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Hierarchical analysis of Chinese financial market based on manifold structure

  • S.I.: Business Analytics and Operations Research
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Abstract

This paper aims to discuss the fragilities of hierarchical structures of Chinese financial market, and find out the possible faults. We propose an analysis approach to explore the structure characteristics of financial market based on the manifold structure of financial data. First, we extract the underlying manifold embedded in financial time series data by manifold learning, which governs the dynamics of financial system. Second, the surface curvature of manifold is used to serve the quantitative analysis of manifold structure, in which the rate of curvature change is employed to measure the structure fragility. Finally, the dynamics of manifold structures are discussed by Lyapunov exponents, which further explore the fragilities of markets and confirm the conclusions of curvature analysis. In empirical research, we select CSI 300 index as the overall trend indicator of China's financial market, and nine industry indexes as hierarchical market indicators. Our research results indicate that Chinese financial market has less chaotic than the real economy industries, in spite of its high fragility. Some real economic industries are most likely to crash first in the event of a crisis. The findings contribute to present the states of financial markets and provide decision support for investors.

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References

  • Ansar, A., Flyvbjerg, B., Budzier, A., & Lunn, D. (2016). Does infrastructure investment lead to economic growth or economic fragility? Evidence from China. Oxford Review of Economic Policy, 32, 360–390.

    Article  Google Scholar 

  • Baker, M., Wurgler, J., & Yuan, Y. (2012). Global, local, and contagious investor sentiment. Journal of Financial Economics, 104, 272–287.

    Article  Google Scholar 

  • Beck, T., Tao, C., Chen, L., & Song, F. M. (2016). Financial innovation: The bright and the dark sides. Journal of Banking and Finance, 72, 28–51.

    Article  Google Scholar 

  • Belkin, M., & Niyogi, P. (2003). Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373–1396.

    Article  Google Scholar 

  • Benincà, E., Huisman, J., Heerkloss, R., et al. (2008). Chaos in a long-term experiment with a plankton community. Nature, 451, 822–825.

    Article  Google Scholar 

  • Cao, L. (1997). Practical method for determining the minimum embedding dimension of a scalar time series. Physical D, 110, 43–50.

    Article  Google Scholar 

  • Casetti, L., Clementi, C., & Pettini, M. (1996). Riemannian theory of Hamiltonian chaos and Lyapunov exponents. Physical Review E, 54, 5969–5984.

    Article  Google Scholar 

  • Demetrius, L. A. (2013). Boltzmann Darwin and directionality theory. Physics Reports, 530, 1–85.

    Article  Google Scholar 

  • Dunlop, G. R. (1980). A rapid computational method for improvements to nearest neighbor interpolation. Computers and Mathematics with Applications, 6, 349–353.

    Article  Google Scholar 

  • Givens, C. R., & Shortt, R. M. (1984). A class of wasserstein metrics for probability distributions. Michigan Mathematical Journal, 31, 231–240.

    Article  Google Scholar 

  • Haji, A. H., Mahzoon, M., & Emdad, H. (2007). Reconstruction and prediction of the in-cylinder pressure attractor of an internal combustion engine using locally constant models. Nonlinear Dynamics, 48, 437–447.

    Article  Google Scholar 

  • Han, L. Y., & Christopher, J. R. (2011). Stable Takens’ embeddings for linear dynamical systems. IEEE Transactions on Signal Processing, 59, 4781–4794.

    Article  Google Scholar 

  • Hui, X. F., & Zhang, A. R. (2020). Construction and empirical research on the dynamic provisioning model of China’s banking sector under the macro-prudential framework. Sustainability, 12(20), 8527.

    Article  Google Scholar 

  • Jamshidi, A. A., Kirby, M. J., & Broomhead, D. S. (2011). Geometric manifold learning. IEEE Signal Processing Magazine, 28, 69–76.

    Article  Google Scholar 

  • Kılıç, D. K., & Uğur, Ö. (2018). Multiresolution analysis of S&P500 time series. Annals of Operations Research, 260(1), 197–216.

    Article  Google Scholar 

  • Kim, K., & Lee, J. (2014). Nonlinear Dynamic Projection for Noise Reduction of Dispersed Manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 2303–2309.

    Article  Google Scholar 

  • Kinsner, W. (2006). Characterizing chaos through Lyapunov metrics. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 36, 141–151.

    Article  Google Scholar 

  • Li, B., Zheng, C. H., & Huang, D. S. (2008). Locally linear discriminate embedding: An efficient method for face recognition. Pattern Recognition, 41, 3813–3821.

    Article  Google Scholar 

  • Lott, J., & Villani, C. (2009). Ricci curvature for metric-measure spaces via optimal transport. Annals of Mathematics, 169, 903–991.

    Article  Google Scholar 

  • Lunga, D., Prasad, S., Crawford, M., & Ersoy, O. (2014). Manifold-learning-based feature extraction for classification of hyperspectral data. IEEE Signal Processing Magazine, 31, 55–66.

    Article  Google Scholar 

  • Menck, P. J., Heitzig, J., Marwan, N., & Kurths, J. (2013). How basin stability complements the linear-stability paradigm. Nature Physics, 9, 89–92.

    Article  Google Scholar 

  • Ollivier, Y. (2007). Ricci curvature of metric spaces. Computers Rendus Mathematique, 345, 643–6465.

    Article  Google Scholar 

  • Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 29, 2323–2326.

    Article  Google Scholar 

  • Sandhu, R. S., Georgiou, T. T., & Tannenbaum, A. R. (2016). Ricci curvature: An economic indicator for market fragility and systemic risk. Science Advances, 2, e1501495–e1501495.

    Article  Google Scholar 

  • Sivakumar, B. (2002). A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. Journal of Hydrology, 258, 149–162.

    Article  Google Scholar 

  • Sturm, K. T. (2006). On the geometry of metric measure spaces. Acta Mathematica, 196, 65–131.

    Article  Google Scholar 

  • Sugihara, G., May, R., Ye, H., Hsieh, C., Deyle, E., Fogarty, E. M., & Munch, S. (2012). Detecting causality in complex ecosystems. Science, 338, 496–500.

    Article  Google Scholar 

  • Takens, F. (1980). Dynamical systems and turbulence. In D. R. Warwick & L. S. Young (Eds.), Detecting strange attractors in turbulence (pp. 366–381). Berlin: Springer.

    Google Scholar 

  • Talmon, R., Cohen, I., Gannot, S., & Coifman, R. R. (2013). Diffusion maps for signal processing: A deeper look at manifold-learning techniques based on kernels and graphs. IEEE Signal Processing Magazine, 30(4), 75–86.

    Article  Google Scholar 

  • Tenenbaum, J. B., Sivlar, V. J., & Langford, C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319–2323.

    Article  Google Scholar 

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Acknowledgements

We would like to thank reviewers and editors for their comments, which are helpful for us to improve the paper. This research was supported by the Open Research Subject of Key Laboratory (Research Base) of Research Center of FinTech and Entrepreneurial Finance (#JR2017-02), and supported by Sichuan Science and Technology Program (#2019YJ0452).

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Correspondence to Yan Huang.

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Huang, Y., Wan, J. Hierarchical analysis of Chinese financial market based on manifold structure. Ann Oper Res 315, 1135–1150 (2022). https://doi.org/10.1007/s10479-021-03959-8

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