Dynamic Orthogonal Lower Dimensional Projections for Improving Hierarchical Risk Allocation and Out of Sample Portfolio Returns

It is challenging to consistently outperform benchmark indices and provide attractive long-run risk-return ratios under different economic scenarios. In this work, newly developed machine learning techniques are proposed to build robust portfolios that potentially can perform well under different stock market dynamics. Hierarchical clustering is used to assign stocks weights by minimizing the overall portfolio volatility (i.e. risk parity). The resulting portfolios present low stock correlations, and the invested stocks tend to neutralize each others potential downside expectation, yielding more reliable out of sample results. A new scheme is proposed to improve the hierarchical allocation method by including a dynamic orthogonal projections method, which tends to reduce outliers and noise, while obtaining subspaces that potentially can discriminate the individual stocks contributions. The preliminary experimental results suggest that attractive portfolios can be obtained, improving on the traditional market benchmarks, while maintaining a high degree of diversification and lower volatility. These experiments focus on the out-of-sample backtesting performance of the proposed scheme on a developing stock market, and on its comparison with traditional market indices to evaluate its potential.