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Christoph Scheuch

Head of BI & Data Science

wikifolio Financial Technologies

Biography

I am the Head of Business Intelligence and Data Science at the social trading platform wikifolio.

Before joining wikifolio, I graduated from the Vienna Graduate School of Finance where my research focused on the economics of technological innovations in the financial sector. One of my papers shows how blockchain-based settlement introduces limits to arbitrage in cross-market trading. In another paper, I analyze how consumers react to the introduction of overdraft facilities through a mobile banking app. In my solo-authored paper, I investigate the potential of crowdfunding mechanisms to elicit demand information and improve the screening of viable projects under different valuation structures.

In my spare time, I replicate asset pricing approaches or build visualizations for arbitrary economic stories which all eventually end up on this page.

Interests

  • Data Science
  • Tidyverse
  • FinTech
  • Blockchain

Education

  • PhD in Finance, 2020

    Vienna Graduate School of Finance

  • MSc in Economics, 2015

    University of Vienna

  • BSc in Economics, 2012

    University of Vienna

Recent Posts

Tidy Unsupervised Learning - Part I: Clustering Binary Data

An application of different clustering approaches to simulated survey responses

Visualizing Historical Food Prices in Units of Fiat, Gold, or Stock

How did food prices evolve during and after the Gold Standard using different numeraires?

Construction of a Historical S&P 500 Total Return Index

How to construct a S&P 500 total return index since 1928 using R

Scraping ESG Data from Yahoo Finance with R

How to scrap environmental, social and governance (ESG) risk scores from Yahoo Finance using R

Tidy Asset Pricing - Part V: The Fama-French 3-Factor Model

A replication effort of the famous Fama-French factors

Research

Crowdfunding and Demand Uncertainty

Reward-based crowdfunding allows entrepreneurs to sell claims on future products to finance investments and, at the same time, to generate demand information that benefits screening for viable projects. I characterize the profit-maximizing crowdfunding mechanism when the entrepreneur knows neither the number of consumers who positively value the product, nor their reservation prices. The entrepreneur can finance all viable projects by committing to prices that decrease in the number of pledgers, which grants consumers with high reservation prices information rents. However, if these information rents are large, then the entrepreneur prefers fixed high prices that lead to underinvestment.

Presentations: $2^{nd}$ Toronto FinTech Conference, VGSF Conference 2018 & 2019

Building Trust Takes Time: Limits to Arbitrage in Blockchain-Based Markets

Distributed ledger technologies replace trusted clearing counterparties and security depositories with time-consuming consensus protocols to record the transfer of ownership. This settlement latency exposes cross-market arbitrageurs to price risk. We theoretically derive arbitrage bounds that increase with expected latency, latency uncertainty, volatility and risk aversion. Using Bitcoin orderbook and network data, we estimate arbitrage bounds of on average 121 basis points, explaining 91% of the observed cross-market price differences. Consistent with our theory, periods of high latency-implied price risk exhibit large price differences, while asset flows chase arbitrage opportunities. Blockchain-based settlement thus introduces a non-trivial friction that impedes arbitrage activity.

Presentations: QFFE 2018, $1^{st}$ International Conference on Data Science in Finance with R, $4^{th}$ Konstanz-Lancaster Workshop on Finance and Econometrics, Crypto Valley Blockchain Conference 2018, HFFE 2018, CFE 2018, CUNEF, University of Heidelberg, University of Vienna, University of Graz, $2^{nd}$ Toronto FinTech Conference, $4^{th}$ Vienna Workshop on High-Dimensional Time Series 2019, Conference on Market Microstructure and High Frequency Data 2019, FIRS Conference 2019, $12^{th}$ Annual SoFiE Conference, IMS at the National University of Singapore, $3^{rd}$ SAFE Microstructure Conference, EFA Annual Meeting 2019, Vienna Congress on Mathematical Finance, International Conference on Fintech & Financial Data Science 2019, $4^{th}$ International Workshop in Financial Econometrics, CFM-Imperial Workshop 2019, WFA 2020

Perceived Precautionary Savings Motives: Evidence from FinTech

In a representative sample of new borrowers, access to lines of credit increases the spending of more liquid households permanently. Liquid consumers reduce their existing savings but do not tap into negative deposits, and hence do not raise debt. Through our FinTech bank setting, we elicit consumers’ risk preferences, beliefs, perceptions, and other characteristics directly. No proxies for precautionary savings motives differ systematically across liquid and illiquid consumers. Liquid consumers appear to have higher subjective beliefs about precautionary savings, despite no different objective characteristics. Our results have implication for the transmission of policy to households through bank credit.

Presentations: Columbia New Technologies in Finance Conference 2019, Red Rock Finance Conference 2019, Summer Finance Conference at ISB 2019, $7^{th}$ ABFER Annual Conference, LBS Summer Finance Symposium 2019, CFPB Conference 2019, AEA and AFA Annual Meeting 2020

Fishing with Pearls: The Value of Lending Relationships with Prestigious Firms

We provide novel evidence of banks establishing lending relationships with prestigious firms to signal their quality and attract future business. Using survey data on firm-level prestige, we show that lenders compete more intensely for prestigious borrowers and offer lower upfront fees to initiate lending relationships with prestigious firms. We also find that banks expand their lending after winning prestigious clients. Prestigious firms benefit from these relations as they face lower costs of borrowing even though prestige has no predictive power for credit risk. Our results are robust to matched sample analyses and a regression discontinuity design.

Presentations: FIRS Conference 2018

Teaching

I was a teaching assistant for the following courses:

  • University of Vienna: Calculus, Keynes for Beginners, International Macroeconomics, Macroeconomics and Inequality
  • TU (Vienna University of Technology): Microeconomics
  • Gutman Private Welath Management Seminar: FinTech
  • WU (Vienna University of Economics and Business): Linear Algebra

Some useful notes for students:

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