Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model
This work addresses the challenge of regime-switching in financial markets for investors and analysts, though it appears incremental as it builds on existing hidden Markov model frameworks.
The paper tackled the problem of predicting risk-adjusted returns in non-stationary financial markets by developing an asset-independent regime-switching model based on hidden Markov models, which improved risk-adjusted returns with accurate detection of market regimes like bull, bear, and high volatility periods while maintaining preferable turnover levels.
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.