iarjbmIARJBMIARJBM2708-5147https://doi.org/10.47310/iarjbm.2021.v02i01.099Investigation of the Effects of Simple Outlier Replacement Protocols in Forecasting Analyses: Are they Robust-in-Utility?FrankHeiligEdwardJ.LuskStrategic Risk-Management, Volkswagen Leasing GmbH, Braunschweig, GermanyEmeritus: The Wharton School, [Dept. Statistics], The University of Pennsylvania, USA & School of Business and Economics, SUNY: Plattsburgh, USA & Chair: International School of Management: Otto-von-Guericke, Magdeburg, GermanyOutliers caused by errors are troublesome in any forecasting analyses. Sometimes errors can be corrected; errors that are related to aggregated or downloaded data often can be identified but sometimes cannot be corrected. In the latter case, the analyst employs outlier screens; if the screen signals a likely outlier, then common practice in the forecasting domain is to replace the suspicions Panel data-point. A standard and very simple replacement protocol is to replace the outlier with the Average of the Nearest Neighbor Panel-points. This is the ANN-replacement protocol. Previous research indicates that more than 50% of the time the ANN-protocol is likely to be Smoothing re: the relative OLS Regression-standard error. There is a predilection to rationalize the use of simple replacement protocols, such as the ANN, by assuming that they are “Robust-in-Utility” in the sense that if they are used when not needed they are usually neutral in their effect thus, they can only be useful. To test this “rationalization”, we (i) collected accounting information to be forecasted from firms on the Bloomberg™ terminals for Income Statement and Balance Sheet sensitive variables and (ii) formed four informative forecasting effect-variables to measure the impact of the ANN-modifications. Using an impact ±2.5% Comfort-Zone, we find that the ANN-protocol had a dramatic effect on relative precision contrary to the often asserted Robustness.