Essays on FinTech

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

Core-competitive Auctions

One of the major drawbacks of the celebrated VCG auction is its low (or zero) revenue even when the agents have high value for the goods and a {\em competitive} outcome could have generated a significant revenue. A competitive outcome is one for which it is impossible for the seller and a subset of buyers to `block' the auction by defecting and negotiating an outcome with higher payoffs for themselves. This corresponds to the well-known concept of {\em core} in cooperative game theory. In particular, VCG revenue is known to be not competitive when the goods being sold have complementarities. A bottleneck here is an impossibility result showing that there is no auction that simultaneously achieves competitive prices (a core outcome) and incentive-compatibility. In this paper we try to overcome the above impossibility result by asking the following natural question: is it possible to design an incentive-compatible auction whose revenue is comparable (even if less) to a competitive outcome? Towards this, we define a notion of {\em core-competitive} auctions. We say that an incentive-compatible auction is $\alpha$-core-competitive if its revenue is at least $1/\alpha$ fraction of the minimum revenue of a core-outcome. We study the Text-and-Image setting. In this setting, there is an ad slot which can be filled with either a single image ad or $k$ text ads. We design an $O(\ln \ln k)$ core-competitive randomized auction and an $O(\sqrt{\ln(k)})$ competitive deterministic auction for the Text-and-Image setting. We also show that both factors are tight

A model for predicting cost control practice in the Ghanaian construction industry

Abstract: One of the key roles of construction project managers is to execute construction projects within the targeted project cost. In Africa, most construction projects suffer huge cost overruns. Project cost control practice is required by every construction firm to keep the project cost in line with the budgeted cost. A comprehension of the different parts of cost control philosophies is fundamental to empower project cost managers to adequately set up robust cost controls and to improve future strategies for active construction project cost delivery. Although there are efforts by project cost managers to control cost, there is a lack of understanding of the factors that determine cost control practice in Ghana, as a developing nation. The factors enhancing cost control practice and a formal model are needed for consideration by project cost managers to guide their operations. This study develops a model for predicting cost control practice in the Ghanaian construction industry. Mixed-method methodology was utilised for this study. The qualitative survey used the Delphi survey approach to investigate the primary factors and measurement-related factors. The study identifies project cost control as eight-factor constructs: project cost estimation, project cost budgeting, project cost reporting, project cost monitoring, project cost analysis, decision-making, change management and project cost communication. These had strong inter-quartile deviations. ..D.Phil. (Engineering Management

Botulinum toxin type A therapy for hemifacial spasm

BACKGROUND: This is an update of a Cochrane Review, first published in 2005. Hemifacial spasm (HFS) is characterised by unilateral, involuntary contractions of the muscles innervated by the facial nerve. It is a chronic disorder, and spontaneous recovery is very rare. The two treatments routinely available are microvascular decompression and intramuscular injections with botulinum toxin type A (BtA). OBJECTIVES: To compare the efficacy, safety, and tolerability of BtA versus placebo in people with HFS. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, reference lists of articles, and conference proceedings in July 2020. We ran the electronic database search, with no language restrictions, in July 2020. SELECTION CRITERIA: Double-blind, parallel, randomised, placebo-controlled trials (RCTs) of BtA versus placebo in adults with HFS. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed records. We planned to select included studies, extract data using a paper pro forma, and evaluate the risk of bias. We resolved disagreements by consensus, or by consulting a third review author. We planned to perform meta-analyses. The primary efficacy outcome was HFS-specific improvement. The primary safety outcome was the proportion of participants with any adverse event. MAIN RESULTS: We found no parallel-group randomised controlled trials comparing BtA and placebo in HFS. AUTHORS' CONCLUSIONS: We did not find any randomised trials that evaluated the efficacy and safety of botulinum toxin type A in people with hemifacial spasm, so we are unable to draw any conclusions. Observational data show a strong association between BtA treatment and symptom improvement, and a favourable safety profile. While it is unlikely that future placebo-controlled RCTs will evaluate absolute efficacy and safety, they should address relevant questions for both people with HFS (such as long-term effects, quality of life, and other patient-reported outcomes), and clinicians (such as relative effectiveness of different BtA formulations and schemes of treatment) to better guide clinical practice.)

Forecasting the Prices of Cryptocurrencies using a Novel Parameter Optimization of VARIMA Models

This work is a comparative study of different univariate and multivariate time series predictive models as applied to Bitcoin, other cryptocurrencies, and other related financial time series data. ARIMA models, long regarded as the gold standard of univariate financial time series prediction due to both its flexibility and simplicity, are used a baseline for prediction. Given the highly correlative nature amongst different cryptocurrencies, this work aims to show the benefit of forecasting with multivariate time series models—primarily focusing on a novel parameter optimization of VARIMA models outlined in this paper. These models are trained on 3 years of historical data, aggregated from different cryptocurrency exchanges by Coinmarketcap.com, which includes: daily average prices and trading volume. Historical time series data of traditional market data, including the stock Nvidia, the de facto leading manufacture of gaming GPU’s, is also analyzed in conjunction with cryptocurrency prices, as gaming GPU’s have played a significant role in solving the profitable SHA256 hashing problems associated with cryptocurrency mining and have seen equivalently correlated investor attention as a result. Models are trained on this historical data using moving window subsets, with window lengths of 100, 200, and 300 days and forecasting 1 day into the future. Validation of this prediction against the actually price from that day are done with following metrics: Directional Forecasting (DF), Mean Absolute Error (MAE), and Mean Squared Error (MSE)