Research
The Cost of Capital and the Innovative Efficiency of Public Firms with Olegb Gredil, Sheri Tice, and Vinh Tran
We study the effects of the cost of capital on innovative efficiency and output. Using idiosyncratic shocks to peer equity valuations, we show that costlier capital increases successful patent applications, future patent citations, and market valuation of future patents. An interquartile increase in the cost of capital increases the innovative efficiency per dollar of intangibles by 0.014 to 0.043 standard deviations, equating to a 6% to 11% increase in average output. In response to adverse shocks to capital cost, firms are observed to curtail their capital and labor investments, reduce sales and fixed assets, yet sustain their R&D spending, suggesting a strategic reallocation of resources to preserve innovation momentum.
Intangible Capital in Factor Models with Huseyin Gulen, Dongmei Li, and Morad Zekhnini
Management Science, Forthcoming
We study the impact of increasingly substantial intangible capital on popular asset pricing models. Conceptually, we explicitly incorporate intangible investment/capital into the dividend discount model in Fama and French (2015) and the classical q-factor model in Hou, Xue, and Zhang (2015) and illustrate the distinctive effects of intangibles on expected stock returns via well-known return predictors such as the book-to-market ratio, investment, and profitability. Our theoretical frameworks highlight the importance of separating tangible from intangible investments and addressing the effects of intangible investment on profitability and other valuation measures. Empirically, we show that incorporating intangibles significantly improves the Fama-French three- and five-factor models and the q-factor model, especially over recent decades during which intangible investments become even more crucial to firms' growth and success.
Link to intangible-adjusted factors
Measuring Intangible Capital with Market Prices with Michael Ewens and Sean Wang
Management Science, Forthcoming
Accounting standards prohibit internally created knowledge and organizational capital from being disclosed on firm balance sheets. As a result, balance sheets exhibit downward biases that have become exacerbated by increasing levels of intangible investments. To offset these biases, researchers must estimate the value of these off-balance sheet intangibles by capitalizing prior flows of R&D and SG&A. In doing so, a set of capitalization parameters must be assumed, i.e., the R&D depreciation rate and the fraction of SG&A that represents a long-lived asset. We estimate these parameters using market prices from firm exits and use them to capitalize intangibles for a comprehensive panel of firms from 1978-2017. We then use a series of validation tests to examine the performance of our intangible capital stocks versus those developed from commonly used parameters. On average, our estimates of intangible capital are 15% smaller than estimates from status-quo parameters while exhibiting larger variation across industry. Intangible capital stocks derived from exit price parameters outperform existing measures when explaining market enterprise values and identifying human capital risk. Adjusting book values with exit-based intangible capital stocks markedly attenuates well-documented biases in market-to-book and return-on-equity ratios while increasing the precision of the HML asset pricing factor. We conclude that our capitalization parameters create intangible stocks that perform equal to or better than status-quo measures in various applications.
Link to Intangible Capital Stocks Data
Link to Purchase Price Allocation (PPA) Data
Link to SDC-Compustat linking table
Volatility and Venture Capital
The performance of venture capital (VC) investments load positively on shocks to aggregate return volatility. I document this novel source of risk at the asset-class, fund, and portfolio-company levels. The positive relation between VC performance and volatility is driven by the option-like structure of VC investments, especially by VCs' contractual option to reinvest. At the asset-class level, shocks to aggregate volatility explain a substantial fraction of VC returns. At the fund level, consistent with the reinvestment channel, this exposure is concentrated in years two through four of fund life and in early-stage VC funds, which have more embedded reinvestment options. For VC-backed portfolio companies, volatility shocks correlate with faster and more frequent reinvestment. The level of volatility at the time of investment has no relation with future performance, consistent with competitive markets. Overall, my results imply that the option-like features of VC investments are first-order determinants of risk in VC.
Intangible Capital and the Investment-q Relation with Lucian A Taylor
Journal of Finacial Economics, Volume 123, Issue 2, pages 251-272, February 2017
The neoclassical theory of investment has mainly been tested with physical investment, but we show that it also helps explain intangible investment. At the firm level, Tobin's q explains physical and intangible investment roughly equally well, and it explains total investment even better. Compared with physical capital, intangible capital adjusts more slowly to changes in investment opportunities. The classic q theory performs better in firms and years with more intangible capital: Total and even physical investment are better explained by Tobin's q and are less sensitive to cash flow. At the macro level, Tobin's q explains intangible investment many times better than physical investment. We propose a simple, new Tobin's q proxy that accounts for intangible capital, and we show that it is a superior proxy for both physical and intangible investment opportunities.
Media: "Towards a Robust Competition Policy", Center for American Progress, April 3rd, 2019
Using Stock Returns to Identify Government Spending Shocks with Jonas DM Fisher
The Economic Journal, Volume 120, Issue 544, pages 414-436, May 2010
This article explores a new approach to identifying government spending shocks which avoids many of the shortcomings of existing approaches. The new approach is to identify government spending shocks with statistical innovations to the accumulated excess returns of large US military contractors. This strategy is used to estimate the dynamic responses of output, hours, consumption and real wages to a government spending shock. We find that positive government spending shocks are associated with increases in output, hours and consumption. Real wages initially decline after a government spending shock and then rise after a year. We estimate the government spending multiplier associated with increases in military spending to be about 1.5 over a horizon of 5 years.