Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

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

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

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

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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",

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doi = "10.1109/CEEC.2016.7835896",

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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.

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