  The methodology presented provides a quantitative way to characterize
investor behavior and price dynamics within a particular asset class and time
period. The methodology is applied to a data set consisting of over 250,000
data points of the S&P 100 stocks during 2004-2018. Using a two-way
fixed-effects model, we uncover trader motivations including evidence of both
under- and overreaction within a unified setting. A nonlinear relationship is
found between return and trend suggesting a small, positive trend increases the
return, while a larger one tends to decrease it. The shape parameters of the
nonlinearity quantify trader motivation to buy into trends or wait for
bargains. The methodology allows the testing of any behavioral finance bias or
technical analysis concept.
