Courtesy of Paul P. Momtaz
Token Offerings or Initial Coin Offerings (ICOs) are a rapidly growing market, with more than 4,500 transactions in 2018 alone. However, there is also substantial fraudulent activity spurred by the absence of regulation. My new paper, Entrepreneurial Finance and Moral Hazard: Evidence from Token Offerings, explores a very subtle, hard-to-track form of fraud: a moral hazard in signaling. I argue that, in token offerings, issuers plausibly have an incentive to bias signals of venture quality to their advantage because there currently are neither functioning institutions that verify signals ex ante nor are there those that punish signals ex post once the bias is detected. If investors are attracted to ventures with the most positive signals and fail to identify biased ones, then firms not sending biased signals may experience a competitive disadvantage. This effectively creates a moral hazard in signaling.
In the paper, I develop a theory of moral hazard in signaling in an entrepreneurial finance context. Manifestations of moral hazard are tied to the ability of dispersed investors to observe the ‘wisdom of the crowd.’ Dispersed investors are individually too small to have an incentive to reduce informational asymmetries on their own in order to be able to verify whether a signal is truthful or not. This creates an environment in which biased signals may go undetected, rendering it profitable for ventures to send biased signals.
The situation plausibly changes when the token gets listed on an exchange platform. Supply and demand dynamics form an equilibrium token price, a fair valuation of the venture, reflecting all available information from all dispersed investors. I argue that the equilibrium price of listed tokens reflects the wisdom of the crowd, which is transparent and readily observable for all dispersed investors. With this information, dispersed investors are able to adjust their venture valuation. Biased signals backfire to the venture because investors will sell these tokens, which creates downward price pressure. In fact, the negative momentum may even hasten platform failure if network effects fail to appear because of potential investor deterrence. In summary, my theory predicts that biased signals are profitable until trading begins (faster time to market and higher funding amount), whereas they backfire as soon as trading begins (lower returns and more likely venture failure).
The empirical results confirm the existence of a systematic moral hazard in signaling. Using an artificial linguistic intelligence on a sample of 495 token offerings, I find that token issuers systematically exaggerate information disclosed in whitepapers. The consequences of manifestations of the moral hazard in signaling in the form of an informational exaggeration bias are consistent with my theoretical predictions. As long as tokens are not traded, ventures sending biased signals are better off. They raise significantly more funds in significantly less time. However, in line with the ‘wisdom of the crowd’ notion of publicly traded token prices, biased signals backfire once trading begins. Biased signals are associated with significantly lower initial returns. Interestingly, they are also characterized by higher initial price volatility that gradually decreases, which reflects investor learning about the true venture quality. Eventually, ventures with biased signals are more likely to fail. Overall, the findings highlight the role token trading plays as a corrective for the moral hazard in signaling created by the absence of institutions in the infant market for token offerings.
The study contributes to the literature on entrepreneurial finance (and its regulation) in several ways. Importantly, the study provides an explanation for the large amount of documented ‘exit scams’ in token offerings. Absent functioning institutions that create trust, a moral hazard in signaling might occur. This has important implications for practitioners such as investors and policy-makers. As a consequence of an enduring absence of institutions that ex ante verify signals or ex post punish biased signals, token offerings might turn into a ‘market for lemons.’
