With topological data analysis, predicting stock market crashes
Dublin Core
Title
With topological data analysis, predicting stock market crashes
Subject
TDA, Market Crashes, Stock Detection, Topology
Description
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
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
Creator
Nugroho Agung Prabowo 1,* , R Arri Widyanto 2 , Mukhtar Hanafi 3 ,
Andi Widiyanto 4 , Bambang Pujiarto 5 , Meidar Hadi Avizenna
Andi Widiyanto 4 , Bambang Pujiarto 5 , Meidar Hadi Avizenna
Date
2021
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Collection
Citation
Nugroho Agung Prabowo 1,* , R Arri Widyanto 2 , Mukhtar Hanafi 3 ,
Andi Widiyanto 4 , Bambang Pujiarto 5 , Meidar Hadi Avizenna, “With topological data analysis, predicting stock market crashes,” Repository Horizon University Indonesia, accessed June 8, 2025, https://repository.horizon.ac.id/items/show/9248.