4.1.2. Feature Selection

First, data cleaning, including estimating outliers (extreme values) and missing values, has been applied to the raw data to build a better data set. After that, VIF is applied to the data set to deal with multicollinearity. Table A2 in the Appendix A.1 shows variables, namely, market capitalization, transactions per block, Hash Rate, mining difficulty, cost per transaction, total transactions per day, Nasdaq Composite, Dow Jones Industrial Average, and S&P 500, which have a VIF greater than 10. Instead of dropping variables, the entire sample period has been tested in nine models with different combinations of variables.
