文档介绍：Learning to Be Overconfident
Simon Gervais
University of Pennsylvania
Terrance Odean
University of California, Davis
We develop a multiperiod market model describing both the process by which traders
learn about their ability and how a bias in this learning can create overconfident traders.
A trader in our model initially does not know his own ability. He infers this ability from
his esses and failures. In assessing his ability the trader takes too much credit for
his esses. This leads him to e overconfident. A trader’s expected level of over-
confidence increases in the early stages of his career. Then, with more experience, he
comes to better recognize his own ability. The patterns in trading volume, expected prof-
its, price volatility, and expected prices resulting from this endogenous overconfidence
are analyzed.
It is mon feature of human existence that we constantly learn about our
own abilities by observing the consequences of our actions. For most people
there is an attribution bias to this learning: we tend to overestimate the degree
to which we are responsible for our own esses [Wolosin, Sherman, and
Till (1973), Langer and Roth (1975), Miller and Ross (1975)]. As Hastorf,
Schneider, and Polifka (1970) write, “We are prone to attribute ess to
our own dispositions and failure to external forces.”
In this article we develop a multiperiod market model describing both the
process by which traders learn about their ability and how a bias in this
learning can create overconfident traders. Traders in our model initially do
not know their ability. They learn about their ability through experience.
Traders who essfully forecast next period dividends improperly update
their beliefs; they overweight the possibility that their ess was due to
superior ability. In so doing they e overconfident.
In our model, a trader’s level of overconfidence changes dynamically with
his esses and failures. A trader is not overconfident when he begins
to