04.03.2021
| Inside Data Science |
Lars Hornuf on Data Science in Economics
In the interview series “Inside Data Science“ we introduce our members with their research activities and interests.
Today with Lars Hornuf, professor of Business Administration, especially Financial Services and Financial Technology at Faculty 7 – Business Studies and Economics at the University of Bremen.
What topics are you currently working on in your research?
I am currently researching topics related to new financial technologies, data protection and the future of work. My main focus is on crowdfunding and crowdworking. However, I am also investigating questions related to data philanthropy and the platform economy in general.
How important is data to your research?
In economics, research has always been empirical. Mathematical theoretical research has largely disappeared from the scene in recent years. The methodological gold standard is currently large-scale randomized controlled trials in the real world. When that is not possible, researchers usually turn to already existing data sets and study them using the methods of inferential statistics. Working with data is our daily business.
What role does data science play in your research: Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
Depending on the research question, existing methods are applied or new methods are developed in economics. However, the latter is rather the exception. Basic research takes place, especially because we deal with the behavior of people and this raises new fundamental questions in more and more new contexts. For example, the interaction between humans and machines in the workday or financing via algorithms was not a core research field in our discipline just a few years ago.
What kind of methods do you currently use for data analysis and which data science methods could be interesting for you in the future?
Depending on the research question, we mainly use classical methods of inferential statistics or classification methods. The latter can answer research questions or just be aids, for example, if data has been collected from the internet and still needs to be encoded.
What are your main challenges in dealing with data?
Especially for young researchers, it is important to not only blindly apply empirical methods, but also to understand which insights can be drawn from the results and which cannot. For all researchers in our discipline, it is increasingly important to ask the right research questions and not to simply run data sets with standard procedures in the respective statistical software.
And finally, what is your personal motivation for joining the Data Science Center?
I am excited about the scientific discourse. We have a lot of very bright minds in other disciplines and I am looking forward to learning from others and possibly developing joint research ideas.
Please note: The interview was originally given in German and translated into English by Lena Steinmann.
You can learn more about Lars Hornuf’s activities in his talk
„The Use of Inferential Statistics, Field Experiments, and Machine Learning in Corporate Finance“ in the Data Science Forum on 11.03.2021.
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04.03.2021 | Inside Data Science
Lars Hornuf on Data Science in Economics
In the interview series “Inside Data Science“ we introduce our members with their research activities and interests.
Today with Lars Hornuf, professor of Business Administration, especially Financial Services and Financial Technology at Faculty 7 – Business Studies and Economics at the University of Bremen.
What topics are you currently working on in your research?
I am currently researching topics related to new financial technologies, data protection and the future of work. My main focus is on crowdfunding and crowdworking. However, I am also investigating questions related to data philanthropy and the platform economy in general.
How important is data to your research?
In economics, research has always been empirical. Mathematical theoretical research has largely disappeared from the scene in recent years. The methodological gold standard is currently large-scale randomized controlled trials in the real world. When that is not possible, researchers usually turn to already existing data sets and study them using the methods of inferential statistics. Working with data is our daily business.
What role does data science play in your research: Do you see yourself more as a user, a method developer, a basic researcher, or perhaps something completely different?
Depending on the research question, existing methods are applied or new methods are developed in economics. However, the latter is rather the exception. Basic research takes place, especially because we deal with the behavior of people and this raises new fundamental questions in more and more new contexts. For example, the interaction between humans and machines in the workday or financing via algorithms was not a core research field in our discipline just a few years ago.
What kind of methods do you currently use for data analysis and which data science methods could be interesting for you in the future?
Depending on the research question, we mainly use classical methods of inferential statistics or classification methods. The latter can answer research questions or just be aids, for example, if data has been collected from the internet and still needs to be encoded.
What are your main challenges in dealing with data?
Especially for young researchers, it is important to not only blindly apply empirical methods, but also to understand which insights can be drawn from the results and which cannot. For all researchers in our discipline, it is increasingly important to ask the right research questions and not to simply run data sets with standard procedures in the respective statistical software.
And finally, what is your personal motivation for joining the Data Science Center?
I am excited about the scientific discourse. We have a lot of very bright minds in other disciplines and I am looking forward to learning from others and possibly developing joint research ideas.
Please note: The interview was originally given in German and translated into English by Lena Steinmann.
You can learn more about Lars Hornuf’s activities in his talk
„The Use of Inferential Statistics, Field Experiments, and Machine Learning in Corporate Finance“ in the Data Science Forum on 11.03.2021.
Interviewee:
Prof. Dr. Lars Hornuf
Professor of Business Administration, esp. Financial Services and Financial Technology
FB 07 – Business Studies and Economics
hornuf@uni-bremen.de
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