A Genetic Programming Approach for Economic Forecasting with Survey Expectations

Abstract

We apply a soft computing method to generate country-specific economic sentiment indicators that provide estimates of year-on-year GDP growth rates for 19 European economies. First, genetic programming is used to evolve business and consumer economic expectations to derive sentiment indicators for each country. To assess the performance of the proposed indicators, we first design a nowcasting experiment in which we recursively generate estimates of GDP at the end of each quarter, using the latest business and consumer survey data available. Second, we design a forecasting exercise in which we iteratively re-compute the sentiment indicators in each out-of-sample period. When evaluating the accuracy of the predictions obtained for different forecast horizons, we find that the evolved sentiment indicators outperform the time-series models used as a benchmark. These results show the potential of the proposed approach for prediction purposes.

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

“…The Covid-19 pandemic wreaked havoc to previous macroeconomic models, and thereupon the need to establish accurate estimates is now, more than ever, evident [ 227 ]. Macroeconomic models are often utilized as a means to guide political and financial decisions [ 228 ] and by integrating SR into those approaches, possible relations between variables might come up to light.…”

“…Agents’ expectations regarding the economy’s condition are high-valued for economic modelling due to the fact that they contain explicit, multi-variate information about market [ 230 ] and usually obtained via tendency surveys (business and consumer surveys) [ 231 ]. As a result, approaches are made via SR to form a link between survey data and a successful economic growth model [ 227 , 232 ].…”

“…Another application of SR via GP has been in econometric modelling [ 233 ] where the conventional ”exchange equation” was reinvented. However, GP has not revealed its full prospective in the field, as being underused in macroeconomic modelling [ 227 ]. Researchers think that the polymeric generated expressions, who differ by those European Commission presents [ 232 ], will eventually escalate the situation by proving their superiority and update the currently equipped models.…”

Forecasting Selected Commodities’ Prices with the Bayesian Symbolic Regression

“…Interestingly, genetic algorithms have been used in various econometric models, and were claimed to be very useful (Claveria et al 2022;Garcia and Kristjanpoller 2019;Claveria et al 2016Claveria et al , 2017Mostafa and El-Masry 2016;Aguilar-Rivera et al 2015;Sermpinis et al 2015;Sheta et al 2013;Hasheminia and Niaki 2006). While widely popular in technical fields, such as engineering, and nature-oriented sciences, such as ecology and medicine (Dimoulkas et al 2018;Klotz et al 2017;Golafshani and Ashour 2016;Ceperic et al 2014;Narotam et al 2014;Sarradj and Geyer 2014), they have not yet been extensively applied in economics or finance, especially in the context of variable uncertainty, and particularly for forecasting commodity prices.…”