Manuscript received July 5, 2022; revised August 22, 2022; accepted January 11, 2023.
Abstract—Under various risks, companies are required to be resilient, so that they can flexibly respond to changes. In this paper, we evaluate the resilience of the automobile manufacturing industry after the 2008 financial crisis, using machine learning methods. The approach consists of time series data clustering named “Amplitude-based clustering” and dimensional reduction method named “UMAP”. In the analysis, companies’ indexed market capitalization data are used. In order to investigate the recovery power of companies, the growth rate is important and the amplitude-based clustering is required. In the UMAP result, we found that one of the principal components can be used as the resilience measurement. Our approach in this paper is widely applicable to measure industries’ resilience after the drastic decline of stock prices.
Index Terms—Time series data clustering Amplitude-based clustering, dimensionality reduction, UMAP, market capitalization, automakers, stock prices, resilience, 2008 financial crisis
Both authors are with the Faculty of Economics, Gakushuin University, Tokyo, Japan. *Correspondence: email@example.com (Y.S.)
Cite: Seiji Matsuhashi and Yukari Shirota, "Resilience Evaluation of Automakers after 2008 Financial Crisis by UMAP," International Journal of Machine Learning vol. 13, no. 3, pp. 125-130, 2023.Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).