AdaptiveReducedRankRegression

Thissettingfrequently arisesinpractice because it is often straightforward to perform feature-engineering and produce a large number of potentially useful features in many machine learning problems. For example, in a typical equity forecasting model,n is around 3,000 (i.e., using 10 years of market data), whereas the number of potentially relevant features can be in the order of thousands [36, 24, 26, 12].