After completing my apprenticeship as a banker and my bachelor’s degree in banking and finance, I decided to continue my academic adventure by starting the Master’s program in Applied Data Science at Frankfurt School of Finance and Management in 2019. Besides studying, I have been working for the Evangelische Bank Group for over 7 years and have been working in portfolio management since 2017. I have been responsible for the company’s global equity strategy since 2018 and currently manage around EUR 350 million on my own responsibility.
Financial markets create millions of data points every day. Countless messages fly via tickers every second and influence the prices of stocks. In addition, any of the global markets is open almost every hour of the day. The resulting task seems unsolvable for a portfolio manager. Right, unless they rely on automation and quantitative methods. At this point, the Master in Applied Data Science complements my previous academic and professional career. As a self-taught programmer, on the one hand I can lay a scientific foundation under my existing know-how. Additionally, knowledge in relevant areas can be deepened.
To give the reader a better idea of the diverse use cases, I would like to give some insights in our basic investment process and focus on aspects where Applied Data Science really matters. After defining my investable universe, the starting point of my investment process is data gathering. On the one hand, a lot of data can be accessed easily using data providers. As you can imagine, accessing the easily available data will not give you that much of an edge to your competitors. On the other hand, millions of other data points can be used in financial markets – the “alternative” data. Think about the prominent topic of sustainability data, where reporting standards and transparency is currently growing. For this kind of data, it is quite useful to be able to screen millions of pages of company reports and search for specific sustainability-related information. Furthermore, one could think about news flow. Especially, newly published information decreases a lot in value in a very small amount of time. Given my own speed-reading ability, I would be lost trying to work manually through all those billions of data points generated. As you can see, making data useful by using e.g. natural language processing is one of the big use cases in real-world environments. Additionally, given the amount of data one collects, he/she is directly confronted with the problem of storing huge amounts of data and being able to process them fast. Especially referring to this aspect, the Applied Data Science program widened my horizon a lot. When one is done with gathering all the data, the hard work begins. Portfolio managers are looking for patterns, fundamental relationships and alpha-generating data features. That sounds like a great task for artificial intelligence, doesn’t it?
Data science is becoming a real hype. Detached from the (often too euphoric) media reporting, I believe that a deep understanding of data, artificial intelligence and the associated mathematics is always an enrichment for one’s own future. This is especially true in an increasingly digital world. In addition, the course is strongly focused on teaching methodological skills. Contrary to the classic idea of just learning something for exams, acquired methodological competencies can be generalized without any problems and thus expand your own toolbox for solving future problems.
Frankfurt School has decades of know-how in the education of subsequent generations. In addition, the Frankfurt School offer can be combined excellently with my own workload. This point is particularly important to me personally, because I believe that real progress can largely be achieved by successfully combining theory and practice. Furthermore, Frankfurt School is an excellent place to strengthen your own network. Of particular advantage is the high diversity of my course, which often enables new perspectives on old problems. After working with people with mostly financial backgrounds, I enjoy to study and cooperate with e.g. neuroscientists or mathematicians.