This paper uses hand-collected historical data to provide empirical evidence on the strategic trading behavior of insiders and its consequences for outsiders. Specifically, we collect all equity trades of all insiders and outsiders in an era without legal restrictions on insider trading and a market where trading is non-anonymous. We find that access to private information creates a significant gap between the post-trade returns of insiders and outsiders. Consistent with theory, insiders capitalize on their information advantage by hiding their identity and timing their trades. Both experienced and inexperienced outsiders face expected losses due to this strategic insider trading.

We present evidence on the asset pricing implications of salience theory. In our model, investors overweight salient past returns when forming expectations about future returns. Consequently, investors are attracted to stocks with salient upsides, which are overvalued and earn low subsequent returns. Conversely, stocks with salient downsides are undervalued and yield high future returns. We find empirical support for these predictions in the cross section of US stocks. The salience effect is stronger among stocks with greater limits to arbitrage and during high-sentiment periods. Our results are not explained by common risk factors, return reversals, lottery demand, and attention-grabbing news events.

We propose a hybrid approach for estimating beta that shrinks rolling window estimates toward firm-specific priors motivated by economic theory. Our method yields superior forecasts of beta that have important practical implications. First, unlike standard rolling window betas, hybrid betas carry a significant price of risk in the cross-section even after controlling for characteristics. Second, the hybrid approach offers statistically and economically significant out-of-sample benefits for investors who use factor models to construct optimal portfolios. We show that the hybrid estimator outperforms existing estimators because shrinkage toward a fundamentals-based prior is effective in reducing measurement noise in extreme beta estimates. Received May 17, 2011; accepted October 7, 2015 by Editor Geert Bekaert.