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

arXiv: https://arxiv.org/abs/2107.05535 · HTML: https://mlsanity.com/paper/2107.05535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing](https://mlsanity.com/paper/1902.10849/) (2019)

## Abstract (arXiv)

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.