Publication:
Detecting Anomalies in the Dynamics of a Market Index with Topological Data Analysis, International Journal of Systematic Innovation
Detecting Anomalies in the Dynamics of a Market Index with Topological Data Analysis, International Journal of Systematic Innovation
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Date
2021
Authors
Ngoc Kim Khanh Nguyen
Marc Bui
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Research Projects
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Abstract
"We investigate the collective behavior of a stock market by studying the dynamics of its representative index’s
return, using the persistence diagram of the index return’s time-delay embedding, an approach of the
Topological Data Analysis (TDA) in time series analysis. While the time-delay embedding captures the state
space of the index return’s dynamics, the persistence diagram encodes the space's topological information under
different spatial resolu-tions. Therefore, based on the changes in the point distribution of the persistence diagram
over time, we propose a framework to detect its extraordinary movements. Our method provides a measure for
the stability level of the mar-ket’s collective behavior. After applying this method for the daily return of the S&P
500 index from 1970 to 2020, we demonstrate that the measure efficiently tracks the changes in topological
information of the index re-turn. Furthermore, we can capture major American recessions when the measure
exceeds a threshold. A continuous and rapid increase of the measure approaching the threshold is considered a
warning of a crisis. Hence, our method provides a technical indicator for systematic risk management."
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Keywords
anomalies detection,
market index,
persistence diagram,
time-delay embedding