Distance Correlation Market Graph: The Case of S&P500 Stocks

Samuel Ugwu (), Pierre Miasnikof and Yuri Lawryshyn
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Samuel Ugwu: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
Pierre Miasnikof: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada
Yuri Lawryshyn: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada

Mathematics, 2023, vol. 11, issue 18, 1-13

Abstract: This study investigates the use of a novel market graph model for equity markets. Our graph model is built on distance correlation instead of the traditional Pearson correlation. We apply it to the study of S&P500 stocks from January 2015 to December 2022. We also compare our market graphs to the traditional market graphs in the literature, those built using Pearson correlation. To further the comparison, we also build graphs using Spearman rank correlation. Our comparisons reveal that non-linear relationships in stock returns are not captured by either Pearson correlation or Spearman rank correlation. We observe that distance correlation is a robust measure for detecting complex relationships in S&P500 stock returns. Networks built on distance correlation networks, are shown to be more responsive to market conditions during turbulent periods such as the COVID crash period.

Keywords: distance correlation; complex networks; market graph; stock market network; non-linearity; investment science (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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