Associate Professor of Finance

London School of Economics

Working Papers

  • Cryptocurrencies and Decentralized Finance (DeFi) (with A. Schoar)
    The paper provides an overview of cryptocurrencies and decentralized finance. The discussion lays out potential benefits and challenges of the new system and presents a comparison to the traditional system of financial intermediation. Our analysis highlights that while the DeFi architecture might have the potential to reduce transaction costs, similar to the traditional financial system, there are several layers where rents can accumulate due to endogenous constraints to competition. We show that the permissionless and pseudonymous design of DeFi generates challenges for enforcing tax compliance, anti-money laundering laws, and preventing financial malfeasance. We highlight ways to regulate the DeFi system which would preserve a majority of benefits of the underlying blockchain architecture but support accountability and regulatory compliance.
  • Blockchain Analysis of the Bitcoin Market (with A. Schoar)
    In this paper, we provide a detailed analysis of the Bitcoin network. We build a novel Bitcoin database and develop a methodology for identifying information about the main market participants. We conduct three major pieces of analysis that focus on the core functions of the new architecture: We document the transaction volumes and network structure of the main participants, the concentration and regional composition of miners which ensure the integrity of the blockchain ledger, and finally, the ownership concentration of the largest Bitcoin holders. We show that the Bitcoin eco-system is still dominated by large and concentrated players.
  • Feedback Trading and Bubbles (with Y. Han)
    The paper develops a model of bubbles that can be taken to the data and explain the behavior of asset prices and their statistics. We depart from the rational expectations framework and assume that investors are only boundedly rational. They observe the price process, but do not fully understand how its volatility and expected returns are determined in equilibrium. Investors learn about the market by looking at past prices. When they observe unexpectedly high returns, they infer that the asset must currently have a high Sharpe ratio, and therefore, allocate a higher share of their wealth to the asset, further increasing the asset price. The interaction of this feedback effect with investors' wealth effect determines the price dynamics and evolution of investors' beliefs in the model. We fit the model to cryptocurrency markets and show that it can successfully explain many empirical facts in these markets.
  • Outsized Arbitrage
    The paper studies incentives and trading decisions of an arbitrageur who can take concentrated bets in an illiquid market and who cares about interim as well as long-term performance. By scaling up his position and using price impact, the arbitrageur can prop up the value of his position, helping him weather periods of low valuation and successfully complete the arbitrage. But that approach also can trap him into building an outsized arbitrage position, which can cause persistent mispricing in the market, even in the presence of other arbitrageurs, and lead to large losses to investors.
  • Sequential Credit Markets (with U. Axelson)
    Entrepreneurs who seek financing for projects typically do so in decentralized markets where they need to approach investors sequentially. We study how well such sequential markets allocate resources when investors have expertise in evaluating investment opportunities, and how surplus is split between entrepreneurs and financiers. Contrary to common belief, we show that the introduction of a credit registry that tracks the application history of a borrower leads to more adverse selection, quicker market break down, and higher rents to investors which are not competed away even as the number of investors grows large. Although sequential search markets lead to substantial investment inefficiencies, they can nevertheless be more efficient than a centralized exchange where excessive competition may impede information aggregation. We also show that investors who rely purely on public information in their lending decisions can out-compete better informed investors with soft information, and that an introduction of interest rate caps can increase the efficiency of the market.
  • Deliberate Limits to Arbitrage (with G. Plantin)
    This paper develops a model in which arbitrageurs are collectively unconstrained, but may still prefer to incur individual limits to arbitrage rather than make full use of their combined resources. These deliberate limits arise because the communication of an arbitrage position reveals the underlying idea, which creates future competition in the absence of relevant property rights. We allow arbitrage opportunities to vary along two dimensions: the ease with which they can be identified and the speed at which they mature. We find that deliberate limits to arbitrage arise for opportunities in the mid-range of the maturity dimension. This range widens when the opportunities are easier to find. Our results thus offer a set of theoretical predictions about the arbitrage trades that are likely to exist in the market.
  • Sources of Systematic Risk (with D. Papanikolau)
    Using the restrictions implied by the heteroskedasticity of stock returns, we identify four factors in the U.S. industry returns. The first correlates highly with the market portfolio; the second is a portfolio of stocks that produce investment goods minus stocks that produce consumption goods; the third differentiates between cyclical and noncyclical stocks. The fourth, a portfolio of industries that produce input goods minus the rest of the market, is a robust predictor of excess returns on the market portfolio and bond returns. The extracted factors are shown to contain significant information about future macroeconomic and financial variables.