Research

We focus on the fundamental role of security analysts as information intermediaries using recent advances in the realized variance literature. We construct a signal-to-noise volatility ratio to examine the heterogeneity in the efficiency contributions of analysts' recommendations while controlling for the noise contained in price data. We find that only analysts' revisions with greater efficiency contributions generate significant stock price reactions in the directions expected by the analysts. Furthermore, these revisions increase the degree of informed trading in the options market and reduce the uncertainty related to the covered firms.

Using a recently developed measure of financial market risk perceptions, we show that risk perceptions affect firm-level corporate financing behavior. Firms tend to adjust their capital structures to cater to investors’ appetite for risk. When perceived risks are low, firms tend to choose more leveraged capital structures to take advantage of higher valuations associated with higher risk exposure. When perceived risks are high, firms tend to deleverage to avoid undervaluation associated with higher risk exposure. Furthermore, in periods of low risk perceptions, bond issue announcement returns tend to be higher, whereas long-run returns tend to decline with leverage.

This paper develops a novel functional predictive regression framework linking option-implied distributions to stock market returns, motivated by the fundamental link between risk-neutral and physical densities. Using S&P 500 option panels, our model exhibits significant forecasting power, achieving robust out-of-sample R-squared exceeding 4% for monthly return predictions, outperforming traditional predictors. Superior performance arises from leveraging the full spectrum of the risk-neutral density via functional principal components. Our analysis reveals that forecasting success stems from nuanced variations in risk-neutral densities, reflecting various economic states of risk perception and trading frictions, and demonstrates potential economic gains through a market-timing strategy.

We propose a novel methodology to integrate firm characteristics into the estimation of systematic diffusive and jump risks using high-frequency return panels, ultimately to identify mispricing that is orthogonal to the decomposed risks. Applying our framework to U.S. equities, we obtain characteristic-based diffusive and jump factor betas and discover mispricing. We attribute diffusive risk, jump risk, and mispricing to distinct characteristic clusters, revealing differences in their sources and time variation. The resulting intraday arbitrage portfolio delivers large risk-adjusted returns. We show that our characteristics-based diffusive and jump factors command significant risk premia, with signed jump risks playing economically distinct roles.

We study how passive investment affects the efficiency with which stock prices incorporate information. Using annual Russell 1000/2000 index reconstitutions as an instrument for exogenous variation in passive ownership, we show that the efficiency effects of passive flows are strongly conditional on existing active ownership. In firms with substantial active mutual fund holdings, increases in passive ownership lead to economically large improvements in price efficiency, reflected in lower intraday pricing errors, weaker short-horizon return autocorrelation, and faster incorporation of market-wide information. In contrast, similar passive inflows have little impact where active ownership is limited. Consistent with an information-intermediation channel, analyst coverage increases, forecast dispersion declines, and post-earnings-announcement drift attenuates only in high-active-ownership firms. Overall, passive and active investors play complementary roles in price discovery: index-driven flows enhance efficiency primarily when an informed investor base is already in place. These findings reconcile mixed evidence on passive investing and highlight the importance of ownership structure for market efficiency.

We show that the term structure of crash insurance pricing captures the perceived persistence of disaster risk and forecasts equity returns through a channel economically distinct from the variance risk premium level. We develop a framework in which time-varying persistence pricing of disaster intensity generates a negative relationship between the crash insurance term structure slope---the difference between short- and long-dated tail implied variance---and future equity returns, with four testable implications that find support in the data. Return predictability is confined to the tail corridor and to cross-maturity variation within it. The level's predictive content is fully explained by investor sentiment, while the slope survives a battery of economic controls and strengthens in stressed markets, where the expected duration of crash risk is most relevant for required returns. Sorting by the slope produces a quintile spread of 19.9% per year, robust out of sample and across all subsamples.

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