Improving Portfolio Performance Using a Novel Method for Predicting Financial Regimes

TL;DR

This paper introduces a novel regime prediction model that significantly improves portfolio performance across multiple asset classes by utilizing a contrarian trading strategy and ex ante regime detection, outperforming traditional models and benchmarks.

Contribution

The paper presents a new regime prediction model that outperforms existing methods like hidden Markov models and benchmarks, incorporating a contrarian strategy and short positions for better returns.

Findings

Model achieves over four times higher returns than benchmarks.

Outperforms hidden Markov models in regime prediction accuracy.

Effective across equities, commodities, and foreign exchange.

Abstract

This work extends a previous work in regime detection, which allowed trading positions to be profitably adjusted when a new regime was detected, to ex ante prediction of regimes, leading to substantial performance improvements over the earlier model, over all three asset classes considered (equities, commodities, and foreign exchange), over a test period of four years. The proposed new model is also benchmarked over this same period against a hidden Markov model, the most popular current model for financial regime prediction, and against an appropriate index benchmark for each asset class, in the case of the commodities model having a test period cost-adjusted cumulative return over four times higher than that expected from the index. Notably, the proposed model makes use of a contrarian trading strategy, not uncommon in the financial industry but relatively unexplored in machine learning models. The model also makes use of frequent short positions, something not always desirable to investors due to issues of both financial risk and ethics; however, it is discussed how further work could remove this reliance on shorting and allow the construction of a long-only version of the model.