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This paper empirically investigates the impact of the government bailout on analysts’ forecast optimism regarding firms 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 MMestimation 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.

Citation/Publisher Attribution

Hettler, B. R., Sorokina, N., Tanai, Y., Booth, D. (2015). Analyst Optimism in the Automotive Industry: A Post-bailout Boost and Methodological Insights. Journal of Data Science, 13(3), 473-494.

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The authors retain copyright in their articles, subject only to the specific rights given to Journal of Data Science and the Sponsor. By retaining the copyright, the authors are reserving for themselves among other things unlimited rights of electronic distribution, and the right to license the work to other publishers, once the article has been published in JDS by Data Science Trust. After first publication, authors’ only obligation is to ensure that appropriate first publication credit is given to JDS and Data Science Trust.



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