Dieter Hendricks

Senior Machine Learning Scientist

Dieter's research interests focus on applied machine learning methods in quantitative finance. Specifically, exploring foundational approaches for data-driven inference, given the nuances of market microstructure, financial economics and system complexity.



Related Events

10th Anniversary Oxford-Man Institute Annual Workshop

Working Paper

Hendricks, D. and Wilcox, D (2014). A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution. IEEE CIFEr.
Hendricks, D (2016). Using real-time cluster configurations of streaming asynchronous features as online state descriptors in financial markets.
Hendricks, D. and Roberts, S.J. (2017). Optimal client recommendation for market makers in illiquid financial products.

Published Research

Harvey, M., Hendricks, D., Gebbie, T. and Wilcox, D (2017). Deviations in expected price impact for small transaction volume under fee restructuring. Physica A. 471. 416-426.
Hendricks, D., Gebbie,, T. and Wilcox, D (2016). Detecting intraday financial market states using temporal clustering. Quantitative Finance. 16(11). 1657-1678.
Hendricks, D., Gebbie, T. and Wilcox, D (2016). High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm. South African Journal of Science. 112(1/2). 9.
Franke, B., Plante, J.F., Roscher, R., Lee, A., Smyth, C., Hatefi, A., Chen, F., Gil, E., Schwing, A. and Hendricks, D (2016). Statistical inference, learning and models in big data. International Statistical Review. 84(3). 371-389.
Hendricks, D and Roberts, S (2017). Optimal client recommendation for market makers in illiquid financial products. Proceedings of ECML. 2017. ArXiv 1704.08488.
Hendricks, D Cobb, A Everett, R Downing, J and Roberts, S (2017). Inferring agent objectives at different scales of a complex adaptive system. NIPS workshop: Learning in the Presence of Strategic Behavior. 2017. ..