  The problem that is solved in this paper is known as index tracking. The
method of Lasso is used to reduce the dimensions of S&P500 index which has many
applications in both investment and portfolio management algorithms. The
novelty of this paper is that post-selection inference is used to have better
modeling and inference for Lasso approach to index tracking. Both confidence
intervals and curves indicate that the performance of Lasso type method for
dimension reduction of S&P500 is remarkably high. Keywords: index tracking,
lasso, post-selection inference, S&P500
