Postprint version. Published in Journal of Banking and Finance, Volume 28, May 1, 2004, pages 931-950.
NOTE: At the time of publication, the author Sanjiv Jaggia was not yet affiliated with Cal Poly.
The definitive version is available at https://doi.org/10.1016/S0378-4266(03)00040-2.
A number of theoretical models, loosely characterized under the rubric of behavioral finance, suggest that price convergence to value is far from instantaneous and possibly involves interplay between noise and informed traders. These models are motivated by documented anomalous patterns in equity markets and assume some form of psychological bias that affects investor behavior. With the benefit of hindsight it seems clear that the technology sector went through a bubble-like pattern in the late 1990s and that investor biases (if indeed they exist and can be inferred) may have been even more pronounced. Accordingly, our study focuses on the medium-term aftermarket in high-tech US IPOs during this period. Using both ordered logit regression and split-population hazard modeling approaches, we document momentum and reversal patterns that are consistent with the predictions of some behavioral finance models. Our findings indicate that momentum variables are important while fundamental variables have at best weak explanatory power.