Application of a New Quantitative Approach to Stock Markets Using MST and dynamic time warping algorithms

Application of a New Quantitative Approach to Stock Markets Using MST and dynamic time warping algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of a New Quantitative Approach to Stock Markets Using MST and dynamic time warping algorithms benyamin safizadeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7404497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, the integration of graph theory and time series analysis has enabled more robust models for understanding complex financial systems. This paper presents a hybrid framework combining Minimum Spanning Tree (MST) and Dynamic Time Warping (DTW) to analyze interdependencies among stock market entities. MST simplifies the high-dimensional correlation structures into a tree-like topology, highlighting key relationships between assets. DTW, on the other hand, allows flexible temporal alignment of financial time series, making it ideal for comparing asset dynamics under variable time lags. Using synthetic stock data for 20 companies, we demonstrate how this framework effectively identifies structural shifts, systemic risk clusters, and central nodes in market behavior. The results emphasize the combined strength of topological and temporal models in modern quantitative finance. Financial Mathematics Stock Market Minimum Spanning Tree Dynamic Time Warping Systemic Risk Network Analysis Time Series Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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