Marko Kangrga has been working to combine fundamental drivers with quantitative and statistical learning methods and to apply this framework to large datasets in order to identify potential alpha opportunities from a broad universe of securities. Examples range from simple web scraping and statistical analysis of macro/fundamental drivers to language processing of financial statements and transcripts, in an effort to screen for investment ideas. Python, Quantitative Finance, Machine Learning, Factor Risk Modeling, Fundamental Modeling, Derivatives Trading, Global Macro, FX, Rates, Distressed, Value Investing, scikit-learn, scipy, VBA, C++, R, MATLAB, CFA L2
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