Anchoring: A Valid Explanation for Biased Forecasts When Rational Predictions are Easily Accessible and Well Incentivized?

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Behavioral biases in forecasting, particularly the lack of adjustment from current values and the overall clustering of forecasts, are increasingly explained as resulting from the anchoring heuristic. Nonetheless, the classical anchoring experiments presented in support of this interpretation lack external validity for economic domains, particularly monetary incentives, feedback for learning effects and a rational strategy of unbiased predictions. We introduce an experimental design that implements central aspects of forecasting to close the gap between empirical studies on forecasting quality and the laboratory evidence for anchoring effects. Comprising more than 5,000 individual forecasts by 455 participants, our study shows significant anchoring effects. Without monetary incentives, the share of rational predictions drops from 42% to 15% in the anchor's presence. Monetary incentives reduce the average bias to one-third of its original value. Additionally, the average anchor bias is doubled when task complexity is increased, and is quadrupled when the underlying risk is increased. The variance of forecasts is significantly reduced by the anchor once risk or cognitive load is increased. Subjects with higher cognitive abilities are on average less biased toward the anchor when task complexity is high. The anchoring bias in our repeated game is not influenced by learning effects, although feedback is provided. Our results support the assumption that biased forecasts and their specific variance can be ascribed to anchoring effects. Terms of use: Documents in

An Experimental Study on Social Anchoring

“…Additionally, running a similar analysis for the neutral treatment as in subsection 4.3, Meub et al (2013) show the same pattern of a systematic anchoring bias. For BASIC, the anchoring bias amounts to 0.94% of the average value to predict, compared to 2.1% for the social anchor.…”

“…Although Chapman and Johnson (2002, p.125) state that "incentives reduce anchoring very little if at all" (referring to the studies of Tversky and Kahnemann, 1974;Wilson et al, 1996 andEpley andGilovich, 2005), Wright and Anderson (1989) as well as Simmons et al (2010) show that incentives reduce anchoring if subjects have task familiarity or are provided clues in terms of the direction of adjustment for their initial predictions. Meub et al (2013) show that monetary incentives reduce anchoring to one-third of its strength when compared to a non-incentivized setting. We argue that the ambiguous outcomes regarding the impact of incentives reflect the availability of a simple rational strategy in the respective experiments.…”

“…Lastly, we compare the impact of the social anchor to the results of a similar anchoring experiment, drawn from Meub et al (2013). This study comprehends two analogous treatments implementing the same calculation tasks as in BASIC and COMPLEX, but does not feature a social anchor.…”

“…The average value subsequently serves as the anchor for the following round. To qualify the relevance of the social anchor, we finally compare its impact to results from Meub et al (2013) who feature an identical experimental setting, but implement a classical external anchor.…”

Anchoring and Adjustment Heuristic: A Unified Explanation for Asset-Return Puzzles

“…Furthermore, unlike earnings level, volatility is not directly observable. A robust finding from the anchoring literature is that anchoring-bias is directly proportional to task complexity (Lieder et al 2013, Meub et al 2013. Plausibly, anchoring-bias is larger for volatility than for earnings level.…”