This article provides the first comprehensive evidence that the return extrapolation behavior of investors leads to biases in the expectations of volatility. Lower past returns are associated with higher expectations of volatility when using the physical, risk-neutral, and survey measures to estimate volatility expectations. Consistent with the return extrapolation framework, recent past returns have a larger impact than distant past returns on volatility expectations. Biases in volatility expectations are i) distinct from extrapolating past realized volatility, ii) asymmetrically induced by recent past negative returns, and iii) lead investors to pay more to insure against the perceived higher expected volatility.

In this article, we use volatility surface data from options contracts to document a strong, robust, and positive cross-sectional relation between risk-neutral skewness (RNS) and subsequent stock returns. The differential return between high- and low-RNS stocks amounts to 0.17% per week. Preannouncement RNS is positively related to earnings announcement returns, and the positive RNS–return relation is more pronounced for other nonscheduled news releases. This suggests that it is informed trading that drives the positive relation between RNS and subsequent stock returns. We also find that RNS contains incremental information beyond trading signals captured by option-implied volatility and volume.

This study examines the informational content of options trading on acquirer announcement returns. We show that implied volatility spread predicts positively on the cumulative abnormal return (CAR), and implied volatility skew predicts negatively on the CAR. The predictability is much stronger around actual merger and acquisition (M&A) announcement days, as compared with pseudo-event days. The prediction is weaker if pre-M&A stock price has incorporated part of the information, but stronger if the acquirer’s options trading is more liquid. Finally, we find that a higher relative trading volume of options to stock predicts higher absolute CARs. The relation also exists among the target firms.

We develop a new methodology to estimate abnormal performance and risk exposure of nontraded assets from cash flows. Our methodology extends the standard internal rate of return approach to a dynamic setting. The small-sample properties are validated using a simulation study. We apply the method to a sample of 958 private equity funds. For venture capital funds, we find a high market beta and underperformance before and after fees. For buyout funds, we find a relatively low market beta and no evidence for outperformance. We find that self-reported net asset values significantly overstate fund values for mature and inactive funds.

We show that attention constraints on decision-makers create barriers to financial inclusion. Using administrative data on retail loan-screening processes, we find that attention-constrained loan officers exert less effort reviewing applicants of lower socioeconomic status (SES) and reject them more frequently. More importantly, when externally imposed increases in loan officers’ workloads tighten attention constraints, loan officers are even more prone to quickly reject low-SES applicants but quickly accept very high-SES applicants without careful review. Such selective attention allocation further widens the approval rate gap between high- and low-SES applicants—a unique prediction of this attention-based mechanism.