The power of information in trading and return dynamics

This dissertation examines how investor disagreement shapes stock returns, trading behavior, and the incorporation of information into prices. The central rationale is that disagreement is a key friction in financial markets, yet existing measures often capture only a narrow subset of beliefs, such as analyst forecasts or social-media opinions. To provide abroader and more market-based view, the dissertation develops and tests two complementary measures of disagreement: one based on cross-signal conflict across return-predictive anomalies, and another based on investors’ heterogeneous responses to firm news. The dissertation advances two main hypotheses. First, greater disagreement weakens the return predictability of public signals because investors face more uncertainty about how to interpret those signals. Second, disagreement itself is priced, affects trading responses, and helps explain cross-sectional variation in future stock returns.Methodologically, the first essay constructs anomaly disagreement as the weighted dispersion of active signals across 153 return-predictive anomalies and studies its relation to expected returns, institutional trading, short-selling activity, and the speed of earnings resolution. The second essay develops a news-based disagreement measure using volume-volatility elasticity around firm news in high-frequency data, and tests its ability to predict returns relative to existing disagreement proxies. Both essays use large-scale equity and news datasets, portfolio sorts, and cross-sectional regressions. The results show that disagreement is economically important but operates through distinct channels. In the first essay, higher anomaly disagreement weakens the return predictability of the average anomaly signal, is associated with a positive return premium, dampens investor position adjustment, and slows the incorporation of earnings news. In the second essay, higher news-based disagreement predicts lower future returns, and the proposed elasticity measure outperforms traditional disagreement proxies in explaining the cross section of stock returns. Overall, the dissertation concludes that disagreement is not a single construct but a multidimensional feature of the information environment. Measuring disagreement from both public signals and market reactions to news provides new evidence on how information frictions influence asset prices and investor behavior.