Predicting Intraday cryptocurrency returns – A Sparse Signals approach

Abstract

We test for the existence of sparse and short-lived signals in minute-by-minute cryptocurrency returns. Using a large set of linear as well as non linear predictors and a machine learning technique called the LASSO, we generate 1-minute ahead out of sample return forecasts for ten major cryptocurrencies. The forecasts obtained from the LASSO are statistically superior to those generated by the benchmark models. The LASSO based estimation selects predictors that are sparse and quite short lived.