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Dr. Christopher Krauss

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Office: LG 4.176
Phone: +49 (0) 911 – 5302 – 276
E-Mail: christopher.krauss@fau.de
Office hours: Wednesday, 14.45 – 15.45
Information: Curriculum vitae
Research

Curriculum vitae

Personal information
Name: Christopher Krauss
Date of Birth: October 20, 1985
Place of Birth: Nuremberg
Education
09/1996 – 07/2002 Wolfgang-Borchert-Gymnasium, Langenzenn
08/2002 – 07/2003 East Forsyth High School, North Carolina, USA
09/2003 – 06/2005 Abitur, Wolfgang-Borchert-Gymnasium, Langenzenn
06/2010 – 04/2011 M.Sc. in Business and Engineering („Wirtschaftsingenieurwesen“)
Friedrich-Alexander-University
Professional experience
09/2005 – 07/2006 Siemens VDO Automotive S.A.S., Toulouse
Internship: Business development support for a leading automotive group
04/2007 – 07/2007 Chair of Engineering Mechanics, Friedrich-Alexander-University
Student assistant
05/2008 – 07/2008 Bain & Company Germany, Inc., Munich
Internship: German market strategy for a logistics service provider
08/2008 – 10/2008 McKinsey & Company, Inc., Munich
Internship: Improving the production footprint of a leading European automotive group
05/2010 – 06/2010 McKinsey & Company, Inc., Munich
Internship: Financial performance benchmarking for a leading automotive group
Since 08/2011 McKinsey & Company, Inc., Munich, Strategy Consultant
Assignments in the financial service, high tech, and automotive industry
06/2016 PhD in Statistics and Econometrics, Friedrich-Alexander-University
Thesis: Essays on statistical arbitrage

Research

Postdoctoral research associate with research focus on statistical arbitrage, relying on:

  • Deep learning (deep neural networks, recurrent neural networks, LSTMs, etc.)
  • Machine learning (boosting, random forests, generalized linear models, etc.)
  • Econometrics and time series analysis (cointegration, GARCH models, copulas, etc.)
  • Financial theory (momentum strategies, mean-reversion strategies, etc.)
  • Statistical computing with R and Python
  • High-performance computing on GPUs and on Amazon Web Services
  • Daily and high-frequency financial market data

Publications

  • Krauss, C. (2015): Statistical arbitrage pairs trading strategies: Review and outlook (IWQW-09-2015)
  • Krauss, C., Herrmann, K. and Teis, St. (2015): On the power and size properties of cointegration tests in the light of high-frequency stylized facts (IWQW-11-2015)
  • Krauss, C., Beerstecher, D. and Krüger, T. (2015): Feasible earnings momentum in the U.S. stock market: An investor’s perspective (IWQW-12-2015)
  • Krauss, C., Krüger, T. and Beerstecher, D. (2015): The Piotroski F-Score: A fundamental value strategy revisited from an investor’s perspective (IWQW-13-2015)
  • Krauss, C. and Stübinger, J. (2015): Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100 (IWF-15-2015)
  • Krauss, C., Do, X. and Huck, N. (2016): Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 (IWF-03-2016)
  • Clegg, M. and Krauss, C. (2016): Pairs trading with partial cointegration (IWF-05-2016)
  • Krauss, C. (2016): Statistical arbitrage pairs trading strategies: Review and outlook (Journal of Economic Surveys, forthcoming)
  • Krauss, C., Do, X. and Huck, N. (2016): Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
    (European Journal of Operational Research, forthcoming)
  • Stübinger, J., Mangold, B. and Krauss, C. (2016): Statistical arbitrage with vine copulas (IWF-11-2016 )

Conferences and workshops

  • On the persistence of cointegration relationships in a high-frequency setting. CEQURA Conference 2015 on Advances in Financial and Insurance Risk Management, Munich, October 2015.
  • Big data in the automotive industry. Audi Planung GmbH, Ingolstadt, July 2015.
  • On the power of cointegration tests in the light of high-frequency stylized facts. Deutsche Börse AG, Eschborn, July 2015.

Teaching

  • Winter term 2014/15:
    Teaching assistant in Statistics
  • Summer term 2015:
    Teaching assistant in Multivariate Time Series Analysis
    Teaching assistant in Advanced Data Analysis
  • Winter term 2015/16:
    Teaching assistant in Applied Time Series Analysis
  • Summer term 2016:
    Teaching assistant in Multivariate Time Series Analysis