A comparative study of symbolic aggregate approximation and topological data analysis

The movement of stocks is often perceived as random due to the complex interactions between different stocks and the inherently chaotic nature of the market. This study investigated the similarity in stock movements across multiple industry sectors in Europe. Specifically, we applied topological dat...

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Main Authors: Fredrik Hobbelhagen, Ioannis Diamantis
Format: Article
Language:English
Published: AIMS Press 2024-10-01
Series:Quantitative Finance and Economics
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/QFE.2024027
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author Fredrik Hobbelhagen
Ioannis Diamantis
author_facet Fredrik Hobbelhagen
Ioannis Diamantis
author_sort Fredrik Hobbelhagen
collection DOAJ
description The movement of stocks is often perceived as random due to the complex interactions between different stocks and the inherently chaotic nature of the market. This study investigated the similarity in stock movements across multiple industry sectors in Europe. Specifically, we applied topological data analysis (TDA) to analyze stock time series data and compared the results with those obtained using an expanded form of a more classical time series analysis method, symbolic aggregate approximation (SAX). Our findings indicated that while TDA offered detailed insights into 'local' stock movements, SAX was more effective in capturing broader trends in financial markets, where less detail was required, making it suitable for portfolio optimization. We also presented an extension of SAX that incorporated volatility measures, improving its performance in highly volatile markets.
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spelling doaj-art-5e35c33fb1d540a0bc3b378d910e83c92025-01-24T01:12:54ZengAIMS PressQuantitative Finance and Economics2573-01342024-10-018470573210.3934/QFE.2024027A comparative study of symbolic aggregate approximation and topological data analysisFredrik Hobbelhagen0Ioannis Diamantis1School of Business and Economics, Maastricht University, P.O.BOX 616, 6200 MD, Maastricht, The NetherlandsSchool of Business and Economics, Maastricht University, P.O.BOX 616, 6200 MD, Maastricht, The NetherlandsThe movement of stocks is often perceived as random due to the complex interactions between different stocks and the inherently chaotic nature of the market. This study investigated the similarity in stock movements across multiple industry sectors in Europe. Specifically, we applied topological data analysis (TDA) to analyze stock time series data and compared the results with those obtained using an expanded form of a more classical time series analysis method, symbolic aggregate approximation (SAX). Our findings indicated that while TDA offered detailed insights into 'local' stock movements, SAX was more effective in capturing broader trends in financial markets, where less detail was required, making it suitable for portfolio optimization. We also presented an extension of SAX that incorporated volatility measures, improving its performance in highly volatile markets.https://www.aimspress.com/article/doi/10.3934/QFE.2024027topological data analysissymbolic aggregate approximationcomparison, time series, stock markets
spellingShingle Fredrik Hobbelhagen
Ioannis Diamantis
A comparative study of symbolic aggregate approximation and topological data analysis
Quantitative Finance and Economics
topological data analysis
symbolic aggregate approximation
comparison, time series, stock markets
title A comparative study of symbolic aggregate approximation and topological data analysis
title_full A comparative study of symbolic aggregate approximation and topological data analysis
title_fullStr A comparative study of symbolic aggregate approximation and topological data analysis
title_full_unstemmed A comparative study of symbolic aggregate approximation and topological data analysis
title_short A comparative study of symbolic aggregate approximation and topological data analysis
title_sort comparative study of symbolic aggregate approximation and topological data analysis
topic topological data analysis
symbolic aggregate approximation
comparison, time series, stock markets
url https://www.aimspress.com/article/doi/10.3934/QFE.2024027
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