Business and Management Research News
B&M Welcomes Dr. Hochreiter and His Passion for Political Science & Quantitative Methods
The business and management department welcomed Dr. Ronald Hochreiter, Associate Professor of Finance, to Webster Vienna in early March. Dr. Hochreiter completed his Habilitation in Business Administration at the Vienna University of Economics & Business five years ago, but earned his PhD in Business Informatics from the University of Vienna in 2005. Dr. Hochreiter has written extensively on a variety of research topics related to his studies, from finance to data mining, and over the years has taught courses on data analytics, quantitative and qualitative methods, hedge funds, and management.
We sat down with Dr. Hochreiter recently to welcome him to Webster and to discuss his background in academia, his recent research pursuits, and his plans for the future at Webster.
WVPU: First and foremost, welcome to Webster! We’re really excited that you’re here. You’ve been involved in academia for almost your entire career. Did you grow up wanting to be a professor and professional researcher? If not, how did it happen?
Dr. Hochreiter: I am really happy to join Webster and am looking forward to continuing research and teaching in the fields of algorithmic/computational/quantitative finance, data science and decision science here. Interestingly, when I was younger, I actually never planned a career in academia but rather wanted to work for the European Union as I love the concept of a unified Europe. I studied political science in addition to business informatics and my initial plan was to complete my MA in political science and do my PhD in this area right after completing my MSc in business informatics; however, just a few days before I actually graduated my BI master’s thesis advisor offered me a PhD position at the Department of Statistics.
Initially, I intended to decline the offer, but following a week of careful consideration I accepted. During this period of reflection I realized that the field of operations research and computational management science, i.e. the quantification of decision management under uncertainty, is applicable to many different areas -including political decision taking. My research is biased by this in the direction of hands-on approaches to difficult problems i.e. I love translating complex quantitative methods for a variety of applications.
WVPU: You’ve taught dozens of courses over the years on topics relating to your expertise and academic interests. Is there any one course which stands out as especially near and dear to your heart? Which course at Webster are you most looking forward to teaching?
Dr. Hochreiter: The course I really love is currently entitled “Quantitative Methods in Finance” where I basically teach how to become a Quant in the financial industry – both in hedge funds as well as investment banks. It is a very hands-on lecture that has been developed by me since 2006.
I am also in the process of creating a textbook out of this content which should be ready within the next six to nine months. I am definitely looking forward to integrating many parts of this lecture in two of my upcoming courses at Webster, i.e. "FINC 6290 Mergers & Acquisitions" as well as "FINC 5830 Institutions and Financial Markets".
WVPU: Research has been a salient component to your career. To the extent that you can share it with us, which topics or projects are currently taking up your time? What can we expect to see from you in the near future?
Dr. Hochreiter: I will basically streamline my research to applying machine learning and artificial intelligence methods to various applications in business, finance and economics. It is not easy to get these methods right but if you do get them right you can create a plethora of new insights into various problems.
It is also important to apply these methods with a certain deep knowledge of the respective area under consideration, which in my view was all too often neglected over the last few years when the main idea seemed to be that artificial intelligence would solve all our problems without having to think about the problem area. Rather, quite the opposite is true – if you want to get a useful solution computed by AI methods you really need to understand the problem in detail. This is also the case why so many companies have been frustrated by AI solutions because those simply do not solve problems out of the box.
WVPU: in 2015 you co-authored a paper on improving election night forecasts. What was your contribution to that paper and, perhaps more importantly, have any news networks called you and your co-author to ask for help?
Dr. Hochreiter: This was the pet project of me and my research assistant at WU Vienna, Christoph Waldhauser, who did his MA in Political Science but is one of the best Data Scientists in Austria I know. Unfortunately, we never had enough time to focus on this branch of research besides creating this paper. However, the methods published in the paper have actually been used to improve certain election night forecasts in Austria, but due to the new legislation which prohibits the publication of partial results during the Election Day the methods are not applicable at the moment.
I am currently using the example of Fake News generation by Cambridge Analytica (i.e. the Facebook scandal) to show how psychometric data can and should be integrated into contemporary AI methods to control voting decisions of humans – of course basically to come up with useful counteractions against Fake News. So, my interest in applying complex algorithms to political problems has not lead to new publications yet, but is still alive.
WVPU: What advice do you have to young researchers, particularly in the field of finance?
Dr. Hochreiter: It depends on whether you are interested in pure finance or in my personally preferred field of algorithmic finance. If you are into pure finance, the best strategy is to build a personal network and visit all the important finance conferences which are mainly organized in the US. If you are into algorithmic and quantitative finance, you’ll need to get your hands dirty by applying various methods to different types of data sets and accomplish to fulfill the painstakingly tedious task to find alpha in the market as opposed to doing beautiful mathematical finance proofs. There’s plenty of opportunity for different kinds of research.