Innovations and Challenges in DeFi
Friday, Jan. 6, 2023 8:00 AM - 10:00 AM (CST)
Lin William Cong, Cornell University
- Agostino Capponi, Columbia University
Don't Trust, Verify: The Economics of Scams in Initial Coin Offerings
AbstractLosses from fraud and financial scams are estimated to exceed U.S. $5 trillion annually. To study the economics of financial scams, we investigate the market for initial coin offerings (ICOs) using point-in-time data snapshots of 5,935 ICOs. Our evidence indicates that ICO issuers strategically screen for naive investors by misrepresenting the characteristics of their offerings across listing websites. Misrepresented ICOs have higher scam risk, and misrepresentations are unlikely to reflect unintentional mistakes. Using on-chain analysis of Ethereum wallets, we find that less sophisticated investors are more likely to invest in misrepresented ICOs. We estimate that 40% of ICOs (U.S. $12 billion) in our sample are scams. Overall, our findings uncover how screening strategies are used in financial scams and reinforce the importance of conducting due diligence.
Equilibrium Staking Levels in a Proof-of-Stake Blockchain
AbstractWe study the equilibrium level of staking in a Proof-of-Stake blockchain when investors have different trading horizons. We find that, contrary to conventional wisdom, staking levels do not always increase in block rewards. Rather, block rewards serve as an inflationary transfer from short-horizon cryptocurrency investors to long-horizon cryptocurrency investors. Thus, increasing block rewards reduces short-horizon cryptocurrency investment which, under certain conditions, reduces the overall transfer to long-horizon cryptocurrency investors and therefore reduces long-horizon investment as well. When this is the case, increasing block rewards decreases total cryptocurrency investment which leads to a reduction in the equilibrium value of staked cryptocurrency.
Fundamental Value Pricing and Bubbles for Nontraditional Assets: The Case of Cryptocurrencies
AbstractWe study the fundamental value pricing relationship for nontraditional assets, for which yields and rare event crash risk are unobservable, and not well understood. We introduce a novel test for deviations from fundamental value pricing for such assets. Simulations show that our test performs well for some benchmark examples that bubble tests based on unit roots or explosive behavior may find hard to handle. For traditional stocks and stock indexes, our test typically does not detect bubbles. When applied to cryptocurrencies, our test suggests suggests the presence of a bubble for multiple large cryptocurrencies at risk adjusted discount rates up to 25% per year, and even at 50% per year. For Ethereum, extremely high risk adjusted discount rates, above 71% per year, are needed for our test to fail to reject the no-bubble null hypothesis. For Stellar, the null is rejected even at the very high risk adjusted discount rate of 100% per year.
- G1 - Asset Markets and Pricing