A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets

TL;DR

This paper introduces a hybrid machine learning framework combining PCA, k-means clustering, and classification to detect regime switches in US financial markets, aiming to improve trading strategies.

Contribution

It presents a novel combination of PCA and clustering for regime detection, enhancing market analysis and trading decision accuracy.

Findings

Effective regime detection using PCA and k-means clustering.

Improved trading strategy performance based on detected regimes.

Demonstrated framework's efficacy with empirical trading results.

Abstract

Financial markets are of much interest to researchers due to their dynamic and stochastic nature. With their relations to world populations, global economies and asset valuations, understanding, identifying and forecasting trends and regimes are highly important. Attempts have been made to forecast market trends by employing machine learning methodologies, while statistical techniques have been the primary methods used in developing market regime switching models used for trading and hedging. In this paper we present a novel framework for the detection of regime switches within the US financial markets. Principal component analysis is applied for dimensionality reduction and the k-means algorithm is used as a clustering technique. Using a combination of cluster analysis and classification, we identify regimes in financial markets based on publicly available economic data. We display the efficacy of the framework by constructing and assessing the performance of two trading strategies based on detected regimes.