Essays on empirical asset pricing and FinTech

This dissertation studies the determinants of return structure in the cross-section of cryptocurrencies as well as the time-series of market returns during the boom of 2017, the largest known price increase in the history of finance. The first chapter examines the cross-section of cryptocurrencies and hypothesizes and tests that, because of cryptocurrencies' unique features, common demand shocks should be the main driver of the covariance structure of cryptocurrency returns. I proxy for the degree of overlapping exposure to correlated demand shocks using a pairwise “connectivity” measure based on cryptocurrencies' trading locations. I find that this “connectivity” measure explains substantial covariation in the cross-section of cryptocurrency returns. Connected currencies exhibit substantial contemporaneous covariation. In addition, currencies connected to those that perform well outperform currencies connected to those that perform poorly by 71 basis points over the next day and 214 basis points over the next week. Evidence from new exchange listings and a quasi-natural experiment exploiting the shutdown of Chinese exchanges shows that the results cannot be explained by endogenous sorting of currencies into exchanges. Moreover, using machine learning techniques to analyze social media data, I find that the demand effects are 40 to 50% larger for currencies that rely more heavily on network externalities of user adoption. This amplified effect is consistent with the notion that demand for a cryptocurrency not only signals investment motives, but also can be perceived as user adoption that potentially affects the fundamental value of these assets. The second chapter examines the time-series of Bitcoin and the aggregate cryptocurrency returns during the boom of 2017. In particular, Bitcoin and other cryptocurrencies offer the promise of an anonymous, decentralized financial system free from banks and government intervention. Ironically, new large entities have gained centralized control over the vast majority of operations in the cryptocurrency world. One type of these large centralized entities is stable coin issuers who can act as a central bank in the crypto world by controlling the supply of money. This chapter examines the role of the largest stable coin, Tether, on Bitcoin and other cryptocurrency prices. Using algorithms to analyze blockchain data, we find that purchases with Tether are timed following market downturns and result in sizable increases in Bitcoin prices. The flow is attributable to one entity, clusters below round prices, induces asymmetric autocorrelations in Bitcoin, and suggests insufficient Tether reserves before month-ends. Rather than demand from cash investors, these patterns are most consistent with the supply-based hypothesis of unbacked digital money inflating cryptocurrency prices