Boom and Bust — Is XGBoost the new Nostradamus of financial bubbles?

Abstract

Ever since the first stock exchange opened in the 16th century, investors have been trying to predict price developments and make profits from them. However, common statistical time series models have little empirical evidence of predictive power for bubbles and crashes. Therefore, we implemented a relatively new machine learning algorithm, namely XGBoost, to predict the daily adjusted closing prices of a single stock based on the last N days of data. For our implementation, we used the programming language Python including standardized packages like Numpy, Pandas, Matplotlib, Seaborn, Sklearn and XGBoost. Finally, we were able to successfully replicate a common stock price prediction algorithm and achieve good results on a selected yahoo finance dataset.

The team

Philipp Fukas Artificial Intelligence

Roles inside the team

Philipp Fukas mainly dealt with the technical setup of the GitHub repository, the formulation of the blogpost as well as the technical implementation and evaluation of the XGBoost model.

Mentor

Maximilian Maiberger

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