Central supervision and earnings management: Quasi-experimental evidence from China

Pan, Jianping; Weng, Ruoyu; Yin, Sirui; Fung, K. C.

doi:10.1016/j.bar.2022.101082

Cited by 15 publications

(3 citation statements)

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“…Apparently, in regions with lower regulatory intensity, the risk of earnings management detection is typically lower, providing greater incentives for internal actors to engage in such practices [ 79 , 80 ]. Similarly, more marketized provinces tend to provide less oversight and exhibit higher tolerance for earnings management [ 81 ].…”

Section: Discussionmentioning

confidence: 99%

Smart city and earnings management: Evidence from China

Sun,

Chen

2024

PLoS ONE

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Smart cities improve services for businesses, among many other benefits. A comprehensive understanding and effective utilization of these advantages is crucial for promoting business development. Using panel data from Chinese listed companies (2010–2020), this study employs a multi-stage DiD model to investigate the impact of smart cities on corporate earnings management. The findings indicate that the smart city pilot policy has significantly reduced corporate earnings management. Further analysis suggests that smart cities primarily reduce earnings management by improving firms’ external information environments. Additionally, the results show that the policy impact of smart cities is more significant in regions with lower regulatory intensity or higher marketization levels, compared to regions with higher regulatory intensity or lower marketization levels. Similarly, firms in less concentrated markets or those more closely related to smart city development tend to experience greater reductions in earnings management due to smart city construction, unlike firms in more concentrated markets or those less involved. Finally, this paper offers several brief suggestions.

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Section: Discussionmentioning

confidence: 99%

Smart city and earnings management: Evidence from China

Sun,

Chen

2024

PLoS ONE

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“…As in previous studies, we also include several commonly used control variables (Nikbakht et al, 2021;Pan et al, 2022). Specifically, OWNER refers to a set of characteristics of the company's ownership structure before the first listing of shares on the stock exchange.…”

Section: Earnings Management In Ipomentioning

confidence: 99%

Institutional Investors' Response to Earnings Management Before Initial Public Offering in Poland

Wrońska-Bukalska¹,

Sosnowski²,

Wawryszuk-Misztal³

2023

Prague Economic Papers

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The aim of the paper is to analyse the association between the use of accrual-based and real earnings management practices before the company goes public and the decision of institutional investors on buying or refraining from buying shares offered in initial public offering (IPO). The sample consists of 258 Polish new stock companies over the period 2005-2020. We find that such companies refrain from massive earnings management in the pre-IPO period: both real and accrual-based. However, we find evidence that the presence of institutional investors in the IPO is related to earnings inflation (selling, general and administrative expenses). Our study contributes to the debate on the role of institutional ownership in the IPO process in Central and Eastern Europe.

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“…X is a vector of control variables. Following Lin et al (2022a ; 2022b ; 2022c ), Pan et al (2022a ; 2022b ), Kong et al (2020) and Chen et al (2020) , we include the following variables in the regression model: (1) TARGETEXP , which is the number of years that an analyst followed a target; (2) FOLLOWCOMS , measured as natural logarithm of the number of target firms an analyst following in a fiscal year; (3) FOLLOWINDUS , measured as natural logarithm of the number of target industries an analyst following in a fiscal year; (4) GENERALEXP , which is natural logarithm of an analyst's working experience (year); (5) ALLSTAR , measured as a dummy that equals one if an analyst is ranked as first to fifth in the institutional investors’ ranking and zero otherwise; (6) BROKERSIZE , which is measured with natural logarithm of the number of active analysts for a broker in a fiscal year.…”

Section: Data and Empirical Designmentioning

confidence: 99%