Decision support from financial disclosureswith deep neural networks and transfer learning
Table 5: Out-of-sample results from regressing the abnormal return. Values in bold indicate an improvement compared to all baselines (i. e. naïve and traditional machine learning).
['[BOLD] Method', '[BOLD] Training set  [BOLD] RMSE', '[BOLD] Test set  [BOLD] RMSE', '[BOLD] Test set  [BOLD] MSE', '[BOLD] Test set  [BOLD] MAE', '[BOLD] Absolute error reduction on test set over baseline  [BOLD] RMSE', '[BOLD] Absolute error reduction on test set over baseline  [BOLD] MSE', '[BOLD] Absolute error reduction on test set over baseline  [BOLD] MAE']
Here the elastic net achieves the lowest mean squared error among the traditional machine learning models, amounting to 37.614, which is −0.930 below the naïve benchmark.