Classification-based machine learning is a supervised machine learning approach in which a model learns from given input data and classifies it according to new observations. Classification may be bi-class, such as identifying whether an option should be exercised or not, or multi-class, such as the direction of a price change, which can be either up, down, or unchanging.
In this section, we will look again at creating cross-asset momentum models by having the prices of four diversified assets predict the daily trend of JPM on a daily basis for the year of 2018. The prior 1-month and 3-month lagged returns of the S&P 500 stock index, the 10-year treasury bond index, the US dollar index, and gold prices will be used to fit the model for prediction. Our target variables consist of Boolean indicators, where a True value...