Predicting investor behaviour in European bond markets through machine learning
Auf einen Blick
- Projektleiter/in : Dr. Martin Hillebrand, Prof. Dr. Peter Schwendner
- Projektteam : Dr. Marko Mravlak, Dr. Martin Schüle, Bastien Winant
- Projektvolumen : EUR 200'000
- Projektstatus : abgeschlossen
- Drittmittelgeber : EU und andere Internationale Programme (European Stability Mechanism ESM)
- Projektpartner : European Stability Mechanism ESM
- Kontaktperson : Peter Schwendner
Beschreibung
ICMA Quarterly Report (11.7.2019), p.23: Predicting investor behaviour in European bond markets through machine learning The quant team of ESM is developing, in cooperation with the Zurich University of Applied Sciences, a machine-learning based application to predict investor demand for syndicated bond issuances. A key reason for using machine learning algorithms is the ability to analyse complex and high-dimensional data sets with widely unknown structures, to capture complex dependencies and relations of variables and identify any kind of patterns in the data. The analysis comprises diverse datasets, including transaction-related data such as orderbooks, internal and external primary and secondary market data, including secondary market transactions reported by primary dealers. In addition, it comprises internal and external investor-specific data as well as macroeconomic data. The applied ML methodology is promising. First results show a prediction power of well above 50% of investor demand by investor type (such as banks, brokers, fund managers, pension funds or insurance). While results for individual investors were overall less accurate due to smaller data sets, qualitative information and behaviour patterns of specific investors could be detected. These results can help better to understand and address investor needs and consider this in the transaction planning and execution. This machine-learning application to predict investor demand is considered work in progress. Further improvements of data quality, inclusion of further data sources, and a refinement of the used ML algorithms are expected to improve forecasts substantially. However, a key limiting factor is the availability of data despite access to diverse data sources, including primary dealer reporting. The inclusion of further primary dealer data can play an important role. ML technology can help solve potential confidentiality issues. www.icmagroup.org/assets/documents/Regulatory/Quarterly_Reports/ICMA-Quarterly-Report-Third-Quarter-2019v2.pdf