## 4 Results
We applied the computed relative weights to combine firms' and consumers' expectations obtained from the evolved confidence indicators (Exp.Agg) and the scaled confidence indicators (Cof.Agg). We additionally computed Av.Cof.Agg as the average between the expectations obtained from the scaled confidence indicators. Results of the forecasting comparison are presented in the last five columns of Table 3.  
Figure 2: Cont. Figure 2: GDP and firms’ and consumers’ evolved confidence indicators. Notes: the black line represents the evolution of GDP growth, the grey dotted line the evolution of consumer confidence (Exp.CONS), and the dashed black line the evolution of industrial confidence (Exp.IND). The vertical line in 2016.Q1 marks the beginning of the out-of-sample period. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & **Industry** & \multicolumn{6}{c}{**Consumers**} & \multicolumn{6}{c}{**Aggregate**} \\ \hline  & **Exp.IND** & **Co.IND** & **HLN** & **Exp.CONS** & **Co.CONS** & **HLN** & **Exp.Agg** & **Co.Agg** & **ArCo.Agg** & **HLN(M)** & **HLN(M)** \\ \hline Austria & 1.097 & 1.186 & \  
Again, we can observe that in most cases, the lowest forecast errors are obtained with the aggregated expectations from the proposed confidence indicators (Exp.Agg), although the difference in accuracy is only statistically significant in seven countries (Belgium, Germany, Hungary, the Netherlands, Poland, Sweden, and the UK). We also found that data-driven weights improved the forecasting performance of the scaled confidence indicators. This forecasting exercise addresses the question about the information content of business and consumer survey expectations, and whether more sophisticated aggregation schemes based on machine learning could provide composite indicators that can better track economic activity. Our findings are in line with recent research by [53], who found that the use of optimized news-based sentiment values yielded accuracy gains for forecasting US industrial production. For Switzerland and Germany, [54] obtained improvements in accuracy of one-step-ahead GDP forecasts by augmenting benchmark autoregressive models with variations in the recession-word index. Similarly, [55] found that accounting for consumer and business sentiments led to the improved forecast accuracy of consumption in Indonesia. There is ample evidence that survey expectations are useful for predicting economic variables [56; 57; 58; 59; 60; 61]. In this sense, the obtained results are consistent with recent research regarding the predictive content of survey expectations. In [62], the authors showed the usefulness of diffusion indexes from the Markit survey in nowcasting and forecasting GDP in emerging markets by means of machine-learning and dimensionality-reduction techniques. Using qualitative survey responses from the ifo's World Economic Survey (WES), in [63], it was found that the respondents provided statistically significant directional forecasts. In [64], the authors used survey data from South Africa to investigate the accuracy of directional and point forecasts of investment, and found that for shorter horizons, survey forecasts enhanced by time-series data significantly improved point forecasting accuracy.