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FinTech typically describes the application of novel technologies in the financial services sector. These technological innovations aim to compete with traditional financial technologies and improve user experience on a broad range of financial applications. Examples range from peer-to-peer investing services and new settlement procedures to the use of smartphones for mobile banking. Each chapter of this dissertation deals with one of these examples with the goal to draw conclusions for broader economic questions. In the first chapter, Crowdfunding and Demand Uncertainty, I analyze the potential of reward-based crowdfunding to elicit demand information and improve the screening of viable projects vis-à-vis traditional external financing. Crowdfunding allows entrepreneurs to sell claims on future products directly to consumers to finance their investments. At the same time, this peer-to-peer sale of claims generates demand information that benefits the screening process for viable projects. I provide a characterization of the profit-maximizing crowdfunding mechanism when an entrepreneur knows neither the number of consumers who positively value the product nor their reservation prices. Using mechanism design theory, I show that the entrepreneur can finance all viable projects by committing to prices that decrease as the number of pledgers increases. This pricing strategy grants ex-post information rents to consumers with high reservation prices. However, if these information rents are large, then the entrepreneur prefers fixed high prices that lead to underinvestment since consumers with low valuations never participate. The second chapter, Building Trust Takes Time: Limits to Arbitrage in Blockchain-Based Markets, is a joint project with Nikolaus Hautsch and Stefan Voigt. We analyze the potential implications of distributed ledger technologies, such as blockchain, for cross-market trading. Distributed ledgers replace trusted clearing counterparties and security depositories with time-consuming consensus protocols to record the transfer of ownership. We argue that this settlement latency exposes cross-market arbitrageurs to price risk and theoretically derive arbitrage bounds that increase with expected latency, latency uncertainty, volatility in the underlying asset, and arbitrageurs' risk aversion. We then use Bitcoin order book and network data to estimate arbitrage bounds of, on average, 121 basis points, which in fact explain 91% of the observed cross-market price differences in our sample period. Consistent with our theoretical framework, we also find that periods of high latency-implied price risk exhibit large price differences, while asset flows across exchanges chase arbitrage opportunities. Our main conclusion is that blockchain-based settlement introduces a non-trivial friction that impedes arbitrageurs' activity. The third chapter, Perceived Precautionary Savings Motives: Evidence from FinTech, is coauthored with Francesco D'Acunto, Thomas Rauter, and Michael Weber. We use data from a European FinTech banking app provider to study the consumption response to the introduction of a mobile overdraft facility. In addition, we use the banking app to elicit consumers' preferences, beliefs, and motives. We find that users increase their spending permanently, lower their savings rate, and reallocate spending from non-discretionary to discretionary goods. Interestingly, users with a lot of deposits relative to their income react more than others but do not tap into negative deposits. We demonstrate that these results are not fully consistent with conventional models of financial constraints, buffer stock models, or present-bias preferences. We hence label this channel perceived precautionary savings motives: users with a lot of liquidity behave as if they had strong precautionary savings motives even though no observables, including the elicited preferences and beliefs, suggest they should. |
