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Event Studies for Publicly Traded Insurers: An Investigation of the Bad-Model Problem
North American Actuarial Journal Pub Date : 2023-09-08 , DOI: 10.1080/10920277.2023.2214603
Leon Chen 1 , Steven W. Pottier 2
Affiliation  

The potential that abnormal returns are due to a misspecified expected (normal) return model is well known in the event study literature. Prior research shows that this “bad-model problem” is serious in long-run studies, and can also be problematic in short-run studies for firms grouped by certain characteristics. We investigate the bad-model problem for a large sample of insurance firms over an 18-year period, based on nine different expected return models and short- and long-run event windows. Using 1000 samples of randomly selected firms and dates, we find that the different normal return models make little difference in the statistical or economic significance of abnormal returns for short event windows (up to 3 days). However, for longer event windows, such as 1 month and 13 months, statistically and economically significant abnormal returns are more common. Further, we find that characteristic-based benchmark models generally perform better than models that require an estimation period. We also examine a sample of insurers that experienced a financial strength rating downgrade, and find significant differences between characteristic-based benchmark models and other normal return models for the 13-month event window. We recommend that abnormal returns from actual events be evaluated for their qualitative significance in relation to random samples with random event dates. Our results support the need to exercise caution in interpreting the findings of event studies.



中文翻译:

上市保险公司的事件研究:不良模型问题的调查

异常回报可能是由于错误指定的预期(正常)回报模型造成的,这在事件研究文献中是众所周知的。先前的研究表明,这种“坏模型问题”在长期研究中很严重,并且在按某些特征分组的公司的短期研究中也可能存在问题。我们基于九种不同的预期回报模型以及短期和长期事件窗口,调查了 18 年期间大量保险公司样本的不良模型问题。使用随机选择的公司和日期的 1000 个样本,我们发现不同的正常回报模型在短事件窗口(最多 3 天)的异常回报的统计或经济意义方面几乎没有差异。然而,对于较长的事件窗口,例如 1 个月和 13 个月,统计上和经济上显着的异常回报更为常见。此外,我们发现基于特征的基准模型通常比需要估计期的模型表现更好。我们还检查了经历过财务实力评级下调的保险公司样本,发现基于特征的基准模型与 13 个月事件窗口的其他正常回报模型之间存在显着差异。我们建议评估实际事件的异常回报相对于具有随机事件日期的随机样本的定性显着性。我们的结果表明在解释事件研究结果时需要谨慎行事。我们还检查了经历过财务实力评级下调的保险公司样本,发现基于特征的基准模型与 13 个月事件窗口的其他正常回报模型之间存在显着差异。我们建议评估实际事件的异常回报相对于具有随机事件日期的随机样本的定性显着性。我们的结果表明在解释事件研究结果时需要谨慎行事。我们还检查了经历过财务实力评级下调的保险公司样本,发现基于特征的基准模型与 13 个月事件窗口的其他正常回报模型之间存在显着差异。我们建议评估实际事件的异常回报相对于具有随机事件日期的随机样本的定性显着性。我们的结果表明在解释事件研究结果时需要谨慎行事。我们建议评估实际事件的异常回报相对于具有随机事件日期的随机样本的定性显着性。我们的结果表明在解释事件研究结果时需要谨慎行事。我们建议评估实际事件的异常回报相对于具有随机事件日期的随机样本的定性显着性。我们的结果表明在解释事件研究结果时需要谨慎行事。

更新日期:2023-09-10
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