Multivariate Variational Mode Decomposition based Analysis on Stock Sectors

The price of a single stock is seldom independent. It has been known to brokers and fund managers, that, they heavily influence each other. Portfolios are built, on the premise of minimizing such dependencies between stocks. There have been several efforts to quantify these dependencies, predominantly using conventional statistics and correlations. Most approaches use single independent variable-based approaches to compare two stock price signals at a time. In this paper, we analyze stock sectors with multiple stocks in each, presenting a multivariate case. We apply two multivariate analysis techniques, namely multivariate variational mode decomposition (MVMD) and spatio-temporal intrinsic mode decomposition (STIMD), to analyze stock sectors based on their individual stock day-wise price series. Sector-wise data is downloaded from Google Finance. Furthermore, we quantify the dependence of a sector on each of its constituent stock, by decomposing this multivariate signal and selectively reconstructing it multiple times, excluding each one from the reconstruction process. The error of reconstruction in each case serves as a measure of how much other stocks depend on the one that was excluded.