With topological data analysis, predicting stock market crashes

Nugroho Agung Prabowo, R Arri Widyanto, Mukhtar Hanafi, Bambang Pujiarto, Meidar Hadi Avizenna

Abstract


We are investigating the evolution of four big US stock market indexes' regular returns after the 2000 technology crash and the 2007-2009 financial crisis. Our approach is based on topological data processing (TDA). To identify and measure topological phenomena occurring in multidimensional time series, we use persistence homology. We obtain time-dependent point cloud data sets using a sliding window, which we connect a topological space for. Our research indicates that a new method of econometric analysis is offered by TDA, which complements the traditional statistical tests. The tool may be used to predict early warning signs of market declines that are inevitable.

Article Metrics

Abstract: 143 Viewers PDF: 63 Viewers

Keywords


TDA, Market Crashes, Stock Detection, Topology

Full Text:

PDF


References


H. Edelsbrunner, D. Letscher, and A. Zomorodian, “Topological persistence and simplification,” Discret. Comput. Geom., vol. 28, no. 4, pp. 511–533, 2002, doi: 10.1007/s00454-002-2885-2.

A. Zomorodian and G. Carlsson, “Computing persistent homology,” Proc. Annu. Symp. Comput. Geom., vol. 274, pp. 347–356, 2004, doi: 10.1145/997817.997870.

G. Carlsson, Topology and data, vol. 46, no. 2. 2009.

F. Chazal, H. T. Data, and A. Handbook, “High-Dimensional Topological Data Analysis Frédéric Chazal To cite this version : HAL Id : hal-01316989 HIGH-DIMENSIONAL TOPOLOGICAL DATA ANALYSIS,” 2016.

C. Maria, J. D. Boissonnat, M. Glisse, and M. Yvinec, “The Gudhi library: Simplicial complexes and persistent homology,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8592 LNCS, pp. 167–174, 2014, doi: 10.1007/978-3-662-44199-2_28.

B. T. Fasy, J. Kim, F. Lecci, and C. Maria, “Introduction to the R package TDA,” no. January 2015, pp. 1–16, 2014, [Online]. Available: http://arxiv.org/abs/1411.1830.

M. Kramar, A. Goullet, L. Kondic, and K. Mischaikow, “Persistence of force networks in compressed granular media,” Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys., vol. 87, no. 4, pp. 1–8, 2013, doi: 10.1103/PhysRevE.87.042207.

T. Nakamura, Y. Hiraoka, A. Hirata, E. G. Escolar, and Y. Nishiura, “Persistent homology and many-body atomic structure for medium-range order in the glass,” Nanotechnology, vol. 26, no. 30, pp. 1–22, 2015, doi: 10.1088/0957-4484/26/30/304001.

P. Skraba, S. Univerity, S. Ca, and L. Guibas, “Persistence-based Segmentation of Deformable Shapes Fr ´,” Work, no. 5, 2006.

K. Turner, S. Mukherjee, and D. M. Boyer, “Persistent homology transform for modeling shapes and surfaces,” Inf. Inference, vol. 3, no. 4, pp. 310–344, 2014, doi: 10.1093/imaiai/iau011.

L. M. Seversky, S. Davis, and M. Berger, “On Time-Series Topological Data Analysis: New Data and Opportunities,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 1014–1022, 2016, doi: 10.1109/CVPRW.2016.131.

Y. Yao et al., “Topological methods for exploring low-density states in biomolecular folding pathways,” J. Chem. Phys., vol. 130, no. 14, pp. 1–10, 2009, doi: 10.1063/1.3103496.

Y. Lee, S. D. Barthel, P. Dłotko, S. M. Moosavi, K. Hess, and B. Smit, “Quantifying similarity of pore-geometry in nanoporous materials,” Nat. Commun., vol. 8, no. May, 2017, doi: 10.1038/ncomms15396.

V. De Silva and R. Ghrist, “Homological Sensor Networks Sensors and Sense-ability.”


Refbacks

  • There are currently no refbacks.


barcodeInternational Journal of Informatics and Information Systems (IJIIS)
ISSN: 2579-7069 (online)
Organized by Information System Department - Universitas Amikom Purwokerto - Indonesia, Laboratoire Signaux Et Systèmes (L2s) - Université Paris 13 - France, and Bright Publisher
Published by Bright Publisher
Website : http://ijiis.org
Email : info@ijiis.orgtaqwa@ijiis.org, andhika@ijiis.org

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0