In recent years, machine learning (ML) techniques have gained popularity in the field of portfolio optimization. One ML-based method that has shown promise is the Nested Clustered Optimization Algorithm (NCO).
1. Correlation Clustering: In the first step, the assets in the portfolio are grouped into clusters based on their similarity in terms of historical returns, correlations, or other relevant factors. Clustering techniques like k-means clustering can be used for this purpose.
2. Intracluster Optimization: Within each cluster, a sub-portfolio is formed by selecting a subset of assets from that cluster. The sub-portfolio optimization aims to find the optimal weights or allocations for the selected assets within the cluster. This can be done using traditional optimization techniques like mean-variance optimization.
3. Intercluster Weights: Once the sub-portfolios for each cluster are determined, they are aggregated to form the final portfolio. The weights assigned to each sub-portfolio are based on the importance or weight of the cluster itself. This can be determined using various criteria such as the historical volatility or risk contribution of the cluster.
4. Optimization Refinement: The final portfolio is then subject to further optimization to fine-tune the allocation of assets and ensure adherence to specific constraints or objectives. This step can involve additional optimization techniques or adjustments to the portfolio weights.
The main advantage of Nested Cluster Optimization is that it allows for a more structured and systematic approach to portfolio optimization. By incorporating clustering, it takes into account the inherent similarities and diversities within the asset universe, leading to portfolios that are better diversified and more robust.
Furthermore, NCO can help address the computational challenges associated with large portfolios by reducing the number of assets involved in each optimization step. By grouping assets into clusters, the optimization problem becomes more manageable, and the computational complexity is reduced.
The Nested Clustered Optimization (NCO) technique mentioned above, which combines machine learning and portfolio optimization, is derived from the book “Machine Learning for Asset Managers” written by Marcos Lopez de Prado. If you are interested in gaining a deeper understanding of portfolio optimization using machine learning techniques, I highly recommend reading this book.
