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Identifying and treating outliers in finance
Author(s) -
Adams John,
Hayunga Darren,
Mansi Sattar,
Reeb David,
Verardi Vincenzo
Publication year - 2019
Publication title -
financial management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.647
H-Index - 68
eISSN - 1755-053X
pISSN - 0046-3892
DOI - 10.1111/fima.12269
Subject(s) - outlier , multivariate statistics , identification (biology) , estimator , econometrics , computer science , anomaly detection , data mining , robust statistics , multivariate analysis , statistics , finance , artificial intelligence , machine learning , mathematics , economics , botany , biology
Outliers represent a fundamental challenge in the empirical finance research. We investigate whether the routine techniques used in finance research to identify and treat outliers are appropriate for the data structures we observe in practice. Specifically, we propose a multivariate identification strategy that can effectively detect outliers. We also introduce an estimator that minimizes the bias outliers caused in both cross‐sectional and panel regressions and provide outlier mitigation guidance. Using replications of four recently published studies in premier finance journals, we show how adjusting for multivariate outliers can lead to significantly different results.