We develop a method for extracting “other information” from the articulation between bottom-line accounting numbers and stock prices. We posit that “other information” captures future earnings growth originating from conservative accounting recognition principles as demonstrated by Penman and Zhang (2020) and Penman and Zhu (2022), as well as nonzero net present value investment opportunities. Our findings confirm that “other information” is strongly associated with various proxies for expected future earnings growth and firm risk attributes. Furthermore, we show how a structural expected return model incorporating our “other information” estimate can predict out-of-sample future stock returns and generate sizeable long-short return spreads.

We investigate the relationship between investor attention and financial market anomalies. We find that anomaly returns tend to be higher following high-attention days. The result is robust after controlling for the effect of news and in a natural experiment setting in which a stock market regulation and rounding errors generate exogenous variations in attention. An analysis of order imbalances suggests that large traders trade on anomaly signals more aggressively upon observing higher attention. We discuss the extent to which the findings are driven by inattention-driven underreaction, bias amplification, or coordinated arbitrage mechanisms, thereby providing insight into the understanding of anomalies.