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

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.