Welcome to my website! I am a fifth year PhD candidate in Financial Economics at Duke University's Fuqua School of Business. 

My interests are in asset pricing, nominal rigidities, and macro-finance. 

You can reach me at andrew.kane@duke.edu.

Download my curriculum vitae here.

Working Papers

Product Price Change Timing and Stock Returns

I show that firms with low price change frequency conditional on macroeconomic shocks earn a risk premium. I build a multisector model in which firms face heterogeneous nominal rigidities. Firms with higher price change frequencies after macroeconomic shocks are less exposed to systematic cashflow risk, lowering average equity returns. I create a new dataset that links firms from Compustat to weekly grocery store scanner data. I demonstrate that a common proxy for price change frequency conditional on price gap size, the kurtosis of price changes, carries a risk premium of 6% in the post-2005 period, consistent with the model. This premium cannot be explained by differences in unconditional price change frequency.

Presented at Trans-Atlantic Doctoral Conference (2022), USC Marshall PhD Conference in Finance (2022), Macro Finance Research Program Summer Session Poster Presentation (2022), Inter-Finance PhD Seminar (2022), Midwest Finance Association (2023), Federal Reserve Board (2023), Northern Finance Association (scheduled)

Marking to Market Corporate Debt with Lorenzo Bretscher, Peter Feldhütter, and Lukas Schmid

Models of capital structure and credit risk make predictions about market valuations of debt, but are routinely tested on the basis of book debt from common data sources. In this paper, we propose to close this gap. We construct a rich data set on firm level debt market valuations by carefully matching data on corporate bond and loan secondary market transactions. We document significant discrepancies between market and book values, especially for distressed firms. We use our dataset to i) provide novel rules of thumb on how to adjust leverage and unlever returns using standard datasets, and ii) to revisit a number of prominent empirical patterns involving corporate debt. Using a market-based measure of Tobin's Q, we find little evidence for investment cash-flow sensitivity in our data. We find that using market debt values significantly improves default prediction, and do not detect a credit spread puzzle. In asset pricing tests, we find a leverage premium, but no evidence for a value premium after controlling for market leverage. Moreover, a novel measure of financial distress, namely market-to-book debt, predicts stock returns positively in the cross-section, inconsistent with a financial distress puzzle. 

Presented at European Finance Association Conference (2021), FIRS (2021), CICF (2021), Duke Finance Brownbag (2021), WFA* (2021), SFS Cavalcade* (2021), MFA* (2021), Adam Smith Workshop* (2021), ASU Sonoran Winter Finance Conference* (2021), 

Winner of the Jacob Gold & Associates Best Paper Award, ASU Sonoran Winter Finance Conference 2021.

Are US Monetary Surprises Surprising? Evidence from Global Markets with Sergey Sarkisyan and Tasaneeya Viratyosin

We show that FOMC announcement surprises are predicted by preceding ECB monetary policy announcement surprises. Specifically, a 1 p.p. ECB monetary policy surprise predicts a subsequent 0.25 p.p. FOMC surprise. We find little evidence that this predictability is due to the Fed using the ECB to update forecasts on the US economy or to the ECB releasing new information pertinent to near-term US macroeconomic conditions. Instead, we propose that the Fed responds to non-US economic conditions more strongly than investors expect. We find that the component of FOMC surprises predicted by ECB surprises has significant effects on the US economy. Our results suggest that the Fed’s response to non-US news is an important facet of monetary policy. 

Midwest Finance Association (2023), MFA Doctoral Symposium (2023), Trans-Atlantic Doctoral Conference (2023)

*Indicates presentation by coauthor


A New Analysis Strategy for Designs with Complex Aliasing with Abhyuday Mandal. Published in The American Statistician, 74 (3) 274-281.