Combining Analytical and Machine Learning Methods in Computational Finance
Johannes Himmelbauer
In his PhD work, Johannes Himmelbauer deals with the theoretical foundations and practical implementations of Combining Analytical and Machine Learning Methods in Computational Finance. This PhD project, which mainly concerns SCCH’s KBT Area, is supervised by SCCH’s scientific partner Prof. Dr. Erich Peter Klement (Johannes Kepler University Linz). The main goal of the proposed work is to use data mining related methods to improve traditional analytical models that have been used by most financial analysts for estimating prices of financial products. Specifically, the work seeks to combine the advantages of analytical and data-driven approaches in a way that the resulting methods and tools provide significantly better support for the financial analyst’s decisions. Possible improvements involve properties such as accuracy, interpretability, confidence and applicability of models. The ultimate long-term goal of the PhD project is to develop a set of tools and strategies for creating such models together with a well-founded theory.
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