Economic cycles and portfolio management.

Summary This thesis seeks to link business cycles and portfolio management. The first chapter builds a theoretical framework between business cycles and risk premia. It highlights the importance of turning points in the growth cycle, better known as the output gap. The next two chapters aim to detect these turning points in real time. The first approach focuses on a simple and easily understood non-parametric machine learning method called adaptive vector quantization. The second approach uses more complex machine learning methods, known as ensemble learning: random forests and boosting. Both approaches allow us to create efficient investment strategies in real time. Finally, the last chapter develops an asset allocation method based on different hierarchical clustering algorithms. Empirical results demonstrate the interest of this attempt: the created portfolios are robust, diversified and lucrative.