All Title Author
Keywords Abstract


Multidimensional Time Series Analysis of Financial Markets Based on the Complex Network Approach

DOI: 10.4236/jmf.2017.73039, PP. 734-750

Keywords: Complex Networks, Time Series, Price-Volume Correlations, Multidimensional

Full-Text   Cite this paper   Add to My Lib

Abstract:

In this study, a modeling method to analyze multidimensional time series based on complex networks is proposed. The rate of return sequence of the closing price and the trading volume fluctuation sequence of the Shanghai Composite Index, the Shenzhen Component Index, the S & P 500 index, and the Dow Jones Industrial Average are analyzed. The two-dimensional time series is transformed into a complex network. We analyze the spatial distribution characteristics of the network to determine the relationship between volume and price. It is found that the interaction of stock return and volume in China’ stock market is more obvious than that in the American market.

References

[1]  Clark, P.K. (1973) A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices. Econometrica, 41, 135-155.
https://doi.org/10.2307/1913889
[2]  Smirlock, M. and Starks, L. (1985) A Further Examination of Stock Prices Change and Transactions Volume. Journal of Financial Research, 8, 217-225.
https://doi.org/10.1111/j.1475-6803.1985.tb00404.x
[3]  Bessembinder, H. and Seguin, P.J. (1993) Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets. The Journal of Financial and Quantitative Analysis, 28, 21-39.
https://doi.org/10.2307/2331149
[4]  Podobnik, B., Horvatic, D., Petersen, A.M. and Stanley, H.E. (2009) Cross-Correlations between Volume Change and Price Change. PNAS, 106, 22079-22084.
https://doi.org/10.1073/pnas.0911983106
[5]  Zhang, W. and Yan, J. (1998) Imperial Study on Capacity Price of Causal Relationship in Shanghai Stock Market. System Engineering Theory and Practice, 6, 111-114. (In Chinese)
[6]  Chen, Y.L. and Song, F.M. (2000) Imperial Study on the Relationship between China’s Stock Market Price Variation and Trading Volume. Management Science, 3, 62-68. (In Chinese)
[7]  He, X.Q. and Liu, X.Y. (2005) Experience Analysis on Asymmetry and Mixed Layout Assumption on China’s Stock Market Variation. Nankai Economy Study, 3, 91-95. (In Chinese)
[8]  Wang, C.F., Sun, X.X. and Zheng, S. (2012) Posterior Distribution Structure and Simulation in Capacity Price Analysis of China’s Stock Market. Practice and Understanding of Maths, 42, 37-47. (In Chinese)
[9]  Zhang, J. and Small, M. (2006) Complex Network from Pseudoperiodic Time Series: Topology versus Dynamics. Physical Review Letters, 96, 238701.
https://doi.org/10.1103/PhysRevLett.96.238701
[10]  Yang, Y. and Yang, H. (2008) Complex Network-Based Time Series Analysis. Physica A, 387, 1381-1386.
https://doi.org/10.1016/j.physa.2007.10.055
[11]  Lacasa, L., Luque, B., Ballesteros, F., Luque, J. and Nuno, J.C. (2008) From Time Series to Complex Networks: The Visibility Graph. Proceedings of the National Academy of Sciences, 105, 4972-4795.
https://doi.org/10.1073/pnas.0709247105
[12]  Li, Y., Cao, H.D. and Tan, Y. (2011) A Novel Method of Identifying Time Series Based on Network Graph. Complexity, 11, 13-33.
https://doi.org/10.1002/cplx.20384
[13]  Li, Y., Cao, H.D. and Tan, Y. (2011) A Comparison of Two Methods for Modeling Large-Scale Data from Time Series as Complex Networks. AIP Advances, 3, 1-10.
https://doi.org/10.1063/1.3556121
[14]  Cao, H.D. and Li, Y. (2014) Unraveling Chaotic Attractors by Complex Networks and Measurements of Stock Market Complexity. Chaos: An Interdisciplinary Journal of Nonlinear Science, 24, 013134.
https://doi.org/10.1063/1.4868258

Full-Text

comments powered by Disqus