{"title":"锚定投资者对首次公开募股前盈利管理的反应:来自新兴市场的证据","authors":"Sahil Narang, Rudra P. Pradhan","doi":"10.1108/mf-04-2023-0264","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. 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引用次数: 0
摘要
本研究旨在考察锚定投资者(AIs)对上市前盈利管理(EM)的反应。作者使用了来自印度锚定实验的独特的详细投标数据。作者还研究了知名锚定投资者对首次公开募股前盈利管理的检测能力和定价行为。作者利用带有行业和年份固定效应的逻辑回归和托比特回归模型,研究了人工智能参与的各种措施与短期和长期可自由支配权责发生制代用指标之间的关系。研究结果作者发现,上市前人工智能与锚支持的可能性呈正相关,但与知名锚支持的可能性呈负相关。研究结果表明,首次公开募股前的新兴市场会误导人工合成企业,但声誉良好的人工合成企业不会。作者还发现,与无声誉的人工合成企业相比,有声誉的人工合成企业所支付的费用随着 EM 的增加而低于上限。这些发现对于监管机构在非锚定市场实施人工智能概念以及在现有锚定环境中更好地实施政策具有重要意义。研究结果也适用于 IPO 领域的非机构投资者。然而,这是第一份分析人工智能企业针对首次公开招股前市场化的首次公开招股投标行为的研究报告。
Response of anchor investors to pre-IPO earnings management: evidence from an emerging market
Purpose
This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.
Design/methodology/approach
The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.
Findings
The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.
Practical implications
The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.
Originality/value
This is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.
期刊介绍:
Managerial Finance provides an international forum for the publication of high quality and topical research in the area of finance, such as corporate finance, financial management, financial markets and institutions, international finance, banking, insurance and risk management, real estate and financial education. Theoretical and empirical research is welcome as well as cross-disciplinary work, such as papers investigating the relationship of finance with other sectors.