“WSJ Category Kings – the Impact of Media Attention on Consumer and Mutual Fund Investment Decisions”, with Ron Kaniel, Journal of Financial Economics (2017), 123(2), 337-356

We exploit a novel natural experiment to establish a causal relation between media attention and consumer investment behavior, independent of the conveyed information. Our findings indicate a 31 percent local average increase in quarterly capital flows into mutual funds mentioned in a prominent Wall Street Journal “Category Kings” ranking list, compared to those funds which just missed making the list. This flow increase is about 7 times larger than extra flows due to the well-documented performance-flow relation. Other funds in the same fund complex receive substantial extra flows as well, especially in smaller complexes. There is no increase in flows when the Wall Street Journal publishes similar lists absent the prominence of the Category Kings labeling. We show mutual fund managers react to the incentive created by the media effect in a strategic way predicted by theory, and present evidence for the existence of propagation mechanisms including increased fund complex advertising subsequent to having a Category King and increased efficacy of subsequent fund media mentions.

“Fitting the errors-in-variables model using high-order cumulants and moments”, with Timothy Erickson and Toni M. Whited, Stata Journal (2017), 17(1), 116-129

In this article, we consider a multiple mismeasured regressor errors-in-variables model. We present xtewreg, a command for using two-step generalized method of moments and minimum distance estimators that exploit overidentifying information contained in high-order cumulants or moments of the data. The command supports cumulant or moment estimation, internal support for the bootstrap with moment condition recentering, an arbitrary number of mismeasured regressors and perfectly measured regressors, and cumulants or moments up to an arbitrary degree. We also demonstrate how to use the estimators in the context of a corporate leverage regression.

Working Papers

“Knowledge Constraints and Firm Growth”

Firms are constrained in growing firm-specific knowledge capital due to frictions on the transfer of knowledge (such as the process of recruiting and training skilled labor). Constrained firms must therefore forgo some positive-NPV projects during their growth. I embed micro-founded knowledge constraints in a q-theory model of a firm with two capital goods, estimate the model, and compare the impact of knowledge constraints with that of financial frictions on firm investment and growth dynamics. I find that knowledge constraints completely subsume financing frictions in explaining the dynamics of R&D performing firms. The constraint endogenously yields that some firms have higher returns to R&D investment than to physical investment, with estimated differences within the range found in previous empirical work. Small, growing firms are especially affected by the constraint and consequently are more R&D intensive and have higher returns to R&D than large firms. I provide external validity to the estimates by using variation between US states in the level of labor-force training that plausibly slacken firms’ knowledge constraints.

“Predictability, innovation, and competition in Bitcoin’s mining market”, with Einar Kjenstad

We construct and estimate a dynamic oligopoly model of the Bitcoin mining market. Mining equipment manufacturers produce differentiated durable capital goods and endogenously choose optimal investments in R&D. Miners make dynamic purchase decisions based partly on beliefs regarding manufacturers’ future choices. We show that policy-relevant values, such as aggregate R&D investment by manufacturers and network energy consumption, are predictable given only a Bitcoin price-path. We further show the industry is uniquely suited to test the impact of product market competition on innovation, a much-debated subject in the economics of R&D literature.

“Searching Google for Patent Values”

I search Google for the full names of all patents on the USPTO patent dataset, and use Google’s estimated number of results (search hits) as an alternative measure of patent value. I compare this measure with the commonly used number of forward citations and show that each of the two has similar explanatory power in predicting patent value in hedonic firm-value regressions, and that using both measures fares better than using each one independently.  Unlike Google Trend data, which has seen wide use in the finance literature, the use of search hits is, to the best of my knowledge, novel.