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The financial crisis of 2008 generated interest in more transparent,rules-based strategies for portfolio construction, with Smart beta strategiesemerging as a trend among institutional investors. While they perform well inthe long run, these strategies often suffer from severe short-term drawdown(peak-to-trough decline) with fluctuating performance across cycles. To addresscyclicality and underperformance, we build a dynamic asset allocation systemusing Hidden Markov Models (HMMs). We test our system across multiplecombinations of smart beta strategies and the resulting portfolios show animprovement in risk-adjusted returns, especially on more return orientedportfolios (up to 50$\%$ in excess of market annually). In addition, we proposea novel smart beta allocation system based on the Feature Saliency HMM (FSHMM)algorithm that performs feature selection simultaneously with the training ofthe HMM, to improve regime identification. We evaluate our systematic tradingsystem with real life assets using MSCI indices; further, the results (up to60$\%$ in excess of market annually) show model performance improvement withrespect to portfolios built using full feature HMMs.

A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing