KEYWORDS: Medicine, Distributed interactive simulations, Biological detection systems, Telecommunications, Statistical analysis, Nanoelectromechanical systems, Computed tomography, Global Positioning System, Network on a chip, Geographic information systems
We review some methods recently used in the literature to detect the existence of a certain degree of common
behavior of stock returns belonging to the same economic sector. Specifically, we discuss methods based on
random matrix theory and hierarchical clustering techniques. We apply these methods to a set of stocks traded
at the New York Stock Exchange. The investigated time series are recorded at a daily time horizon. All the
considered methods are able to detect economic information and the presence of clusters characterized by the
economic sector of stocks. However, different methodologies provide different information about the considered
set. Our comparative analysis suggests that the application of just a single method could not be able to extract
all the economic information present in the correlation coefficient matrix of a set of stocks.
Financial markets can be described on several time scales. We use data from the limit order book of the
London Stock Exchange (LSE) to compare how the fluctuation dominated microstructure crosses over to a more
systematic global behavior.
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