# A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing

## TL;DR

This paper introduces a dynamic asset allocation system using Feature Saliency Hidden Markov Models to enhance smart beta investing, significantly improving risk-adjusted returns and regime detection accuracy in real-world asset portfolios.

## Contribution

It presents a novel feature selection approach with FSHMM for better regime identification and demonstrates substantial performance improvements over traditional methods.

## Key findings

- Up to 50% annual excess returns over the market.
- Feature Saliency HMM improves regime detection accuracy.
- Systematic trading with real assets shows significant risk-adjusted return gains.

## Abstract

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.