quantlab.model.evaluation.cross_sectional_ic

quantlab.model.evaluation.cross_sectional_ic(pred, target)

Return the mean over time of the per-row Pearson correlation.

The mean runs over the rows cross_sectional_ic_series does not skip (see there for when a row is skipped). NaN is returned when every row is skipped.

Parameters:

predarray_like

A 2-D [T, S] panel of predictions.

targetarray_like

A 2-D [T, S] panel of realised values.

Raises:

ValueError

If the inputs are not 2-D or differ in shape.

Examples

>>> pred = np.array([[3.0, 1.0, 2.0], [1.0, 3.0, 2.0]])
>>> target = np.array([[9.0, 1.0, 4.0], [1.0, 9.0, 4.0]])
>>> cross_sectional_ic(pred, target)
0.989743318610787