Exact Post-selection Inference For Tracking S&P500

TL;DR

This paper applies post-selection inference to Lasso-based index tracking of the S&P500, improving model inference and demonstrating high performance in dimension reduction for investment applications.

Contribution

It introduces the use of post-selection inference to enhance Lasso-based index tracking, providing more reliable modeling and inference methods.

Findings

Lasso method effectively reduces dimensions of S&P500 index.

Post-selection inference improves the reliability of Lasso-based models.

Lasso approach shows high performance in index tracking tasks.

Abstract

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