Risks and Returns of Cryptocurrencywith Aleh Tsyvinski

Review of Financial Studies, 2021, Editor’s Choice

Common Risk Factors in Cryptocurrency”, with Aleh Tsyvinski and Xi Wu

Journal of Finance,  Forthcoming

Long Run Risk: Is It There?”, with Ben Matthies

Journal of Finance, Forthcoming

Working paper

Factor Clustering with t-SNE”, with Philip Greengard, Stefan Steinerberger, and Aleh Tsyvinski, 2020

We cluster asset pricing factors using the t-distributed Stochastic Neighborhood Embedding (t-SNE), one of the most empirically successful dimensionality reduction techniques. t-SNE endogenously separates the strategies into six distinct clusters. The first five clusters resemble the standard value, momentum, investment, profitability, and volatility strategies. The sixth cluster is new, and we denote it as the Firm cluster. We show that the first five clusters are low dimensional and are dominated by their corresponding first principal components, while the Firm cluster is intrinsically high dimensional.

Risk, Return and Diversification in Times of Crisis: (How) Is COVID-19 Different?”, with Jacob Boudoukh, Toby Moskowitz, and Matthew Richardson, 2020

We analyze the impact of COVID-19 on the risk and diversification characteristics of financial securities across major asset classes and countries. Using high frequency data, we first show how the factor structure of asset returns dramatically changes during COVID-19 times compared to both normal times, as well as other crises periods (e.g., Global Financial Crisis). Second, we identify how systematic factors become related to COVID-19 using news/shocks about the virus and epidemiological model forecast errors. Third, we investigate the implications of these findings for popular asset portfolios, with a particular focus on the volatility of these portfolios and their risk exposure. The benefits to diversification and the ability to hedge systematic risk are greatly reduced during the peak of COVID-19 news.

Labor Market Competitors”, with Xi Wu, 2019

  • Winner, Q-Group Jack Treynor Prize
  • WFA Cubist Systematic Strategies Award
  • Best Paper Award, TAMU Young Scholars Finance Consortium
  • Chicago Quantitative Alliance Annual Academic Competition, Second Prize
  • Crowell Memorial Prize, PanAgora Asset Management Finalist

We construct a time-varying network of labor market competitors for all public companies in the United States using the near-universe of online job postings. We find that firms can face vastly different labor market and industry competitors—the overlap between a firm’s labor market competitors and its product market rivals is less than 20 percent. We apply our network to study the effect of labor and industry shocks. We show that hiring decisions, performance, and stock returns strongly comove for firms in the same labor market. Moreover, industry shocks can affect firms outside the industry through the labor network.

Labor-Based Asset Pricing”, 2019

  • Winner, Blackrock Applied Research Award

I establish empirically and theoretically that expectations of returns and cash flows are linked to firms’ labor search decisions. Using a dataset that covers the near-universe of online job vacancy postings, I show that vacancy rates negatively predict stock returns and positively predict cash flows in the cross-section of firms and industries. The predictive power of vacancy postings is strengthened for firms facing less favorable labor-market conditions. Incorporating the supply and demand information of the aggregate labor market, I construct a new measure of employment value that strongly predicts aggregate stock and bond market returns, even in the presence of other known predictors. A production-side asset pricing model that combines heterogeneous production decisions with varying firm labor-market conditions generates these empirical findings.

Do Cryptocurrencies Have Fundamental Value?”, with Jinfei Sheng and Wanyi Wang, 2019

This paper studies the role of technological fundamentals in Initial Coin Offering (ICO) successes and valuations. Using various machine learning methods, we construct four technology indexes for all cryptocurrencies from their ICO whitepapers. We find that the cryptocurrencies with high technology indexes are more likely to succeed and less likely to be delisted subsequently. Moreover, the technology indexes strongly and positively predict the long-run performances of the ICOs. Overall, the results suggest that technological fundamentals are an important determinant of cryptocurrency valuations.

How Does Shareholder Governance Affect the Cost of Borrowing?”, with Xi Wu, 2019

  • Revise and Resubmit, Journal of Accounting and Economics

This paper examines the effect of shareholder activism on firms’ cost of borrowing. Using voting results on close-call shareholder-sponsored governance proposals, we find that banks demand higher interest rates and more general covenants after the passage of governance proposals. The effects on loan terms are more pronounced for ex ante risky firms. Moreover, firms with proposals passed by a small margin become more volatile after the vote, indicating an increase in risk-shifting incentives of the firms. Collectively, our findings suggest that shareholder governance can exacerbate shareholder-debtholder conflicts and raise firms’ costs of debt.