Topological evolution of financial network: A genetic algorithmic approach

Ga Ching Lui; Chun Yin Yip; Kwok Yip Szeto

doi:10.1007/978-3-319-59650-1_10

Topological evolution of financial network: A genetic algorithmic approach

Ga Ching Lui, Chun Yin Yip, Kwok Yip Szeto^*

^*Corresponding author for this work

Department of Physics

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

Abstract

The structure of financial market is captured using a novel time warping method known as discrete time warping genetic algorithm (dTWGA). In contrast to previous studies which estimate the correlations between different time series, dTWGA can be used to analyse time series with different lengths and with data sampled unevenly. Moreover, since coupling between different time series or at different periods of time would be changing over time, the time delay for the influence of a time series to reach another time series would be changing as well, which would not be well captured with correlation measurements. The proposed algorithm is applied on Dow Jones Index (DJI) and its compositions consisting of 30 stocks, and different measurements are performed to observe the evolution of the network structure. It is suggested that there are major topological changes during market crashes, leading to a significant decrease in the size of the network.

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings
Editors	Hector Quintian, Emilio Corchado, Francisco Javier [surname]Martinez de Pison, Ruben Urraca
Publisher	Springer Verlag
Pages	113-124
Number of pages	12
ISBN (Print)	9783319596495
DOIs	https://doi.org/10.1007/978-3-319-59650-1_10
Publication status	Published - 2017
Event	12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017 - La Rioja, Spain Duration: 21 Jun 2017 → 23 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10334 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017
Country/Territory	Spain
City	La Rioja
Period	21/06/17 → 23/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Financial network
Genetic algorithm
Market crash
Minimum spanning tree
Systemic risk
Time series alignment
Time warping

Access to Document

10.1007/978-3-319-59650-1_10

Cite this

Lui, G. C., Yip, C. Y., & Szeto, K. Y. (2017). Topological evolution of financial network: A genetic algorithmic approach. In H. Quintian, E. Corchado, F. J. [surname]Martinez de Pison, & R. Urraca (Eds.), Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings (pp. 113-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10334 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-59650-1_10

Lui, Ga Ching ; Yip, Chun Yin ; Szeto, Kwok Yip. / Topological evolution of financial network : A genetic algorithmic approach. Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings. editor / Hector Quintian ; Emilio Corchado ; Francisco Javier [surname]Martinez de Pison ; Ruben Urraca. Springer Verlag, 2017. pp. 113-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The structure of financial market is captured using a novel time warping method known as discrete time warping genetic algorithm (dTWGA). In contrast to previous studies which estimate the correlations between different time series, dTWGA can be used to analyse time series with different lengths and with data sampled unevenly. Moreover, since coupling between different time series or at different periods of time would be changing over time, the time delay for the influence of a time series to reach another time series would be changing as well, which would not be well captured with correlation measurements. The proposed algorithm is applied on Dow Jones Index (DJI) and its compositions consisting of 30 stocks, and different measurements are performed to observe the evolution of the network structure. It is suggested that there are major topological changes during market crashes, leading to a significant decrease in the size of the network.",

keywords = "Financial network, Genetic algorithm, Market crash, Minimum spanning tree, Systemic risk, Time series alignment, Time warping",

author = "Lui, \{Ga Ching\} and Yip, \{Chun Yin\} and Szeto, \{Kwok Yip\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017 ; Conference date: 21-06-2017 Through 23-06-2017",

year = "2017",

doi = "10.1007/978-3-319-59650-1\_10",

language = "English",

isbn = "9783319596495",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "113--124",

editor = "Hector Quintian and Emilio Corchado and \{[surname]Martinez de Pison\}, \{Francisco Javier\} and Ruben Urraca",

booktitle = "Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings",

}

Lui, GC, Yip, CY & Szeto, KY 2017, Topological evolution of financial network: A genetic algorithmic approach. in H Quintian, E Corchado, FJ [surname]Martinez de Pison & R Urraca (eds), Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10334 LNCS, Springer Verlag, pp. 113-124, 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017, La Rioja, Spain, 21/06/17. https://doi.org/10.1007/978-3-319-59650-1_10

Topological evolution of financial network: A genetic algorithmic approach. / Lui, Ga Ching; Yip, Chun Yin; Szeto, Kwok Yip.
Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings. ed. / Hector Quintian; Emilio Corchado; Francisco Javier [surname]Martinez de Pison; Ruben Urraca. Springer Verlag, 2017. p. 113-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10334 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Topological evolution of financial network

T2 - 12th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2017

AU - Lui, Ga Ching

AU - Yip, Chun Yin

AU - Szeto, Kwok Yip

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - The structure of financial market is captured using a novel time warping method known as discrete time warping genetic algorithm (dTWGA). In contrast to previous studies which estimate the correlations between different time series, dTWGA can be used to analyse time series with different lengths and with data sampled unevenly. Moreover, since coupling between different time series or at different periods of time would be changing over time, the time delay for the influence of a time series to reach another time series would be changing as well, which would not be well captured with correlation measurements. The proposed algorithm is applied on Dow Jones Index (DJI) and its compositions consisting of 30 stocks, and different measurements are performed to observe the evolution of the network structure. It is suggested that there are major topological changes during market crashes, leading to a significant decrease in the size of the network.

AB - The structure of financial market is captured using a novel time warping method known as discrete time warping genetic algorithm (dTWGA). In contrast to previous studies which estimate the correlations between different time series, dTWGA can be used to analyse time series with different lengths and with data sampled unevenly. Moreover, since coupling between different time series or at different periods of time would be changing over time, the time delay for the influence of a time series to reach another time series would be changing as well, which would not be well captured with correlation measurements. The proposed algorithm is applied on Dow Jones Index (DJI) and its compositions consisting of 30 stocks, and different measurements are performed to observe the evolution of the network structure. It is suggested that there are major topological changes during market crashes, leading to a significant decrease in the size of the network.

KW - Financial network

KW - Genetic algorithm

KW - Market crash

KW - Minimum spanning tree

KW - Systemic risk

KW - Time series alignment

KW - Time warping

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings

A2 - Quintian, Hector

A2 - Corchado, Emilio

A2 - [surname]Martinez de Pison, Francisco Javier

A2 - Urraca, Ruben

PB - Springer Verlag

Y2 - 21 June 2017 through 23 June 2017

ER -

Lui GC, Yip CY, Szeto KY. Topological evolution of financial network: A genetic algorithmic approach. In Quintian H, Corchado E, [surname]Martinez de Pison FJ, Urraca R, editors, Hybrid Artificial Intelligent Systems - 12th International Conference, HAIS 2017, Proceedings. Springer Verlag. 2017. p. 113-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59650-1_10

Topological evolution of financial network: A genetic algorithmic approach

Abstract

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Keywords

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