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 higher round-to-exit returns and to be 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 of 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.

Presentations: 8th EBRD and CEPR Research Symposium on The Frontiers of Finance in Emerging Markets,  5th DC Finance Conference, IFC/WBG DEC-PM Brownbag Seminar (co-author)

Venture capital is widely viewed as financing knowledge-intensive, R&D-driven startups, but owing to data constraints, this view has been based largely on high-income economies. By taking a global perspective, this paper documents that what venture capital finances differs systematically across economies, varying with institutional and business conditions rather than following a single model. Using a new cross-country dataset that harmonizes firm-level venture capital records with equity issuance data for more than 150 economies, the data shows that VC markets differ across countries not only in scale but in kind. Outside high-income countries, rather than being concentrated in knowledge intangibles, venture capital tilts–at both the sector and firm levels–toward organizational intangibles such as distribution, logistics, and payments. Highlighting the unique features of venture capital, this pattern is absent from public equity markets, where sectoral composition is far more similar across income levels. An accounting decomposition separates venture capital depth into the rate at which firms enter the market and the funding each entrant attracts, and entry accounts for the largest cross-country gaps. Moreover, the business environment is associated with VC primarily through entry, linked both to the size of the market and to the composition of what venture capital finances, the latter through entry into knowledge-intensive sectors in particular.

IFC/WBG Report on Lowering the Cost of Equity for Businesses in Low- and Middle-Income Countries, led by Paolo Mauro and Cesaire Meh 

Macro-Managed Factor Portfolios

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.

Financing Innovation under Financial Feedback Effects 

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.