I am an assistant professor of finance at Arizona State University within the W.P. Carey School of Business. I joined ASU after completing my PhD in Financial Economics at MIT Sloan. My research focuses on the interaction of financial markets, labor markets, and innovation. Outside of academia, I am an avid reader and writer of poetry and literary fiction.
Investment composition matters for asset pricing. I develop a production-based model where firms invest in both tangible and intangible capital. The model predicts that, conditional on intangible-adjusted book-to-market, expected returns increase in investment composition---the difference between intangible and tangible investment rates---through differential exposure to displacement risk. Empirically, portfolios sorted on investment composition generate significant alphas, with annual premia of 4--5\% unconditionally and 9--10\% when conditioned on valuation. I validate the mechanism and apply the framework to explain the decline of the value premium as a compositional shift in investment.
We examine the role of process innovation in shaping firm investment and compensation. Empirically, investment, executive pay, and firm valuation ratios all rise with process intensity --- the share of innovation that is process-focused. To account for these patterns, we develop a dynamic agency model in which process innovation enhances firm-specific capital efficiency but exacerbates the hold-up problem for managers and skilled labor. The model not only explains the observed links between process intensity, investment, and compensation, but also predicts a convex relationship between compensation and process intensity. These predictions are supported by the data.
A factor's risk premium can be point-identified even when the vector of risk premia is not. We derive the necessary and sufficient condition---the kernel-orthogonality (KO) condition---and show it is equivalent to the existence of a population mimicking portfolio. When KO fails, standard estimators converge to a random variable rather than a constant, and $t$-tests spuriously reject zero risk premia. We develop a test to determine \emph{which} individual factor risk premia are identified, not just whether the entire model is identified. Applying our methodology to well-known models, we find that certain factors (e.g., consumption growth, intermediary leverage) fail KO while others (e.g., the market) pass.
I recover a strictly positive, semi-parametric stochastic discount factor from test asset returns and use it to estimate and decompose factor risk premia. The SDF is the closest positive probability measure to the empirical distribution such that no-arbitrage holds. Each factor is priced individually against this SDF; the estimate does not depend on which other factors the researcher specifies. Sorting states by marginal utility and cumulating each factor's per-state contribution reveals where the premium originates. Factors differ sharply: SMB earns 91\% of its premium in the worst 5\% of states; HML and RMW reach only 52--65\% by the same point; momentum accumulates almost entirely outside the left tail.