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AI for Quants – Accelerated, explainable AI/ML in capital markets
Seminar by Jochen Papenbrock
13th July 2021
Dr. Jochen Papenbrock returns to the LQG to discuss the implications and benefits of work completed with a team at Munich Re Markets on robust accelerated portfolio construction and market data generators which was published in the spring edition of the Journal of Financial Data Science – accessible here
Business, regulatory and performance requirements demand robust, explainable, trustworthy, sustainable, efficient, transparent, and larger AI/ML models. Jochen will illuminate the current era of model building, referencing algorithmic developments including risk management techniques.
Jochen will also highlight how to access freely available open source software so you can harness potentially massive improvements in processing times while retaining IT cost control and critical business flexibility – in short how to access “your own” super-computer – or more accurately your own accelerated computing platform – without having to learn a totally new language and without paying the earth for the privilege!
Jaeger, M, S. Krügel, D. Marinelli, J. Papenbrock, P. Schwendner. 2021 “Interpretable Machine Learning for Diversified Portfolio Construction“
Lopez de Prado, 2016. “Building Diversified Portfolios That Outperform Out of Sample.” The Journal of Portfolio Management 42 (4): 59–69.
Lundberg, S., and S.-I. Lee. “A Unified Approach to Interpreting Model Predictions.” In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, pp. 4765–4774. Curran Associates, 2017,
Lundberg, S. M., G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee. 2020. “From Local Explanations to Global Understanding with Explainable AI for Trees.” Nature Machine Intelligence 2 (1): 56–67.
Dr. Jochen Papenbrock
Based in Frankfurt, Germany, Jochen has spent the last 15 years in various roles on the topic of AI in Financial Services, as a thought leader, implementer, researcher and ecosystem shaper.
He is a financial data scientist and received his degree and PhD from the Karlsruhe Institute of Technology (KIT). As a consultant, entrepreneur and researcher he worked with well-known asset managers, banks, insurance companies and central banks. He is a manager at NVIDIA and works with partners, communities, and developers in financial services in Europe and also in some global teams.
Jochen is board member of the EU Horizon 2020 project ‘FIN-TECH’, specialty chief co-editor (Co) at Frontiers ‘AI in Finance’, and project leader in GAIA-X. He has published on AI and quantitative finance in Journal of Financial Data Science, Journal of Investment Strategies, Financial Markets and Portfolio Management, Quantitative Finance, Applied Financial Economics, Frontiers Journal of AI in Finance and Journal of Network Theory in Finance.