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Peter Woehrmann

Consulting Associate Professor, Management Science and Engineering

Bio
Peter has more than 20 years of experience researching and implementing cutting-edge methods for prediction, optimization, and trading in the industry. Prior to becoming an entrepreneur he gained work experience at Deutsche Bank, UBS, and a large pension fund. His current portfolio includes the launch of two webpages (retail wealth management for consumers / sentiment-based real-time search engine for news and blogs) and starting a hedge fund.
Academic Appointments: 
Consulting Associate Professor, Management Science and Engineering
Administrative Appointments: 
Faculty, Center for Financial and Risk Analytics, see links (2013 - Present)
Faculty, Stanford Quantitative Finance Certificate Program, see links (2013 - Present)
Referee, National Science Foundation, NSF (2008 - Present)
Referee, Risk Magazine, Computational Economics, Journal of Economic Dynamics & Control (2004 - Present)
Referee, Journal of Financial Markets and Portfolio Management (2012 - Present)
Honors and Awards: 
Research Grant, National Centers of Competence in Research, NCCR, Switzerland (2004-2009)
Research Grant, Ecoscentia Foundation, Switzerland (2008)
Research Grant, Deutsche Forschungsgesellschaft, DFG, Germany (1998-2000)
Professional Organizations: 
External Thought Leader, UBS Wealth Management (2013 - Present)
Education: 
Ph.D., University of Bielefeld, Germany, Quantitative Methods and Economics (2000)
B.A. and M.Sc., University of Kiel, Germany, Quantitative Methods, Economics, and Computer Science (1996)
Research & Scholarship
Current Research Interests: 
My research interests are robust prediction and optimization in finance and "big data" (e.g. advertisement).

We propose a new statistical method to improve reliability of state-of-the-art methods used in research and in the industry for prediction, portfolio optimization, or risk assessment. Our information theoretic method is documented in the "Parameter-free inference", with Professor David G. Luenberger, 2012.

The impact of estimation errors of nonlinear and nonparametric methods is well put in recent publications:

"The literature is difficult to absorb. Different papers use different techniques, variables, and time periods. Results from papers that were written years ago may change when more recent data is used. Some papers contradict the findings of others. Still, most readers are left with the impression that 'prediction [of the equity premium] works' - though it is unclear exactly what works." Goyal/Welch, Review of Financial Studies, 2007.

"We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. ... none [of the stat-of-the-art portfolio optimization methods] is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, [because of estimation errors]. This suggests that there are still many 'miles to go' before the gains promised by optimal portfolio choice can actually be realized." DeMiguel/Garlappi/Uppal, Review of Financial Studies, 2007.

In order to overcome statistical illusion, we propose a new inference method which does not involve any estimated/tuned parameter, while no assumptions on underlying probability distributions or stochastic processes are made.We present a parameter-free density estimator which is more accurate in finite samples than competitive estimation schemes like kernel density estimation with adaptive bandwidths. This is the core of deriving a parameter-free prediction method which does not need assumptions on the underlying data generating process. Numerical experiments show that benchmark prediction methods are out-performed for different data sets. Based on the Law of Small Numbers a stochastic optimization method is proposed which reveals fast convergence to optimal portfolios in small samples. An accurate measure of (Conditional) Value-at-Risk is obtained based on the Central Sample Theorem. Our new method is also a valuable tool in "big data" tasks, since our universal prediction and optimization method is convex, thus computationally very tractable.
Projects: 
Predicting click-through rates of advertisement
Title: 
Predicting click-through rates of advertisement
Detail: 

Location

Stanford, CA

Optimal sponsored search advertisement
Title: 
Optimal sponsored search advertisement
Detail: 

Location

Stanford, CA

Extracting sentiment from news, blogs, and tweets through naive Bayes and support vector machines with an application to stock picking
Title: 
Extracting sentiment from news, blogs, and tweets through naive Bayes and support vector machines with an application to stock picking
Detail: 

Location

Stanford, CA

A quantitative approach to Due diligence of private equity / venture capital deals
Title: 
A quantitative approach to Due diligence of private equity / venture capital deals
Detail: 

Location

Stanford, CA

Including alternative investments (hedge funds, private equity and venture capital) into traditional portfolios using conditional value-at-risk optimization
Title: 
Including alternative investments (hedge funds, private equity and venture capital) into traditional portfolios using conditional value-at-risk optimization
Detail: 

Location

Stanford, CA

Automated market making (liquidity providing) in double auctions (U.S. stock and futures markets): Building a Java-API for high-frequency trading
Title: 
Automated market making (liquidity providing) in double auctions (U.S. stock and futures markets): Building a Java-API for high-frequency trading
Detail: 

Location

Stanford, CA

Volatility as an asset class: Timing with stochastic volatility models
Title: 
Volatility as an asset class: Timing with stochastic volatility models
Detail: 

Location

Stanford, CA

Exploiting behavioral biases in fixed income futures around news releases: Building a Java-API for high-frequency trading
Title: 
Exploiting behavioral biases in fixed income futures around news releases: Building a Java-API for high-frequency trading
Detail: 

Location

Stanford, CA

Exploiting the volatility risk premium using options contracts
Title: 
Exploiting the volatility risk premium using options contracts
Detail: 

Location

Stanford, CA

Investing into stocks based on earnings revisions: A nonlinear econometric model
Title: 
Investing into stocks based on earnings revisions: A nonlinear econometric model
Detail: 

Location

Stanford, CA

Statistical arbitrage (generalized pairs trading) in the U.S. stock market: Building a Java-API for high-frequency trading
Title: 
Statistical arbitrage (generalized pairs trading) in the U.S. stock market: Building a Java-API for high-frequency trading
Detail: 

Location

Stanford, CA

Football analytics: Predicting run/pass decisions in the NFL using econometric methods and ideas from behavioral sciences.
Title: 
Football analytics: Predicting run/pass decisions in the NFL using econometric methods and ideas from behavioral sciences.
Detail: 

Location

Stanford, CA

Teaching
Courses Taught: 
Academic Year: 
2014-15
Courses: 
Investment Science
MS&E 245A (Sum)
Independent Study Courses: 
Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum)
Academic Year: 
2013-14
Courses: 
Independent Study Courses: 
Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum)
Academic Year: 
2012-13
Courses: 
Independent Study Courses: 
Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum)
Academic Year: 
2011-12
Courses: 
Independent Study Courses: 
Directed Reading and Research
MS&E 408 (Aut, Win, Spr, Sum)
Publications
Parameter-free inference unpublished working paper Luenberger, D. G., Woehrmann, P. 2012
Risk aversion in the large and in the small ECONOMICS LETTERS Haug, J., Hens, T., Woehrmann, P. 2013; 118 (2): 310-313
An evolutionary explanation of the value premium puzzle JOURNAL OF EVOLUTIONARY ECONOMICS Hens, T., Lensberg, T., Schenk-Hoppe, K. R., Woehrmann, P. 2011; 21 (5): 803-815
Robust Reverse Engineering of Cross-Sectional Returns and Improved Portfolio Allocation Performance Using the CAPM JOURNAL OF PORTFOLIO MANAGEMENT Ni, X., Malevergne, Y., Sornette, D., Woehrmann, P. 2011; 37 (4): 76-85
Dynamic General Equilibrium and T-Period Fund Separation JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS Gerber, A., Hens, T., Woehrmann, P. 2010; 45 (2): 369-400
Look-Ahead Benchmark Bias in Portfolio Performance Evaluation JOURNAL OF PORTFOLIO MANAGEMENT Daniel, G., Sornette, D., Woehrmann, P. 2009; 36 (1): 121-130
Strategic asset allocation and market timing: A reinforcement learning approach Computational Economics Hens, T., Woehrmann, P. 2007; 30: 369381
Credit risk and sustainable debt: a model and estimations of why the Euro is stable in the long-run ECONOMIC MODELLING Semmler, W., Wohrmann, P. 2004; 21 (6): 1145-1160
Dynamic asset pricing models with nonparametric expectations Woehrmann, P. Tectum. 2002
Nonlinearities and chaos in the Austrian stock market Third International Conference on Forecasting Financial Markets Dockner, E. J., Woehrmann, P. 1996
Nonlinear Arbitrage Pricing Theory: An empirical study for the German stock market Conference of the German Society for Operations Research Woehrmann, P. 1994
Presentations: