Research
Publications, working papers, and work in progress
Publications
Abstract
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.
Abstract
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.
Working Papers
FMA (2025), SoFiE (2025), Eastern FA (2025), MFA (2025), SWFA (2025),
Erasmus University Rotterdam (2024), University College Dublin (2024)
Abstract
We propose a framework that integrates firm characteristics into the estimation of systematic diffusive and jump risks using high-frequency return panels, identifying mispricing orthogonal to decomposed systematic risks. Applying the framework to more than 6,600 U.S. equities, we find that diffusive risk concentrates in classical risk attributes, jump risk draws on valuation, investment, and balance-sheet characteristics, and mispricing is dispersed across a broad set of clusters. A portfolio strategy exploiting the estimated mispricing generates substantial out-of-sample risk-adjusted returns. Both systematic diffusive and jump risks identified by our framework command significant premia, with signed jump risks playing economically distinct roles.
MFA (2025), NBER-NSF Conference (2024), FMA (2024), SoFiE (2024), SETA (2022),
FMA on Derivatives and Volatility (2021), AKFA (2021), KAEA (2021), APAD (2020), Baruch College (2019)
Abstract
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.
MEG (2026)
Abstract
We propose a framework that identifies and exploits \textit{systematic delay} in individual stocks: delayed price adjustment to systematic shocks. Identification exploits the same factor realization entering consecutive return cross-sections, first when the shock occurs and then some stock prices continue to adjust. The framework offers a systematic-delay portfolio that targets the predictable delayed component while hedging contemporaneous factor exposure. In U.S. equities, the portfolio earns an annualized Sharpe ratio of 1.64 and monthly FF6 alpha of 2.40\%. Systematic delay is pervasive across stocks but greater among smaller, less liquid, and younger stocks. Evidence from decimalization supports a trading-frictions channel.
The Conditional Price Efficiency of Passive Investing
CICF (2021), MFA (2020), FMA (2018), NFA (2018), Triple Crown Conference (2019),
Baruch College (2020), Yeshiva University (2019)
Abstract
The efficiency impact of passive investment on stock prices is conditional on existing active mutual fund ownership. Using annual Russell 1000/2000 index reconstitutions as an instrument for passive ownership, we show that instrumented increases in passive holdings produce economically large improvements in price efficiency---lower intraday pricing errors, weaker return autocorrelation, and faster information incorporation---in firms with substantial active ownership, but have negligible effects where active ownership is limited. This complementarity is specific to active ownership: it is not replicated when conditioning on analyst coverage, trading activity, or institutional breadth, and only the active ownership interaction retains significance when all proxies are nested simultaneously. Consistent with an information-intermediation channel, analyst coverage increases, forecast dispersion declines, and post-earnings-announcement drift attenuates exclusively in high-active-ownership firms. These findings reconcile conflicting evidence on passive investing and price informativeness.
SFA (2026, Scheduled)
Abstract
Crash-risk compensation has two dimensions: level and persistence. We develop a framework in which option maturity separates them: short-dated tail options price near-term disaster intensity, while longer-dated tail options also price the risk-neutral continuation. A persistence-pricing wedge makes long-dated crash insurance expensive relative to short-dated protection, so a more negative short-minus-long slope of deep-tail option-implied variance forecasts higher expected returns. Empirical analysis using SPX options supports the model's tail-specific, level-separating, and state-dependent predictions. The tail slope predicts market excess returns, reflects opposite-signed loadings on short- and longer-dated tail implied variances, retains residual predictive content beyond observable channels, and is not subsumed by the VIX-futures term structure. The evidence identifies priced crash-risk persistence as a maturity dimension of crash-risk compensation.
Work in Progress
Momentum and Reversal
Jump Prediction with Machine Learning
Macro News and Systematic Risk
Connectivity of Stock Market and the Cross-Section of Returns