Innovation and Asset Prices

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2020

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Abstract

This dissertation studies the relation between technological innovation and asset prices. It explains the heterogeneity in the cross-sectional firm returns from the perspective of the technological race, in which firms with lagging technologies innovate to displace firms with leading technologies. In addition, it studies the resource reallocation from highly innovative firms towards less innovative firms, which is triggered by aggregate uncertainty spikes, and its economic growth implication. It proposes a novel link between technological growth and aggregate economic uncertainty.

The first chapter studies the cross-section of returns from the perspective of firms with differentially advanced technologies. Firms with leading technologies have some market power and enjoy monopolistic rents. Firms with lagging technologies, however, have to sell their products in more competitive markets. Lagging firms innovate to displace leaders in a technological race. I develop a general equilibrium model in which (1) technological leaders have market power and enjoy monopolistic rents, while followers generate no rents, and (2) each period, leaders, followers, and entrants innovate to take or keep the leading positions in the next period. Leading technologies are risky, since market power allows leaders to raise rents in good times and thus their monopoly profits are procyclical. Firms with high exposure to the risk of leading technologies (LTR) have high risk premia. While both current leaders and current followers can be the future leaders, the returns on current followers are more exposed to the future LTR and thus have higher premia, due to the potential large price jump from becoming a new leader. Empirically, I construct the factor that captures LTR. I find that leading technology is risky, and that the LTR price of risk is 7 percent. The followers that actively innovate have high exposure to the future LTR and high risk premia, supporting my model.

The second chapter, co-authored with Ravi Bansal, Mariano Max Croce and Samuel Rosen, shows the existence of a significant link between aggregate uncertainty and reallocation of resources away from R\&D-intensive capital, focusing on both micro and aggregate U.S. data. This link is important because a decrease in the aggregate share of R\&D-oriented capital forecasts lower medium-term growth. In a multi-sector production economy in which (i) growth is endogenously supported by risky R\&D investments, and (ii) the representative agent is volatility-risk averse and has access to other safer technologies that do not support growth, uncertainty shocks have a first-order negative impact on medium-term growth and welfare.

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Liao, Wenxi (2020). Innovation and Asset Prices. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/20970.

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