  The financial crisis of 2008 generated interest in more transparent,
rules-based strategies for portfolio construction, with Smart beta strategies
emerging as a trend among institutional investors. While they perform well in
the long run, these strategies often suffer from severe short-term drawdown
(peak-to-trough decline) with fluctuating performance across cycles. To address
cyclicality and underperformance, we build a dynamic asset allocation system
using Hidden Markov Models (HMMs). We test our system across multiple
combinations of smart beta strategies and the resulting portfolios show an
improvement in risk-adjusted returns, especially on more return oriented
portfolios (up to 50$\%$ in excess of market annually). In addition, we propose
a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM)
algorithm that performs feature selection simultaneously with the training of
the HMM, to improve regime identification. We evaluate our systematic trading
system with real life assets using MSCI indices; further, the results (up to
60$\%$ in excess of market annually) show model performance improvement with
respect to portfolios built using full feature HMMs.
