## 6 Conclusions  
Economic sentiment indicators are key for monitoring the current state of the economy and providing forward-looking information regarding imminent economic developments. In this paper, we propose a machine-learning method for sentiment indicator construction. The proposed approach allows us to find optimal combinations of a wide range of qualitative survey expectations that minimize the loss function and generate quantitative estimates of economic growth. By means of genetic algorithms, we obtained country-specific industry and consumer confidence indicators that allow for the monitoring of the dynamics of economic activity in nineteen European countries and the EA. First, when examining the obtained mathematical expressions, we observed that firms' production expectations for the months ahead and consumers' assessments about the general economic situation over the previous months are, respectively, the survey variables that most frequently appear in the evolved indicators, both lagged and contemporaneous. We also found that all questions of the consumer survey appeared in the indicators, while in the case of the industry survey, the distribution between variables is less uniform, with \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & \(h=1\) & & & \(h=4\) & \\ \hline  & **Evo.Exp** & **ARIMA** & **HLN** & **Evo.Exp** & **ARIMA** & **HLN** \\ \hline Netherlands & 0.287 & 0.734 & \(-1.773\) & 1.107 & 1.182 & 0.402 \\ Poland & 0.419 & 2.774 & \(-5.990\) & 0.842 & 2.743 & \(-2.458\) \\ Portugal & 0.643 & 1.335 & \(-1.157\) & 1.502 & 1.802 & \(-0.380\) \\ Romania & 0.543 & 3.254 & \(-8.019\) & 2.326 & 3.329 & \(-0.775\) \\ Slovenia & 0.543 & 2.380 & \(-5.018\) & 2.048 & 2.574 & \(-0.924\) \\ Spain & 0.711 & 1.630 & \(-0.929\) & 0.685 & 1.862 & \(-1.952\) \\ Sweden & 0.274 & 0.955 & \(-4.316\) & 0.572 & 1.804 & \(-6.157\) \\ United Kingdom & 0.383 & 0.861 & \(-2.978\) & 0.609 & 1.465 & \(-1.819\) \\ Euro Area & 0.426 & 1.102 & \(-0.867\) & 0.853 & 1.705 & \(-1.610\) \\ \hline \hline \end{tabular} Notes: \(h\) denotes the forecasting horizon. Evo.Exp refers to the iterative forecasts obtained with the proposed GP-based SR approach, and ARIMA to the iterative time-series forecasts used as benchmark. HLN denotes the Harvey-Leybourne–Newbold test statistic.  
\end{table} 
Table 4: _Cont._the two questions related to production being the most frequent. These results can be very useful when using data from business and consumer surveys for economic analysis. Second, we assessed the forecasting performance of the proposed indicators. On the one hand, we compared them to the confidence indicators constructed by the EC in a nowcasting exercise and found that the evolved expressions outperformed the scaled confidence indicators in most cases. On the other hand, we designed a recursive out-of-sample forecasting experiment in which we iteratively re-computed the indicators to track economic growth. We found that the proposed approach significantly outperformed univariate time-series models in terms of accuracy. The obtained results provide evidence regarding the ability of genetic programming to solve optimization problems related to economic modelling, and show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, from both the demand and the supply sides. This set of country-specific indicators can also be used to transform the qualitative expectations of firms and consumers into advanced estimates of national GDP growth, without making any assumptions regarding economic agents' behaviours. We want to note that due to the empirical nature of the proposed approach, the evolved expressions lack any theoretical background. In this sense, an issue left for further research is the introduction of restrictions in the design of the experiments with the objective of generating expressions that admit an economic interpretation.