Data Science in Finance
The interdisciplinary Working Group on Data Science in Finance is conducting research on data science applications in the financial industry.
Its objective is to develop solutions for the challenges in modern finance, i.e., by utilizing the power of computationally intense algorithms and statistical analyses. Research projects may be supported by corporate partners. The focus of the working group is on i) quantitative asset management, ii) text mining & sentiment analysis, and iii) credit risk analytics.
PD Dr. Ronald Hochreiter
Univ.Prof. Dr. Kurt Hornik
Univ.Prof. Dr. Stefan Pichler
Research Partners & Collaborators
Dr. Alexander Eisl
Dr. Stephan Gasser (IMC Krems)
Rainer Hirk, MSc
Dr. Gregor Kastner
Stephan Kranner, MSc
Christian Ochs, MSc
Dipl.-Ing. Florian Schwendinger
Laura Vana, MSc
Dr. Karl Weinmayer (MODUL University)
Quantitative Asset Management
High Frequency European Sovereign Bond Markets: Research project on the liquidity and market conditions of European sovereign bond markets, based on trade and order book data on a tick-by-tick basis. Research Partner is WU Institute for Finance, Banking and Insurance and NYU Stern School of Business.
Cryptocurrency Portfolio Diversification: Research project on the impact of cryptocurrency investments on the composition of traditional portfolios and diversification effects with respect to multi-dimensional portfolio risk measures.
Giga-Asset Alpha Portfolio Optimizer: A contemporary portfolio optimization tool for a huge asset universe (1000+) has been implemented in cooperation with GAM Investments. Besides being able to handle lots of assets a large set of real-world constraints can be added.
Pension Fund Asset Liability Management: Together with OePAG (now: VALIDA) an Award-winning Asset Liability Management system has been implemented. This system has been awarded with the IPE Europe Pension Fund Award.
Drawdown Optimization: Together with Union Invest a drawdown portfolio optimization tool has been designed. Latest research has been bundled with industry knowledge to create a portfolio optimizer which can be used for both strategic and tactical asset management.
Stochastic TTR-based Portfolio Optimization: Together with Nereas Asset Management SAM a stochastic portfolio optimizer based on TTR time-series scenarios has been developed. This approach allows for a complete rethinking of the application of stochastic portfolio optimization and provides promising results for various regimes.
Credit Risk Analytics
Text Mining & Sentiment Analysis
Sentiment-based Portfolio Optimization: Due to an ongoing cooperation with PsychSignal various trading tools based on their data have been implemented see e.g. this paper.