We try to quantify these so we can gauge how they will affect the price of a stock. After completing the model, we launched a behavior-finance based fund in Europe in 1999. But the image effect can temporarily disappear, as we have seen with the technology, media and telecom sector. To quantify overreaction, the variable is last month's stock-price change. If the model detects a sharp price movement, it picks out the stock as a potential victim of overreaction to recent news.
Investors also tend to underweigh fundamental information such as a new trend.
Overconfidence is measured by the number of analysts that have revised their earnings estimates and recommendations for a stock. Investors tend to have excessive confidence in their own opinions. People seldom revise their raw estimates as fast as the analysts. We don't have a static model where we just plug in the numbers and the recommendation comes out. We also refine the weights to various factors based on different market circumstances.