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The use of biweight mid correlation to improve graph based portfolio construction

Research output: Chapter in Book/Report/Conference proceedingConference paper

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The use of biweight mid correlation to improve graph based portfolio construction. / Veenstra, Patrick; Cooper, Colin; Phelps, Steve.

2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 101-106 7835896.

Research output: Chapter in Book/Report/Conference proceedingConference paper

Harvard

Veenstra, P, Cooper, C & Phelps, S 2017, The use of biweight mid correlation to improve graph based portfolio construction. in 2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings., 7835896, Institute of Electrical and Electronics Engineers Inc., pp. 101-106, 8th Computer Science and Electronic Engineering Conference, CEEC 2016, Colchester, United Kingdom, 28/09/2016. DOI: 10.1109/CEEC.2016.7835896

APA

Veenstra, P., Cooper, C., & Phelps, S. (2017). The use of biweight mid correlation to improve graph based portfolio construction. In 2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings (pp. 101-106). [7835896] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/CEEC.2016.7835896

Vancouver

Veenstra P, Cooper C, Phelps S. The use of biweight mid correlation to improve graph based portfolio construction. In 2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc.2017. p. 101-106. 7835896. Available from, DOI: 10.1109/CEEC.2016.7835896

Author

Veenstra, Patrick ; Cooper, Colin ; Phelps, Steve. / The use of biweight mid correlation to improve graph based portfolio construction. 2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 101-106

Bibtex Download

@misc{640449c622394cedac635f2b71033ae4,
title = "The use of biweight mid correlation to improve graph based portfolio construction",
abstract = "An analysis of the correlation between the returns of different securities is of fundamental importance in many areas of finance, such as portfolio optimisation. The most commonly used measure of correlation is the Pearson correlation coefficient; however, this suffers from several problems when applied to data from the real world. We propose an alternative estimator - the Biweight Mid Correlation (Bicor) - as a more robust measure for capturing the relationship between returns. We systematically evaluate Bicor empirically using data from the FTSE 100 constituents, and show that it is more robust when compared with the Pearson correlation coefficient. Finally, we demonstrate that Bicor can be used to improve a graph-based method of portfolio construction. Specifically, we show that when treating the correlation matrix as an adjacency matrix for a graph and using graph centrality to construct portfolios, the use of Bicor leads to better performing portfolios.",
author = "Patrick Veenstra and Colin Cooper and Steve Phelps",
year = "2017",
month = "1",
day = "27",
doi = "10.1109/CEEC.2016.7835896",
language = "English",
isbn = "9781509020508",
pages = "101--106",
booktitle = "2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS (suitable for import to EndNote) Download

TY - GEN

T1 - The use of biweight mid correlation to improve graph based portfolio construction

AU - Veenstra,Patrick

AU - Cooper,Colin

AU - Phelps,Steve

PY - 2017/1/27

Y1 - 2017/1/27

N2 - An analysis of the correlation between the returns of different securities is of fundamental importance in many areas of finance, such as portfolio optimisation. The most commonly used measure of correlation is the Pearson correlation coefficient; however, this suffers from several problems when applied to data from the real world. We propose an alternative estimator - the Biweight Mid Correlation (Bicor) - as a more robust measure for capturing the relationship between returns. We systematically evaluate Bicor empirically using data from the FTSE 100 constituents, and show that it is more robust when compared with the Pearson correlation coefficient. Finally, we demonstrate that Bicor can be used to improve a graph-based method of portfolio construction. Specifically, we show that when treating the correlation matrix as an adjacency matrix for a graph and using graph centrality to construct portfolios, the use of Bicor leads to better performing portfolios.

AB - An analysis of the correlation between the returns of different securities is of fundamental importance in many areas of finance, such as portfolio optimisation. The most commonly used measure of correlation is the Pearson correlation coefficient; however, this suffers from several problems when applied to data from the real world. We propose an alternative estimator - the Biweight Mid Correlation (Bicor) - as a more robust measure for capturing the relationship between returns. We systematically evaluate Bicor empirically using data from the FTSE 100 constituents, and show that it is more robust when compared with the Pearson correlation coefficient. Finally, we demonstrate that Bicor can be used to improve a graph-based method of portfolio construction. Specifically, we show that when treating the correlation matrix as an adjacency matrix for a graph and using graph centrality to construct portfolios, the use of Bicor leads to better performing portfolios.

UR - http://www.scopus.com/inward/record.url?scp=85015266628&partnerID=8YFLogxK

U2 - 10.1109/CEEC.2016.7835896

DO - 10.1109/CEEC.2016.7835896

M3 - Conference paper

SN - 9781509020508

SP - 101

EP - 106

BT - 2016 8th Computer Science and Electronic Engineering Conference, CEEC 2016 - Conference Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

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