Overweighting Private Information: Three Measures, One Bias?

Overweighting private information is often used to explain various detrimental decisions. In behavioral economics and finance, it is usually modeled as a direct consequence of misperceiving signal reliability. This bias is typically dubbed overconfidence and linked to the judgment literature in psychology. Empirical tests of the models often fail to find evidence for the predicted effects of overconfidence. These studies assume, however, that a specific type of overconfidence, i.e., "miscalibration," captures the underlying trait. We challenge this assumption and borrow the psychological methodology of single-cue probability learning to obtain a direct measure for overweighting private information. We find that overweighting private information and measures of "miscalibration" are unrelated, indicating that different kinds of misperceptions are at work. Thus, in order to test the theoretical predictions of the overconfidence literature in economics and finance, one cannot rely on the well-established "miscalibration" bias. We find no gender differences in overconfidence for our measures except for one, where women are more overconfident than men.