Explainable Machine Learning for Regime-Based Asset Allocation

This paper explores an explainable AI model in the financial industry. Macroeconomic and market data serve as inputs of Hierarchical Clustering to distinguish among different economic regimes. Compared with traditional models such as Investment Clock, this method can adjust the classification standard in time according to recent market sentiment. The regime, therefore, can be interpreted by not only macro indicators but also investors' mood swings using Artificial Intelligence. When we compute the statistical characteristics of returns of each asset, we find that they can be well distinguished among regimes. This method can also identify the abnormally large wave of the stock market from 2015 to 2016 by separating it as an unusual regime, which cannot be realized by traditional methods. The clustering technique enables us to explain and understand the current market status and predict different assets' performances. Therefore, thanks to the superior interpretability of AI, the mean and variance of returns in each regime are estimated and viewed as viewpoints of the Black-Litterman asset allocation model to construct portfolios. To simulate the real situation, a dynamic backtesting method is used and asset weights change because of the rolling time windows. The results show that equipped with a simple timing strategy, the clustering technique can improve the results and yield excess returns. Some other machine learning techniques are also applied in an attempt to improve the model.