Research

The relative influence of advice from human experts and statistical methods on forecast adjustments


Reference:

Onkal, D., Goodwin, P., Thomson, M., Gonul, S. and Pollock, A., 2009. The relative influence of advice from human experts and statistical methods on forecast adjustments. Journal of Behavioral Decision Making, 22 (4), pp. 390-409.

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Official URL:

http://dx.doi.org/10.1002/bdm.637

Abstract

Decision makers and forecasters often receive advice from different sources including human experts and statistical methods. This research examines, in the context of stock price forecasting, how the apparent source of the advice affects the attention that is paid to it when the mode of delivery of the advice is identical for both sources. In Study 1, two groups of participants were given the same advised point and interval forecasts. One group was told that these were the advice of a human expert and the other that they were generated by a statistical forecasting method. The participants were then asked to adjust forecasts they had previously made in light of this advice. While in both cases the advice led to improved point forecast accuracy and better calibration of the prediction intervals, the advice which apparently emanated from a statistical method was discounted much more severely. In Study 2, participants were provided with advice from two sources. When the participants were told that both sources were either human experts or both were statistical methods, the apparent statistical-based advice had the same influence on the adjusted estimates as the advice that appeared to come from a human expert. However when the apparent sources of advice were different, much greater attention was paid to the advice that apparently came from a human expert. Theories of advice utilization are used to identify why the advice of a human expert is likely to be preferred to advice from a statistical method

Details

Item Type Articles
CreatorsOnkal, D., Goodwin, P., Thomson, M., Gonul, S. and Pollock, A.
DOI10.1002/bdm.637
DepartmentsSchool of Management
RefereedYes
StatusPublished
ID Code16269

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