Training Session: Data Science Applications in Quantitative Finance

Abstract: In this training session I will give an introduction to quantitative finance applications for data scientists [with no prior knowledge in the field]. I will start by discussing a few fundamental ideas such as the efficient market hypothesis and the capital asset pricing model. I will also introduce some concepts on investment strategies including portfolio theory and smart beta investing. Many such strategies are based on fundamental factors revealed by academic research, such as value or profitability, which can yield excess returns when suitably applied. There is currently a lot of interest to use alternative data sets and machine learning tools to uncover further factors, making the field exciting for data scientists.

Throughout the training session we will work on some simplified hands-on examples to illustrate the main ideas.

We will discuss the setup and subtleties of backtesting for investment strategies and define relevant metrics. Typically, a major challenge is to avoid overfitting, which we will address as well.

Although the focus of the session is investment strategies, I will also illustrate a few other machine learning applications in finance from my experience.

Participants don’t need to have prior experience in finance. However, familiarity with python and the numpy, pandas, scikit-learn ecosystem would be very beneficial.

Bio: Johannes is Data Analytics Associate Director at IHS Markit, a global information and intelligence provider. He technically manages multiple data science projects across various business lines including finance, the automotive industry and the energy sector. He has a keen interest in the full data science spectrum including mathematical statistics, machine learning, databases, distributed computing, and dynamic visualizations.

He holds a PhD in theoretical condensed matter physics from Imperial College and has been active in quantitative research for more than 10 years including research positions at Harvard University and the Max-Planck Institute in Germany. He has disseminated his research in more than 30 peer reviewed publications and over 50 talks.

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