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Master's Thesis from the year 2017 in the subject Business economics - Business Management, Corporate Governance, grade: 1,3, Zeppelin University Friedrichshafen (Chair of Econometrics and Finance), language: English, abstract: This Master Thesis introduces basic concepts and methods of Machine Learning as well as applying them on a virtual buy- or sell algorithm enabled by an accurately predicting classifier. In the first section, the historical development of Machine Learning is presented. Consequently, an array of classifiers is described in detail. The third section results in a python-based Machine Learning application of seven classifiers, a cross validation and in initializing the final valuation algorithm. The optimal classifier predicts following day Open Prizes for the S&P 500 with 72% accuracy outperforming market return by nearly 150% within the span of one month.