  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.
