Prof. Dr. Dietmar Maringer
Dietmar Maringer is Professor of Computational Economics and Finance at the Faculty of Economics and Business (Wirtschaftswissenschaftliches Zentrum, WWZ) at the University of Basel, Switzerland. His research interests combine finance and computational methods from artificial intelligence and data analysis. These include risk management, portfolio optimization, algorithmic trading, high-frequency markets, financial networks, complex adaptive systems, machine learning, computational intelligence and simulation-based techniques. He studied computer science and business, and finance in Vienna, AT, and Cambridge, UK, and he earned his PostDoc qualification at the University of Erfurt’s econometrics department.
Prior to his current position, he was Director of Research and PhD Programmes at the Centre for Computational Finance and Economics Agents (University of Essex, UK). He has published numerous journal articles, books, and conference papers and has won several best-paper awards. Amongst other commitments, he chaired the Portfolio Optimization Task Force in IEEE’s Computational Economics and Finance Technical Committee 2008-2018, and is a frequent member of organization and program committees of international conferences.
- IEEE Computational Economics and Finance TC
- Computational Optimisation Methods in Statistics, Econometrics and Finance (COMISEF)
- European Research Consortium for Informatics and Mathematics (ERCIM)
- centre for innovative finance
- European Financial Management Association
- Manfred Gilli, Dietmar Maringer, and Enrico Schumann. Numerical Methods and Optimization in Finance. Academic Press, 2011.
- Dietmar Maringer. Portfolio Management with Heuristic Optimization. Advances in Computational Management Science. Springer, Boston, MA, 2005. .
- Christian Oesch and Dietmar Maringer. Portfolio Optimization under Market Impact Costs. In: 2013 IEEE Congress on Evolutionary Computation. 2013, pp. 1–7.
- Dietmar Maringer and Sebastian H. M. Deininger. Selecting and Estimating Interest Rate Models with Evolutionary Methods. In: Evolutionary Intelligence 9.4 (2016), pp. 137–151.
- Dietmar Maringer and Tikesh Ramtohul. Regime-Switching Recurrent Reinforcement Learning for Investment Decision Making. In: Computational Management Science 9.1 (2012), pp. 89–107.
- XiaoHua Chen and Dietmar Maringer. Detecting Time-Variation in Corporate Bond Index Returns: A Smooth Transition Regression Model. In: Journal of Banking & Finance 35.1 (2011), pp. 95–103. .
- Qingfu Zhang, Hui Li, Dietmar Maringer, and Edward Tsang. MOEA/D with NBI-style Tchebycheff Approach for Portfolio Management. In: IEEE Congress on Evolutionary Computation. 2010, pp. 1–8.
- Philip Saks and Dietmar Maringer. Statistical Arbitrage with Genetic Programming. In: Natural Computing in Computational Finance: Volume 2. Ed. by Anthony Brabazon and Michael O’Neill. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 9–29. .
- Kai-Tai Fang, Dietmar Maringer, Yu Tang, and Peter Winker. Lower Bounds and Stochastic Optimization Algorithms for Uniform Designs with Turee or Four Levels. In: Mathematics of Computation 75 (Apr. 2006), pp. 859–878
- Peter Winker and Dietmar Maringer. The Hidden Risks of Optimizing Bond Portfolios under VaR. In: Journal of Risk 9.4 (July 2005), pp. 1–19.
- Peter Winker and Dietmar Maringer. Optimal Lag Structure Selection in VEC-Models. In: New Directions in Macromodelling. Vol. 269. Contributions to Economic Analysis. Elsevier, 2004, pp. 213–234.
- Dietmar Maringer and Hans Kellerer. Optimization of Cardinality Constrained Portfolios with a Hybrid Local Search Algorithm. In: OR Spectrum 25.4 (2003), pp. 481–495.
further publications can be found on the research page.
Prof. Dr. Dietmar Maringer
Office 5.35 Faculty of Business and Economics
Computational Economics and Finance
Peter Merian-Weg 6 4002 Basel
Phone: +41 61 207 32 52
Office Hours: Please contact me via email to arrange a meeting.
- Publikationen (Forschungsdatenbank der Universität)
- Projekte (Forschungsdatenbank der Universität)
- Aktuelle Lehrveranstaltungen