Research

Venture capital investing has three key features which impact its risk characteristics: cyclicality, intangibility, and a narrow industry focus. I propose two new venture-capital specific risk factors: intangible investment intensity and technology-sector specialization. I motivate these factors in a model of venture capital general partners facing both investment and fundraising risk. By making more intangible or sector-focused investments, general partners risk not being able to raise new funds from their investors (limited partners) during downturns. This exposure demands risk compensation for general partners beyond common public market benchmarks. The model generates this compensation by allowing them to benefit more strongly from technology shocks when making more intangible or sector-focused investments, at least on average. Consistent with the model, I find startup investments associated with higher levels of intangibility (sector focus) to generate 11% (3%) higher round-to-exit returns and to be around 2% (1%) more likely to be acquired or file for an initial public offering.

Using market data together with household-level consumption data, we extend the factor model ICAPM consistency test of Maio and Santa-Clara (2012). We find that more consistent factor models have less persistent alphas, and more stable betas and out-of-sample mean squared errors. We propose a novel statistical test for the sign the consistency-stability relationship across many factor models and over time. Our methodology allows for the identification of the historically most ICAPM-consistent factor models and factors. Our results suggest that historically consistent models are likely to be stable in the future.

In this paper, I construct portfolios hedging factor performance across different macroeconomic environments. Using a big data approach, I am able to improve traditional factor performance by 1.09-3.4% p.a. on a risk-adjusted basis with monthly rebalancing. A long-short portfolio of volatility-hedged portfolios of Muir and Moreira (2016) traded on the same signals delivers risk-adjusted outperformance of 3.24% per year over the original strategies.

[Presentation available on request]

This paper proposes a novel closed-form model of innovative firms seeking to attract R&D financing in the presence of financial feedback effects and asymmetric information. The model features investment complementarities and multiple equilibria, an inefficient low investment equilibrium and an efficient high investment equilibrium. I then derive conditions under which firms engage in risky showcasing to achieve the high investment equilibrium.

[Presentation available on request]