Assoc. Prof. Dr. Ronald Hochreiter

 Current Position:Assoc. Prof. Dr. Ronald Hochreiter
 Associate Professor of Finance

 Country of Origin:
 Austria

 E-Mail:
 ronald.hochreiter[at]webster.ac.at

 

 

 

Courses Taught at WVPU

Undergraduate Courses

  • BUSN 2750 Introduction to Statistics

Graduate Courses

  • BUSN 6110 Operations and Project Management
  • FINC 5830 Institutions and Financial Markets
  • FINC 6290 Financial Strategy: Mergers and Acquisitions

Education

  • Habilitation in Business Administration (WU Vienna University of Economics and Business, 2013)
  • Ph.D. (Dr.rer.soc.oec.) in Computational Management Science (University of Vienna, 2005)
  • M.Sc. (Mag.rer.soc.oec.) in Business Informatics (University of Vienna, 2001)

Background

Dr. Ronald Hochreiter is Associate Professor for Finance at the Department of Business and Management, Webster Vienna Private University. Before joining Webster in March 2019, he was a research assistant and post-doc researcher at the University of Vienna (Department of Statistics and Decision Support Systems) from 2001 to 2008 and Assistant Professor and Principal Investigator at the WU Vienna University of Economics and Business (Department of Finance, Accounting and Statistics) from 2009 to 2019.

Ronald has published more than 60 research papers and presented his research at more than 100 conferences. He is Associate Editor of the journal "Frontiers in Artificial Intelligence (AI in Finance)" and an Editorial Advisory Board member of the journal "Mendel - Soft Computing Journal". He actively reviews papers for various research journals and conferences.

Ronald is the principal investigator of national and international research projects, e.g. the FFG ASAP (Austrian Space Applications Programme) Project ReKlaSat 3D (Deep Learning from High-Resolution Satellite Imagery). At the EU level, he is a member of the executive board and Country Coordinator of the EU Horizon 2020 Project Fin-Tech (A FINancial supervision and TECHnology compliance training programme) as well as a member of the executive board of the EU Erasmus+ Project "ADA: Curriculum Development Advanced Analytics in Business" where he is acting as  the work package leader for quality control and monitoring. Previous research projects include the FFG Bridge project ALSOpt (Data Mining Analysis of Geo-Laserscan-Data, 2012-2014), an EU TEMPUS Project (Curriculum Development - Master Studies in Applied Statistics in Serbia,  2010-2013), and an Austrian National Bank Jubiläumsfonds project (Models for the valuation of complex credit portfolios using Coupled Markov Chains, 2007-2009).

He is President of the Academy of Data Science in Finance and Vice-President of the Austrian Society of Operations Research (Österreichische Gesellschaft für Operations Research, ÖGOR). He is an active member of the Austrian Statistical Society (Österreichische Statistische Gesellschaft, ÖSG) and the Association of Computing Machinery (ACM).

Ronald is one of the main organizers of the International Conference of Data Science in Finance with R (DSF-R) - a conference series which started in 2018. See http://dsf.academy/conference/ for more details.

He has taught many different courses at the University of Vienna, WU Vienna University of Economics and Business, WU Executive Academy, University of Bergamo, FHWien, FH Burgenland and the FH Campus Vienna. Some of his lectures include: Statistics, Data Science and Machine Learning, Quantitative Finance and Advanced Marketing Research Methods.

Research Interests

Ronald is currently interested in the following fields:

Quantitative Risk Management and Decision Making in Finance and Banking
Strategic Asset Allocation for Institutional Investors (Pension Funds, Endowments, ...)
Algorithmic Finance and Technical Trading of alternative asset classes
FinTech: RegTech, SupTech, LawTech and variants
Energy Economics using Decision Science and Optimization
Health Economics using Data Science and Big Data

Research Methods

Contemporary Statistics and Statistical Learning using R
Data Science (Machine Learning, Deep Learning, Statistical AI) using R and Python
Decision Science (Optimization under Uncertainty, Stochastic and Robust Programming)
Heuristic Optimization (including nature-inspired algorithms)


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