Stefan, what data is available in the financial sector already and how can it be used?
The data ranges from customer information and transaction data to market data, macroeconomic indicators and social media. The integration and analysis of this data can help banks and financial service providers to gain deeper insights into customer behaviour, create data-based predictions, better manage risks and identify market opportunities more efficiently.
Much hope is pinned on the use of Artificial Intelligence (AI). What specific areas of application are there in asset management?
Hundreds of key figures are available for each company, which can influence stock market prices. By skilfully processing these data volumes, machine learning models can independently recognise patterns and uncover hidden correlations between company key figures and future returns. In this way, daily forecasts can be derived for the price development of shares worldwide. These predictions can then be implemented in funds and mandates using a sophisticated portfolio construction process. Zürcher Kantonalbank Asset Management has had a tried-and-tested machine learning algorithm for stock selection for several years and uses it successfully.
Large Language Models (LLM) are particularly in the spotlight. Where are they used in asset management?
One field of application is the automated processing of news, company reports and conference calls. Sentiment indicators can be derived for each company on a daily basis. These show whether news reflects a positive or negative mood. These sentiment indicators can be used as an additional source of information to make well-founded investment decisions. LLM also support portfolio managers in their favourite discipline, namely picking out price-relevant information from the thicket of data. Specially trained GPT (Generative Pre-trained Transformers) can help to summarise analyst reports and extract the relevant information.
To what extent is the profession of portfolio managers and analysts at risk?
It will still be several years before artificial intelligence can emulate human intelligence in all its facets, such as reasoning, visual perception or motor skills. Nevertheless, AI already offers considerable advantages in some areas. For example, it can analyse huge amounts of data in a very short time and independently recognise patterns that often remain invisible to humans. The key question is: how can we use AI skillfully to support our work, make better decisions and increase our efficiency?