Predicting Risk-adjusted Returns using an Asset Independent Regime-switching Model

TL;DR

This paper introduces a regime-switching model based on hidden Markov models that predicts risk-adjusted returns across various financial markets, effectively identifying different market regimes and improving performance.

Contribution

The paper presents a novel asset-independent regime-switching model utilizing sticky features and hidden Markov models for accurate market regime detection.

Findings

Accurately detects bull, bear, and high volatility periods.

Improves risk-adjusted returns in empirical tests.

Maintains preferable turnover levels.

Abstract

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.