  Financial markets tend to switch between various market regimes over time,
making stationarity-based models unsustainable. We construct a regime-switching
model independent of asset classes for risk-adjusted return predictions based
on hidden Markov models. This framework can distinguish between market regimes
in a wide range of financial markets such as the commodity, currency, stock,
and fixed income market. The proposed method employs sticky features that
directly affect the regime stickiness and thereby changing turnover levels. An
investigation of our metric for risk-adjusted return predictions is conducted
by analyzing daily financial market changes for almost twenty years. Empirical
demonstrations of out-of-sample observations obtain an accurate detection of
bull, bear, and high volatility periods, improving risk-adjusted returns while
keeping a preferable turnover level.
