文章基本信息

标题：Analyst Optimism in the Automotive Industry: A post-bailout boost and methodological insights
本地全文：下载
作者：Barry Hettler ; Nonna Sorokina ; Yertai Tanai 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2015
卷号：13
期号：3
页码：473-494
出版社：Tingmao Publish Company
摘要：This paper empirically investigates the impact of the government bailout on analysts’ forecast optimism regardingfirms in the automotive industry. We compare the results fromM- and MM-robust methodologies to the results from OLS regression in an event study context and find that inferences change. When M- and MM-robust estimation methods are used to estimate the same model, the results for key control variables fall directly in line with those of similar previous studies. Furthermore, an analysis of residuals indicates that the application of M- and MM-estimation methods pulls the main prediction equation towards the main sample data, suggesting a more rigorous fit. Based on robust methods, we observe changes in analyst optimism during the announcement period of the bailout, as evidenced by the significantly positive variable of interest. We support our empirical results with simulations and confirm significant improvements in estimation accuracy when robust regression methods are applied to the samples contaminated by outliers.
关键词：Financial analysts; M-estimation; optimism; robust regression.