Analyst Optimism in the Automotive Industry: A Post-Bailout Boost and Methodological Insights

Journal of Data Science

article

Barry Hettler¹  Nonna Sorokina²  Yertai Tanai³  David Booth³ 

[1] School of Business Administration and Economics, The College at Brockport – SUNY;School of Business, Wake Forest University;Department of Management and Information Systems, College of Business Administration, Kent State University

关键词: Financial analysts;    M-estimation;    optimism;    robust regression;   

DOI  :  10.6339/JDS.201507_13(3).0004

学科分类：土木及结构工程学

来源: JDS

【 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 from M- 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.

【 授权许可】

CC BY