Analysis of Large Financial Correlation Matrices

This master thesis explores financial markets as complex systems. Random Matrix Theory (RMT) and spectral analysis techniques are applied to correlation matrices in order to obtain noise-free correlations and insights on the hidden patterns of the market. The main goal is to group assets according to their financial sector based on the correlations between them by applying Clustering algorithms. The correlation matrices analised correspond to the S&P500 and IGBM stock indices. These techniques provide a clearer understanding of the market dynamics and offer valuable information for optimal portfolio management and investment strategies.

TFM Supervisor: Pere Colet

TFM Committee: Rosa Lopez, Damià Gomila and Raúl Toral

This online event can be followed at: https://us06web.zoom.us/j/83693989660?pwd=4go5LQTzrLLdyzAzHyJUVvoSZeFHvn.1