Each year, firms disclose information that is analyzed and eventually reflected in the market price. Sources of information are for example annual reports, earnings announcements and press releases. In the past, financial accounting research focused primarily on the numerical financial information disclosed (cf. Hales et al. 2011, 224).1 Interestingly, research showed that asset price movements could only partly be explained by this quantitative information and thus must have additional influencing factors (cf. Demers/Vega 2010, 2).
Since corporate disclosure generally consists only to a small fraction of qualitative data and dominantly of textual information (cf. Li 2011, 1)2, and since language is the natural medium through which people communicate, financial accounting research started to focus on the analysis of textual disclosure (cf. Hales et al. 2011, 224). Results of these studies show that different aspects of textual disclosure, like the tone (how information is written/expressed) or the readability can influence for example market prices or analyst behavior (e.g. Li 2010 or Tetlock/Saar-Tsechansky/Macskassy 2008).
This paper focuses on research in the field of tone as important characteristic of corporate textual disclosure. Its aim is to provide an overview about the most recent approaches and about challenges that researchers face.
The remainder of this paper proceeds as follows. In section 2 the importance of textual analysis and the information content of textual information are discussed. Furthermore this section provides an overview about different approaches to characterize textual disclosure and a tabular classification of the recent literature. Since this paper focuses on the tone of textual disclosure, different approaches to measure tone are discussed as well. In section 3 two recent studies are discussed and section 4 concludes with a summary of the main results of this paper and gives suggestions for future research.
Table of Contents
1 Introduction
2 Theoretical Framework
2.1 Importance of textual analysis
2.2 Information content of textual disclosure
2.3 Characteristics of textual disclosure
2.4 Approaches of textual analysis
2.4.1 Manual vs. computer-based approach
2.4.2 Dictionary approach vs. statistical approach
3 Recent Studies
3.1 Feldman et al. (2010)
3.1.1 Data, Sample Selection and Variable Definition
3.1.2 Results
3.1.3 Business Impact
3.2 Hales et al. (2011)
3.2.1 Hypotheses and Design of Experiment 1
3.2.2 Results of Experiment 1
3.2.3 Hypotheses and Design of Experiment 2
3.2.4 Results of Experiment 2
3.2.5 Business Impact
4 Conclusion and future challenges
Objectives and Topics
The paper examines the growing field of financial accounting research focused on corporate textual disclosure. Its primary goal is to provide a comprehensive overview of recent methodologies used to analyze textual data—such as tone and readability—and to discuss the challenges researchers face in transforming qualitative linguistic data into objective numerical information for market analysis.
- The importance of analyzing qualitative data beyond numerical financial disclosures.
- Methodological approaches to textual analysis (manual vs. computer-based, dictionary vs. statistical).
- The impact of tone changes in management disclosures on capital market reactions.
- The influence of vivid versus pallid language on investor judgment and decision-making.
- The role of investor motivation and investment position in processing linguistic information.
Excerpt from the Book
2.4.1 Manual vs. computer-based approach
One fundamental difference in textual analysis is between the manual and the computer based approach. Researchers using the further are manually collecting data or manually developing word lists to analyze corporate disclosure (cf. Larcker/Zakolyukina 2010, 5). One example for a manually developed word list is a study by Li (2006) in which he examines the impact of risk sentiment in annual 10-K filings on future stock returns. In order to measure risk sentiment, he selected and counted words that are related to risk (e.g. “risky”) and uncertainty (e.g. “uncertain”) (cf. Li 2006, 31). An advantage of this approach is that the researcher can select words that are related to his individual research construct and thus guarantee a high level of preciseness (cf. Larcker/Zakolyukina 2010, 5).
Summary of Chapters
1 Introduction: Provides an overview of the shift in financial research from purely numerical data toward analyzing qualitative corporate disclosures.
2 Theoretical Framework: Discusses the significance of textual analysis, the nature of textual information, and the methodologies used to quantify tone and linguistic characteristics.
3 Recent Studies: Presents a detailed analysis of two key studies: Feldman et al. (2010) on tone change and market reactions, and Hales et al. (2011) on the impact of linguistic vividness.
4 Conclusion and future challenges: Summarizes the findings and highlights the limitations of current methodologies, suggesting future research directions in cross-cultural and interdisciplinary analysis.
Keywords
Corporate Disclosure, Textual Analysis, Market Returns, Tone Change, Investor Sentiment, Vividness, Financial Accounting, MD&A, Computational Linguistics, Market Efficiency, Behavioral Finance, Disclosure Complexity, Investor Judgment, Portfolio Construction, Information Content
Frequently Asked Questions
What is the core subject of this paper?
The paper explores how corporate textual information, such as that found in annual reports and MD&A sections, is analyzed to extract price-relevant insights for capital markets.
What are the primary themes discussed?
Central themes include the measurement of tone in corporate disclosures, the distinction between numerical and qualitative data, and how linguistic nuances like vividness influence investor behavior.
What is the main research goal?
The aim is to provide a detailed overview of current research approaches in textual analysis and to explain the challenges and impacts these disclosures have on market participants and asset pricing.
Which scientific methods are primarily addressed?
The paper reviews manual and computer-based methods, including psychosocial dictionaries (e.g., General Inquirer, LIWC) and statistical techniques like Naïve Bayesian algorithms.
What is the focus of the main section?
The main part analyzes two specific studies: Feldman et al. (2010), which investigates the effect of tone change on market returns, and Hales et al. (2011), which examines how the vividness of language affects investor judgments based on their specific investment positions.
How is the paper characterized by its keywords?
The work is defined by terms such as Corporate Disclosure, Textual Analysis, Investor Sentiment, Tone Change, and Behavioral Finance.
How does Feldman et al. (2010) measure tone?
Feldman et al. measure "tone change" by calculating the difference in the proportion of positive and negative words in current versus previous MD&A filings, rather than looking at the absolute level of tone.
What is the significance of the "vivid" versus "pallid" language distinction in Hales et al. (2011)?
The study demonstrates that vivid language is more persuasive and impacts investor judgment more strongly, but this effect depends on whether the information is consistent or inconsistent with the investor’s current market position.
- Quote paper
- Saskia Jarick (Author), 2011, The Impact of Corporate Textutal Disclosure on Capital Markets, Munich, GRIN Verlag, https://www.grin.com/document/174775
