This repository provides materials on the LASSO theory and its application in finance --- has been used in the course Advances of Machine Learning in Finance (ACCFIN5229), at ASBS, University of Glasgow, 2022-23.
Gelman, A., & Vehtari, A. (2021). What are the most important statistical ideas of the past 50 years?. Journal of the American Statistical Association, 116(536), 2087-2097.
We use the glmnet function from glmnet package to run LASSO regression in R. We have two examples to show the results of this function and interpretation:
- An artificial data analysis to illustrate variable selection results and draw the solution (or regularization) path
- A real data analysis to show the applicability of the LASSO in finance
There is a short review on LASSO theory and R programming.
The LASSO created a new path in the world of variable selection, and model fitting. In this Google Sheet, we introduce those methods which have close connection with LASSO.
There exist five sheets:
- penalty function: introduces lasso-related penalties
- loss function: introduces references which use an alternative loss function insead of the sum of squares
- computational algorightm: introduces studies which propose an algorithm to solve the objective functions in penalized regression
- theoretical properties
- applications of lasso and its different variants in non-regression models