TY - GEN
T1 - A preliminary survey of analyzing dynamic time-varying financial networks using graph kernels
AU - Cui, Lixin
AU - Bai, Lu
AU - Rossi, Luca
AU - Zhang, Zhihong
AU - Jiao, Yuhang
AU - Hancock, Edwin R.
N1 - Funding Information:
Acknowledgments. This work is supported by the National Natural Science Foundation of China (Grant no. 61602535, 61503422 and 61773415), the Open Projects Program of National Laboratory of Pattern Recognition, and the program for innovation research in Central University of Finance and Economics.
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018/8
Y1 - 2018/8
N2 - In this paper, we investigate whether graph kernels can be used as a means of analyzing time-varying financial market networks. Specifically, we aim to identify the significant financial incident that changes the financial network properties through graph kernels. Our financial networks are abstracted from the New York Stock Exchange (NYSE) data over 6004 trading days, where each vertex represents the individual daily return price time series of a stock and each edge represents the correlation between pairwise series. We propose to use two state-of-the-art graph kernels for the analysis, i.e., the Jensen-Shannon graph kernel and the Weisfeiler-Lehman subtree kernel. The reason of using the two kernels is that they are the representative methods of global graph kernels and local graph kernels, respectively. We perform kernel Principle Components Analysis (kPCA) associated with each kernel matrix to embed the networks into a 3-dimensional principle space, where the time-varying networks of all trading days are visualized. Experimental results on the financial time series of NYSE dataset demonstrate that graph kernels can well distinguish abrupt changes of financial networks with time, and provide a more effective alternative way of analyzing original multiple co-evolving financial time series. We theoretically indicate the perspective of developing novel graph kernels on time-varying networks for multiple co-evolving time series analysis in future work.
AB - In this paper, we investigate whether graph kernels can be used as a means of analyzing time-varying financial market networks. Specifically, we aim to identify the significant financial incident that changes the financial network properties through graph kernels. Our financial networks are abstracted from the New York Stock Exchange (NYSE) data over 6004 trading days, where each vertex represents the individual daily return price time series of a stock and each edge represents the correlation between pairwise series. We propose to use two state-of-the-art graph kernels for the analysis, i.e., the Jensen-Shannon graph kernel and the Weisfeiler-Lehman subtree kernel. The reason of using the two kernels is that they are the representative methods of global graph kernels and local graph kernels, respectively. We perform kernel Principle Components Analysis (kPCA) associated with each kernel matrix to embed the networks into a 3-dimensional principle space, where the time-varying networks of all trading days are visualized. Experimental results on the financial time series of NYSE dataset demonstrate that graph kernels can well distinguish abrupt changes of financial networks with time, and provide a more effective alternative way of analyzing original multiple co-evolving financial time series. We theoretically indicate the perspective of developing novel graph kernels on time-varying networks for multiple co-evolving time series analysis in future work.
KW - Graph kernels
KW - NYSE dataset
KW - Time-varying financial networks
UR - http://www.scopus.com/inward/record.url?scp=85052215974&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-97785-0_23
DO - 10.1007/978-3-319-97785-0_23
M3 - Conference article published in proceeding or book
AN - SCOPUS:85052215974
SN - 9783319977843
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 237
EP - 247
BT - Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Proceedings
A2 - Hancock, Edwin R.
A2 - Ho, Tin Kam
A2 - Biggio, Battista
A2 - Wilson, Richard C.
A2 - Robles-Kelly, Antonio
A2 - Bai, Xiao
PB - Springer Verlag
T2 - Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition, SSPR 2018 and Statistical Techniques in Pattern Recognition, SPR 2018
Y2 - 17 August 2018 through 19 August 2018
ER -