Decision support from financial disclosureswith deep neural networks and transfer learning
Table 3: Out-of-sample results from classifying the direction of the abnormal return. Values in bold indicate models that outperform both the naïve baseline and traditional machine learning.
['[BOLD] Method', '[BOLD] Training set  [BOLD] Accuracy', '[BOLD] Test set  [BOLD] Accuracy', '[BOLD] Test set  [BOLD] Balanced accuracy', '[BOLD] Test set  [BOLD] AUC', '[BOLD] Absolute improvement on test set over baseline  [BOLD] Accuracy', '[BOLD] Absolute improvement on test set over baseline  [BOLD] Balanced accuracy', '[BOLD] Absolute improvement on test set over baseline  [BOLD] AUC']
The random forest again scores well with a balanced accuracy of 0.542, but is beaten by 0.545 from the ridge regression. The latter achieves a balanced accuracy that is 4.5 percentage points higher than the naïve benchmark. This performance is obtained when α is set to 0.99.