Modern Risk Management in times of Machine Learning

Dieser Inhalt ist für professionelle Anleger im Sinne der MiFID-Richtlinie bestimmt.

by Dr. Denisa Čumova, FRM, Head of Portfolio Management and Quantitative Research & Dr. Philipp J. Kremer, CAIA, Senior Portfolio Manager & Quant Researcher

ARTIFICIAL INTELLIGENCE METHODS IN ASSET MANAGEMENT 

The launch of ChatGPT by Open AI at the beginning of the year highlighted the revolutionary character of artificial intelligence methods. While artificial intelligence was already present in translation services, digital assistants in customer chats, as well as image and speech recognition in medical science, ChatGPT has further revealed the variety of applications of artificial intelligence in our everyday lives. 

In the asset management industry, adopting Machine learning techniques can contribute to optimizing the investment process and can ameliorate risk management. Machine learning (ML), as a subfield of Artificial intelligence (AI), refers to algorithms and models that can learn complex patterns from input data in order to make predictions. Hence, ML methods can provide new insight on how to capture and evaluate capital market drivers. 

ML algorithms can model complex capital market relationships more precisely and can respond more dynamically to changes in market environments than traditional quant models since the structure of ML models is derived from input data. However, as ML methods are demanding with regard to data and computing power, they have for a long time not lived up to their full potential, despite the fact that the first models date back to the 1980s.

For the asset management industry, the solution to these limitations was resorting to a linear world, utilizing economic models such as the Capital Asset Pricing Model or the Arbitrage Pricing Theory, where the return and risk of an asset depends linearly on a set of factors. While the interpretability of such models is simple, economic reality reveals that relationships among variables are inherently non-linear in nature and that many non-linear economic relationships are not properly captured by traditional econometric models. Figure 1., where monthly US equity returns are plotted against US breakeven inflation rates, illustrates such a relationship. Clearly, a non-linear model is superior in capturing the underlying relationship.