Metalabeling and the duality between cross-sectional and time-series factors
Ernie Chan (noreply@blogger.com)
By Ernest Chan and Akshay Nautiyal Features are inputs to supervised machine learning (ML)
models. In traditional finance, they are typically called “factors”, and they
are used in linear regression models to either explain or predict returns. In
the former usage, the factors are contemporaneous with the target returns,
while in the latter the factors must be from a prior period. There are generally two types of factors: cross-sectional vs
time-series. If you are modeling stock returns, cross-se