Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour?

Abstract

a b s t r a c tThe behaviour of poker players and sports gamblers has been shown to change after winning or losing a significant amount of money on a single hand. In this paper, we explore whether there are changes in experts' behaviour when performing judgmental adjustments to statistical forecasts and, in particular, examine the impact of 'big losses'. We define a big loss as a judgmental adjustment that significantly decreases the forecasting accuracy compared to the baseline statistical forecast. In essence, big losses are directly linked with wrong direction or highly overshooting judgmental overrides. Using relevant behavioural theories, we empirically examine the effect of such big losses on subsequent judgmental adjustments exploiting a large multinational data set containing statistical forecasts of demand for pharmaceutical products, expert adjustments and actual sales. We then discuss the implications of our findings for the effective design of forecasting support systems, focusing on the aspects of guidance and restrictiveness.

Judgmental selection of forecasting models

“…Franses and Legerstee (2011) found that a simple combination of forecasts outperformed both statistical and judgmentally adjusted forecasts. Petropoulos et al (2016) demonstrated that a 50‐50 combination of forecasts in the period after a manager's adjustments have resulted in significant losses can indeed increase accuracy by 14%. Wang and Petropoulos (2016) found that a combination is as good, if not better, than selecting between a statistical or an expert forecast.…”

Behavioural operational research: Returning to the roots of the OR profession

“…Behavioural issues have been on the forecasting research agenda for a long time, and typically studied under the area of judgemental forecasting. Petropulous, Fildes and Goodwin (2015) examine the effect of big losses on experts' behaviour when performing adjustments to statistical forecasts. The authors show how after a big loss, defined as a judgmental adjustment that significantly decreases the forecasting accuracy compared to the baseline statistical forecast, experts are more likely to make large adjustments in the opposite direction to the previous large error.…”

Behavioral Operational Research

“…For example, there is a stream of research that examines the work of expert modellers (Tako, 2014;Tako & Robinson, 2010;Waisel, Wallace, & Willemain, 2008;Willemain, 1994Willemain, , 1995 , novice modellers (S. G. Tavella & Papadopoulos, 2015b;, or both (Tavella & Papadopoulos, 2015a). Research focusing on other types of actors is also beginning to appear, such as studies of forecasting experts (Petropoulos, Fildes, & Goodwin, 2016;Syntetos, Kholidasari, & Naim, 2016), decision analysts (Papamichail, Alves, French, Yang, & Snowdon, 2007), and OR consultants proving strategy support (O'Brien, 2015).…”