Hands-On Machine Learning for Algorithmic Trading

Prediction versus inference

The functional relationship produced by a supervised learning algorithm can be used for inference—that is, to gain insights into how the outcomes are generated—or for prediction—that is, to generate accurate output estimates (represented by ) for unknown or future inputs (represented by X).

For algorithmic trading, inference can be used to estimate the causal or statistical dependence of the returns of an asset on a risk factor, whereas prediction can be used to forecast the risk factor. Combining the two can yield a prediction of the asset price, which in turn can be translated into a trading signal.

Statistical ...