Sector identification in a set of stock return time series traded at the London Stock Exchange

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Abstract

We compare 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 portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree: of sensitivity with respect to different sectors. 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 stock portfolio.
Lingua originaleEnglish
pagine (da-a)2653-2679
Numero di pagine27
RivistaActa Physica Polonica B
Volume36 (9)
Stato di pubblicazionePublished - 2005

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sectors
horizon
economics
correlation coefficients
matrix theory
sensitivity

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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title = "Sector identification in a set of stock return time series traded at the London Stock Exchange",
abstract = "We compare 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 portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree: of sensitivity with respect to different sectors. 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 stock portfolio.",
keywords = "FINANCIAL-MARKETS; EXPRESSION DATA; MATRICES; SYSTEMS; NOISE",
author = "Mantegna, {Rosario Nunzio} and Fabrizio Lillo and Michele Tumminello and Claudia Coronnello and Salvatore Micciche' and Lillo and Mantbgna and Miccich{\`e} and Claudia Coronnello",
year = "2005",
language = "English",
volume = "36 (9)",
pages = "2653--2679",
journal = "Acta Physica Polonica B",
issn = "0587-4254",
publisher = "Jagellonian University",

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TY - JOUR

T1 - Sector identification in a set of stock return time series traded at the London Stock Exchange

AU - Mantegna, Rosario Nunzio

AU - Lillo, Fabrizio

AU - Tumminello, Michele

AU - Coronnello, Claudia

AU - Micciche', Salvatore

AU - Lillo, null

AU - Mantbgna, null

AU - Miccichè, null

AU - Coronnello, Claudia

PY - 2005

Y1 - 2005

N2 - We compare 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 portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree: of sensitivity with respect to different sectors. 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 stock portfolio.

AB - We compare 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 portfolio of stocks traded at the London Stock Exchange. The investigated time series are recorded both at a daily time horizon and at a 5-minute time horizon. The correlation coefficient matrix is very different at different time horizons confirming that more structured correlation coefficient matrices are observed for long time horizons. All the considered methods are able to detect economic information and the presence of clusters characterized by the economic sector of stocks. However, different methods present a different degree: of sensitivity with respect to different sectors. 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 stock portfolio.

KW - FINANCIAL-MARKETS; EXPRESSION DATA; MATRICES; SYSTEMS; NOISE

UR - http://hdl.handle.net/10447/5857

M3 - Article

VL - 36 (9)

SP - 2653

EP - 2679

JO - Acta Physica Polonica B

JF - Acta Physica Polonica B

SN - 0587-4254

ER -