I have built a model that explains how much risk of the stock market (S&P 500 index) is attributable to each sector, where each sector is independent from each other (correlation coefficients among the sectors are all 0).
For example, I have the following data:
Period Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Tech Materials Telecommunication Utilities 2018-04-26 to 2018-05-25 10.32% 7.13% 7.75% 12.61% 15.21% 7.34% 27.62% 7.79% 1.76% 2.45% 2018-04-27 to 2018-05-29 7.90% 5.22% 4.96% 20.67% 12.27% 10.97% 21.85% 9.72% 5.41% 1.01%
where I am using rolling-regression (using daily return, 31 days rolling-period) to calculate the values. It is interpretated that, during 2018-04-26 and 2018-05-25, 10.32% of the total market risk is explained by the Consumer Discretionary sector, independent from other sectors. Likewise, during 2018-04-27 to 2018-05-29, 21.85% of the total market risk is explained by the Information Tech sector, independent from other sectors.
The input data for those two regressions are exactly the same, except the first one contains 2018-04-26 and does not contain 2018-05-29 while the second one does not contain 2018-04-26 but does contain 2018-05-29. So any two consecutive rows differ by two data points.
My question is, using the regressions output and input data, is it possible to identify which input data are responsible for changes in the values?
For example, you can see that the risk contribution of Financials increases from 12.61% to 20.67% between the two rows. Is it because something happened on 2018-05-29, which is present in the second regression but not the first regression? Or is it because of 2018-04-26, which is present in the first regression but not the second regression? If neither, does it mean that those two data points (2018-04-26 and 2018-05-29) are not that important in explaining the change?
I am interested in this analysis because, given the output data, I want to know what actually took place in the market and explain it qualitatively. What is the name of this kind of analysis?
Thank you very much.