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Financial markets are of much interest to researchers due to their dynamicand stochastic nature. With their relations to world populations, globaleconomies and asset valuations, understanding, identifying and forecastingtrends and regimes are highly important. Attempts have been made to forecastmarket trends by employing machine learning methodologies, while statisticaltechniques have been the primary methods used in developing market regimeswitching models used for trading and hedging. In this paper we present a novelframework for the detection of regime switches within the US financial markets.Principal component analysis is applied for dimensionality reduction and thek-means algorithm is used as a clustering technique. Using a combination ofcluster analysis and classification, we identify regimes in financial marketsbased on publicly available economic data. We display the efficacy of theframework by constructing and assessing the performance of two tradingstrategies based on detected regimes.

A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets