Decision support from financial disclosureswith deep neural networks and transfer learning
Table 4: Out-of-sample results from regressing the nominal return. Bold values indicate models that outperform 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']
While the previous section studied the accuracy in terms of classifying the direction of stock price changes, we now incorporate the nominal magnitude of the price adjustment. With regard to the baseline models, only support vector regression yields favorable results in comparison to the naïve approach (which represents the mean return of the training set). Its performance stems from choosing a radial basis function kernel and setting the cost to 0.05. While the previous section studied the accuracy in terms of classifying the direction of stock price changes, we now incorporate the nominal magnitude of the price adjustment. With regard to the baseline models, only support vector regression yields favorable results in comparison to the naïve approach. Its performance stems from choosing a radial basis function kernel and setting the cost to 0.05.