Fast Super-Paramagnetic Clustering
This work addresses the challenge of real-time financial market classification for economists and data analysts, but it is incremental as it modifies an existing clustering method.
The authors tackled the problem of clustering stock market interactions by mapping them to spin models and comparing a simulated annealing-based Super-Paramagnetic Clustering (SPC) algorithm with a modified maximum likelihood version called Fast SPC (f-SPC). They applied these methods to 447 NYSE stocks over 1249 days, recovering clusters that approximate economic sectors and revealing mixed clusters that highlight the adaptive nature of financial markets, with f-SPC showing better performance for high-dimensional datasets.
We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach we call Fast Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a data-set of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed ones whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data analytics. A key result is that we show that f-SPC maximum likelihood solutions converge to ones found within the Super-Paramagnetic Phase where the entropy is maximum, and those solutions are qualitatively better for high dimensionality data-sets.