## 5 Experimental Results
### ANN  
The ANN approach has been endorsed as an alternative technique to time series forecasting and has achieved immense popularity in the last few years. The main objective of ANN is to build a model for mimicking the intelligence of the human brain in a machine. Similar to the processed followed by a human brain, ANN will try to identify predictabilities and patterns within the input data, learn from past knowledge and then provide accurate estimates on new, unobserved data. Despite the fact that the development of ANN was mainly biologically motivated, they have been applied in numerous domains, primarily for forecasting and classification purposes. The main characteristic of ANN is that it is a data-driven and self-adaptive in nature method. There is no need to specify a particular model form or to make any a priori statement about the statistical distribution of data. Therefore, the desired model is adaptively formed and based on the features presented from the data. Despite the fact that ARIMA only supports univariate time series and therefore cannot cope with sentiment data from news or tweets, we initially carried out an evaluation of the aforementioned models upon only the closing price of each of the five stock indices, namely AAPL, GE, IBM, MSFT and ORCL. Data from each company were split into two subsets, i.e., a training set of the first 127 days and a test set of the remaining 10. Since all models are sensitive to internal parameters, such as p (order of the autoregressive model) and q (order of the moving average) for ARIMA, \(\epsilon\) (learning rate) for the ANN, C (misclassification coefficient) for SVM, etc., we applied a grid search approach that optimized these parameters on the training set. This approach searched among various combinations of the parameters for the model that minimized the RMSE, using 10-fold cross-validation on the first 127 days. Therefore, we ensured that the last 10 days used as the test set would never be known to any of the above models. Figure 7 tabulates the performance of each model expressed in RMSE, for each stock. In the parenthesis next to the stock's index, the average close price for the last 10 days test set is included. As regards [PERSON]'s _U2_ decomposition metric, the results support our aforementioned claim about the superiority and robustness of SVM and LR, since as shown in Table 3, they present the lowest _U2_ scores. Based on the above, we only consider the first two superior models, i.e., SVM and LR, throughout the further experiments that would examine if news and tweets can improve the prediction of the closing price of the next day, especially when considering time periods that have been identified from the rule extraction phase. Even though the obvious approach when comparing two forecasting models is to select the one that has the smaller error measurement based on one of the error measurements described above, we need to determine whether this difference is significant or basically due to the specific choice of data values in the sample. Therefore, each of the five forecasting models was compared to the others in terms of the Diebold-Mariano (DM) test [25]. Considering the null hypothesis to be as: \"both forecasting model have the same accuracy\", the DM test returns two metrics, i.e., a \(p\)-value, denoting that the hypothesis holds when close to one or does not hold when close to zero, and DM-statistics, measuring the squared errors of the two models. Negative values show that the squared errors of the model listed first are lower than those of the model listed last.