Machine Learning in Financial Economics: An Investment Perspective

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Type and Duration

PhD-Thesis, since September 2020


Chair in Finance

Main Research

Wealth Management


With increasing computing power, advanced algorithms and growing data resources, machine learning methods are increasingly applied in various scientific domains. Deviating from other research fields, economic as well as financial data suffer from low signal-to-noise ratios, which significantly hinders the meaningful application of these advanced methods. This dissertation deals with the development and practical application of noise-robust algorithms in the research domain of financial economics. In particular, we focus on current problems in asset pricing, portfolio management and international finance research. The combination of both research areas (financial economics and data science) enables the discovery and practical exploitation of learnable and generalizable patterns in large, noisy data sets.