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Contribution Details

Type Master Thesis
Scope Discipline-based scholarship
Title Stock market volatility: Identification of risk drivers and forecasting using random forest
Organization Unit
Authors
  • Fabian Smits
Supervisors
  • Boris Wälchli
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Number of Pages 43
Date November 2017
Abstract Text Random forest is a machine learning method which can be used for regression and classification. Due to its ability to handle large data setswith highly nonlinear dependencies, random forest is a powerful prediction tool. Furthermore, despite being a non-parametric approach, random forest allows for inference regarding the importance of input variables.
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