Using MD&amp;A to Improve Earnings Forecasts

Bochkay, Khrystyna; Levine, Carolyn B.

doi:10.2139/ssrn.2253054

Cited by 12 publications

(16 citation statements)

References 28 publications

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“…In order to explore text data, Liu and Moffitt (2016) examine the association between SEC comment letters and the probability of a company 10-K restatement using text mining. Bochkay and Levine (2013) examine how the use of text analytics on MD&A information from SEC filings can improve earnings forecasts. The text analysis is based on the ''bag-ofwords'' method without consideration word sequence.…”

Section: Integration Of Evidencementioning

confidence: 99%

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

Issa

Sun²,

Vasarhelyi³

2016

Journal of Emerging Technologies in Accounting

257

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After decades of frustration with long “AI Winters,” various business industries are witnessing the arrival of AI's “Spring,” with its massive and compelling benefits. Auditing will also evolve with the application of AI. Recently, there has been a progressive evolution of technology aimed at creating “artificially intelligent” devices. Although this evolution has been permeated with false starts and exaggerated claims, there is some convergence on the fact that substantive progress has been obtained in the last few years with the adoption of deep learning in conjunction with much faster machines and dimensionally larger storage spaces (and samples). The area of auditing has lagged business adoption in the past (Oldhouser 2016), but is prime for partial automation due to its labor intensiveness and range of decision structures. Several accounting firms have disclosed substantive investments in the AI fields. This paper proposes various areas of AI-related research to examine where this emerging technology is most promising. Moreover, this paper raises a series of methodological and evolutionary research questions aiming to study the AI-driven transformation of today's world of audit into the assurance of the future.

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Section: Integration Of Evidencementioning

confidence: 99%

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

Issa

Sun²,

Vasarhelyi³

2016

Journal of Emerging Technologies in Accounting

257

View full text Add to dashboard Cite

show abstract

“…by making more forward-looking MD&A disclosures (Muslu et al, 2015). Bochkay and Levine (2017) also show that when combined with the textual information in MD&As, quantitative financial information leads to more accurate earnings forecasts.…”

Section: Audit Fees and Mdanda Readabilitymentioning

confidence: 94%

“…In fact, the majority of an annual report, 80 percent on average, is represented by textual narrative (Lo et al, 2017). Textual narratives help investors interpret and understand financial reports (Lo et al, 2017), and also contain valuable incremental information (Bryan, 1997;Clarkson et al, 1999;Bochkay and Levine, 2017). Some aspects of firm fundamentals that cannot be quantified but can affect stock price are better captured in relevant qualitative information (Tetlock et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

MD&A readability, auditor characteristics, and audit fees

Wang

Chen

et al. 2021

Accounting & Finance

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This paper investigates the relationship between audit fees and MD&A readability in China. We find that audit fees generally are higher when MD&A readability is lower. However, we find Big4 auditors and industry specialist auditors do not increase audit fees when MD&As are less readable while the other auditors do. We also find that it is the content unique to the reporting firm, rather than standard industry and market content in a MD&A that affects auditor fees. Our results also support that audit risk and audit efforts are two channels through which MD&A readability affects audit fees.

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“…4. For excellent reviews of the earnings forecasting literature see Echterling et al (2015) and Easton and Monahan (2005). Recent studies in this literature focus on the use of qualitative information, such as narrative disclosures, in earnings forecasting (Bochkay and Levine, 2017) and alternative estimation techniques, such as least absolute deviation (Evans et al , 2017) and mixed data sampling regressions (Ball and Ghysels, 2017) and econometric concerns (Gerakos and Gramacy, 2013). Other studies focus on the term structure of implied costs of equity capital (Callen and Lyle, 2019), correlations between future realised returns and the composite implied cost of capital estimated from cross‐sectional earnings forecasting models (Paton et al , 2020) and links between earnings and stock prices that are helpful in earnings forecasting (Harris and Wang, 2019).…”

Section: Notesmentioning

confidence: 99%

A study of the reliability of cross-sectional earnings forecasting models for estimating IPO firms’ implied cost of capital

Schreder

Bilinski

2021

ARJ

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Purpose This study aims to evaluate the earnings forecasting models of Hou et al. (J Account Econ, 53:504–526, 2012) and Li and Mohanram (Rev Account Stud, 19:1152–1185, 2014) in terms of bias and accuracy and validity of the implied cost of capital (ICC) estimates for a sample of initial public offerings (IPOs). Design/methodology/approach The authors use a sample of 1,657 NYSE, Amex and Nasdaq IPOs from 1972 to 2013. Findings The models of Hou et al. and Li and Mohanram produce relatively inaccurate and biased earnings forecasts, leading to unreliable ICC estimates, particularly for small and loss-making IPOs that constitute the bulk of new listings. As a remedy, the authors propose a new earnings forecasting model, a combination of Hou et al.’s and Li and Mohanram’s earnings persistence models, and show that it produces more accurate and less biased earnings forecasts and more valid ICC estimates. Originality/value The study contributes novel results to the literature on the validity of cross-sectional earnings models in forecasting IPO firm earnings and estimating the ICC. The findings are directly relevant for practitioners, who can improve their earnings forecasting accuracy for IPO firms and related ICC estimates. The insights can be extended to other settings where investors have limited access to financial information, such as acquisitions of private targets.

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Using MD&A to Improve Earnings Forecasts

Cited by 12 publications

References 28 publications

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

Research Ideas for Artificial Intelligence in Auditing: The Formalization of Audit and Workforce Supplementation

MD&A readability, auditor characteristics, and audit fees

A study of the reliability of cross-sectional earnings forecasting models for estimating IPO firms’ implied cost of capital

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