Earnings Attribution and Information Transfers

Koo, David S.; Wu, Julie; Yeung, P. Eric

doi:10.1111/1911-3846.12308

Cited by 20 publications

(20 citation statements)

References 58 publications

(89 reference statements)

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“…We argue that customers demand information about the supplier's future profitability and product market, including the information contained in management earnings and sales forecasts, because this information is an important aspect of firm viability. Moreover, prior studies have shown that management forecasts are usually accompanied with other related financial and nonfinancial information that explain or justify the forecasts (Baginski et al ; Hutton et al ; Koo et al ). These studies show that managers often supplement their firms' forecasts with explanations of relevant economic factors, such as changes in operational strategies, changes in product mix, and changes in product markets .…”

Section: Prior Studies and Hypothesis Developmentmentioning

confidence: 99%

Customer Concentration and Public Disclosure: Evidence from Management Earnings and Sales Forecasts

Crawford

Huang²,

Li³

et al. 2020

Contemporary Accting Res

100

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This study examines the association between customer base concentration and corporate public disclosure policy. When the customer base is more concentrated, large customers face lower costs of accessing the supplier firm's private information, reducing customers' overall demand for the supplier's public information, suggesting a negative association between customer concentration and the amount of public disclosure. Alternatively, large customers have greater bargaining power and may demand that the supplier firm provide more public disclosures. Consistent with customer concentration facilitating private information flow from the supplier to customers, we find that the frequencies of management earnings and sales forecasts are negatively associated with customer concentration among firms with major corporate customers. These associations are stronger when the supplier and customers are engaged in more relationship‐specific investments, when customers' private information acquisition costs are lower, and when it is less costly for customers to find another supplier.

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Section: Prior Studies and Hypothesis Developmentmentioning

confidence: 99%

Customer Concentration and Public Disclosure: Evidence from Management Earnings and Sales Forecasts

Crawford

Huang²,

Li³

et al. 2020

Contemporary Accting Res

100

View full text Add to dashboard Cite

show abstract

“…Several papers examine causal language and attributions using CL methods (Dikolli, Keusch, Mayew, & Steffen, ; Koo, Wu, & Yeung, ; Zhang & Aerts, ; Zhang, Aerts, and Pan, ). While these studies provide useful insights on aspects of causal reasoning, they focus primarily on measuring the incidence and volume of causal language using causation keywords rather than on distinguishing between defensive and enhancing attributions or identifying the nature of specific causal factor(s).…”

mentioning

confidence: 99%

In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse

El-Haj

Rayson

Walker

et al. 2019

Business Fin & Account

105

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We critically assess mainstream accounting and finance research applying methods from computational linguistics (CL) to study financial discourse. We also review common themes and innovations in the literature and assess the incremental contributions of studies applying CL methods over manual content analysis. Key conclusions emerging from our analysis are: (a) accounting and finance research is behind the curve in terms of CL methods generally and word sense disambiguation in particular; (b) implementation issues mean the proposed benefits of CL are often less pronounced than proponents suggest; (c) structural issues limit practical relevance; and (d) CL methods and high quality manual analysis represent complementary approaches to analyzing financial discourse. We describe four CL tools that have yet to gain traction in mainstream AF research but which we believe offer promising ways to enhance the study of meaning in financial discourse. The four tools are named entity recognition (NER), summarization, semantics and corpus linguistics. K E Y W O R D S 10-K, annual reports, computational linguistics, conference calls, corpus linguistics, earnings announcements, machine learning, NLP, semantics 1Information is the lifeblood of financial markets and the amount of data available to decision-makers is increasing exponentially. Bank of England (2015) estimates that 90% of global information has been created during the last decade, (MD&A), whereas practitioners, standard setters and regulators are often interested in more granular issues such as the format and content of specific disclosures, placement of content within the overall reporting package, limits on the use of jargon concerning particular topics, etc. Second, it is not immediately obvious how commonly employed empirical proxies for discourse quality such as readability (Fog index), tone (word-frequency counts) and text re-use (cosine similarity) map into the practical properties of effective communication identified by financial market regulators.With these caveats in mind, we proceed to review common themes and innovations in the literature and assess the incremental contributions of work applying CL methods over manual content analysis. The median AF study examines 10-K filings using basic content analysis methods such as readability algorithms and keyword counts. The degree of clustering is consistent with the initial phase of the research lifecycle, with agendas shaped as much by ease of data access and implementation as by research priorities. Nevertheless, closer inspection reveals how relatively basic word-level methods have been used to provide richer insights into the properties and effects of financial discourse.Refinements to standard readability metrics, development of domain-specific wordlists, and the use of weighting schemes and text filtering to improve word-sense disambiguation represent welcome advances on naïve unigram word counts. We also acknowledge a move towards the use of more NLP technology in the form of machine learning and topic...

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“…Several papers have empirically examined information transfer in capital markets. For example, some papers focus on the information transfers from earnings announcements and conference calls (e.g., Brochet et al, 2018;Foster, 1981;Ramnath, 2002), while others focus on the transfers from management earnings forecasts (e.g., Baginski, 1987;Han et al, 1989;Kim et al, 2008;Koo, Wu, & Yeung, 2017;Pyo & Lustgarten, 1990) 7 The initial sample also includes transcripts from 2004, 2005, and 2006 but the coverage in these years is limited. So, transcripts from these years get dropped in latter analyzes.…”

Section: 1b the Self-improvement Motive And Upward Comparisonsmentioning

confidence: 99%

Who is Talking about Whom? Determinants and Consequences.

Ward¹

2020

Preprint

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Earnings Attribution and Information Transfers

Cited by 20 publications

References 58 publications

Customer Concentration and Public Disclosure: Evidence from Management Earnings and Sales Forecasts

Customer Concentration and Public Disclosure: Evidence from Management Earnings and Sales Forecasts

In search of meaning: Lessons, resources and next steps for computational analysis of financial discourse

Who is Talking about Whom? Determinants and Consequences.

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