone on the market. Findings This study found literature on how value relevance of tone is varied with the use of different automated methods and using different information sources. This study also found literature on the impact of such tone on market. These are contributing to help investor’s decision-making and earnings and returns prediction by researchers. The literature survey shows that the research gap lies in the development of methodologies toward the calculation of tone more accurately. This study also mention how different information sources and methodologies can influence the change in disclosure tone for the same firm, which, in turn, may change market performance. The research gap also lies in finding the determinants of disclosure tone with large scale data. Originality/value After reviewing some papers based on automated textual analysis of corporate disclosure, this study shows how the accuracy of the result is incrementing according to the evolution of automated methodology. Apart from the methodological research gaps, this study also identify some other research gaps related to determinants (corporate governance, firm-level, macroeconomic factors, etc.) and transparency or credibility of disclosure which could stimulate new research agendas in the areas of automated textual analysis of corporate disclosure.

Tax aggressiveness and the proportion of quantitative information in income tax footnotes

“…They extracted textual disclosures by removing tabulated or numeric information from annual reports to compute the readability index or count words that carry specific tones. In these studies, a less readable annual report is one that is lengthy or contains more difficult words (Chakraborty and Bhattacharjee, 2020).…”

“…There have been several studies analysing the characteristics of qualitative data such as readability or linguistic tones in corporate annual reports (Form 10-K) (Li, 2008; Loughran and McDonald, 2011; Loughran and McDonald, 2014; Bonsall and Miller, 2016; Guay et al , 2016). Some have also examined investors’ reactions to these textual characteristics (Rennekamp, 2012; Tan et al , 2014; Chakraborty and Bhattacharjee, 2020). They show that characteristics of qualitative data are correlated with a firm’s earnings performance or incentives to help investors understand their financial information.…”

Corporate Secretary Professional Expertise and Annual Report Readability

“…In addition to financial information, the explanations in this section are a source of information used by investors to assess and consider decisions that will be made afterward. This section represents how transparent the company is in conveying a signal to the public through word choice and narrative writing style that readers can measure the level of ease of understanding (Chakraborty & Bhattacharjee, 2020;Lo et al, 2017).…”

“…Many have researched the variables that affect the readability of the company's annual financial statements. Among them are Subramanian et al (1993), Courtis (1995), Clatworthy & Jones (2001), Lo et al (2017), Chakraborty & Bhattacharjee (2020), Hasan (2020), Ajina et al (2016), Moreno & Casasola (2016), Lim et al (2018), and many more. However, all of these studies examine the related variables in terms of company performance.…”

Corporate liquidity, supply chain and cost issues awareness within the Covid-19 context: evidence from us management reports’ textual analysis

“…As the economic and financial consequences of the pandemic are still unfolding and will probably be long-lasting too, we base our empirical analysis of COVID-19 on alternate data resources. Even though quantitative information extracted from financial statements significantly predicts stock market performance, textual analysis of corporate disclosures has also emerged as an additional tool to help reduce information asymmetry (Chakraborty and Bhattacharjee, 2020). Several studies, which focus on similar methodologies, are using the company reports as they are comprehensive and as such, they form the route of information for the investors (Lo et al, 2017).…”