Geodesy

World Wide Web

Benchmark (surveying)

Engineering

Elastic net regularization

Statistics

Chromatography

Predictability

Petroleum engineering

Accounting

Economics

Set (abstract data type)

Computer science

Regression

Variance (accounting)

Mathematics

Geography

Feature selection

Econometrics

Lasso (programming language)

Chemistry

Programming language

Crude oil

Sample (material)

Machine learning

Journal of Empirical Finance

In this paper, we use two prevailing shrinkage methods, the lasso and elastic net, to predict oil price returns with a large set of predictors. The out-of-sample results indicate that the lasso and elastic net models outperform a host of widely used competing models in terms of out-of-sample R-square and success ratio. In an asset allocation exercise, a mean–variance investor obtains positive and sizeable economic gains based on the return forecasts of the lasso and elastic net methods relative to both the benchmark forecasts and competing forecasts. We further investigate the source of predictability from a variable selection perspective. The lasso and elastic net methods are found to select powerful predictors and the ones that can provide complementary information. The OLS regression models based on the selected predictors also exhibit better out-of-sample performances than the competing models. In addition, our results are robust to various settings.

Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?