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Article
Publication date: 6 August 2018

Fernando Comiran, Tatiana Fedyk and Joohyung Ha

This paper aims to investigate how media coverage affects the quality of accounting information for seasoned equity offering (SEO) firms.

496

Abstract

Purpose

This paper aims to investigate how media coverage affects the quality of accounting information for seasoned equity offering (SEO) firms.

Design/methodology/approach

The sample includes SEOs completed between January 1993 and December 2014 in the USA that are available from Thomson Financial’s Securities Data Company. The FactSet database was used to measure the amount of media coverage. The paper considers two types of earnings management: accrual-based earnings management and real earnings management.

Findings

This study finds that the media serves as a watchdog for real earnings management but does not affect accrual manipulations. These findings hold when endogenous factors affecting firms’ earnings management choices are controlled for and also when alternative time windows for media coverage are examined.

Originality/value

This paper is the first to demonstrate that media attention affects the quality of accounting information during equity offerings, as it successfully reduces real earnings management.

Details

International Journal of Accounting & Information Management, vol. 26 no. 3
Type: Research Article
ISSN: 1834-7649

Keywords

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Article
Publication date: 12 February 2018

Tatiana Fedyk and Natalya Khimich

The purpose of this paper is to link valuation of different accounting items to research and development (R&D) investment decisions and investigate how suboptimal R&D choices…

1247

Abstract

Purpose

The purpose of this paper is to link valuation of different accounting items to research and development (R&D) investment decisions and investigate how suboptimal R&D choices during initial public offering (IPO) are linked to future operating and market underperformance.

Design/methodology/approach

For firms with substantial growth opportunities, accounting net income is a poor measure of the firm’s performance (Smith and Watts, 1992). Therefore, other metrics such as R&D intensity are used by investors to evaluate firms’ performance. This leads to a coexistence of two strategies: if earnings are the main value driver, firms tend to underinvest in R&D; and if R&D expenditures are the main value driver, firms tend to overinvest in R&D.

Findings

The authors show that the R&D investment decision varies systematically with cross-sectional characteristics: firms that are at the growth stage, unprofitable or belong to science-driven industries are more likely to overinvest, while firms that are able to avoid losses by decreasing R&D expenditure are more likely to underinvest. Finally, they find that R&D overinvestment leads to future underperformance as evidenced by poor operating return on assets, lower product market share, higher frequency of delisting due to poor performance and negative abnormal stock returns.

Originality/value

While prior literature concentrates on R&D underinvestment as a tool of reporting higher net income, the authors demonstrate the existence of an alternative strategy used by many IPO firms – R&D overinvestment.

Details

Review of Accounting and Finance, vol. 17 no. 1
Type: Research Article
ISSN: 1475-7702

Keywords

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Article
Publication date: 2 May 2017

Tatiana Fedyk

The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and…

569

Abstract

Purpose

The purpose of this paper is to examine the way serial correlation in quarterly earnings forecast errors varies with firm and analyst attributes such as the firm’s industry and the analyst’s experience and brokerage house affiliation. Prior research on financial analysts’ quarterly earnings forecasts has documented serial correlation in forecast errors.

Design/methodology/approach

Finding that serial correlation in forecast errors is significant and seemingly independent of firm and analyst attributes, the consensus forecast errors are modeled as an autoregressive process. The model of forecast errors that best fits the data is AR(1), and the obtained autoregressive coefficients are used to predict consensus forecast errors.

Findings

Modeling the consensus forecast errors as an autoregressive process, the present study predicts future consensus forecast errors and proposes a series of refinements to the consensus.

Originality/value

These refinements were not presented in prior literature and can be useful to financial analysts and investors.

Details

International Journal of Accounting & Information Management, vol. 25 no. 2
Type: Research Article
ISSN: 1834-7649

Keywords

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