What drives the relevance and quality of experts' adjustment to model-based forecasts?

Experts frequently adjust statistical model-based forecasts. Sometimes this leads to higher forecast accuracy, but expert forecasts can also be dramatically worse. We explore the potential drivers of the relevance and quality of experts' added knowledge. For that purpose, we examine a very large database covering monthly forecasts for pharmaceutical products in seven categories concerning thirty-five countries. The extensive results lead to two main outcomes which are (1) that more balance between model and expert leads to more relevance of the added value of the expert and (2) that smaller-sized adjustments lead to higher quality, although sometimes very large adjustments can be beneficial too. In general, too much input of the expert leads to a deterioration of the quality of the final forecast.