Outlier Screening Protocols for Stock Market Studies: A Suggested Screen

Abstract

In the Data Streaming world, screening for outliers is an often overlooked aspect of the data preparation phase, which is needed to rationalize inferences drawn from the analysis of data. In this paper, we examine the effects of three outlier screens: A Trimming Window, The Box-Plot Screen and the Mahalanobis Screen on the market performance profile of firms traded on the NASDAQ and NYSE. From among seven screening combinations tested, we identify a single screening protocol that is the sequential application of all three screens. This protocol is: (1) simple to program, (2) significantly effective statistically and (3) does not compromise power. This important result demonstrates that for the usual data used by Financial Analysts there is one screening protocol that can be relied upon to satisfy the outlier assumption of the regression model used in generating the usual firm CAPM Return and Risk profile. JEL: Classification: G11, G12, G32, and G30