A genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union
Abstract
In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms’ and households’ expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.
Keyword : genetic algorithms, sentiment indicators, qualitative expectations, forecasting, economic growth
How to Cite
Claveria, O., Monte, E., & Torra, S. (2021). A genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union. Technological and Economic Development of Economy, 27(1), 262-279. https://doi.org/10.3846/tede.2021.13989
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Girardi, A., Gayer, C., & Reuter, A. (2015). The role of survey data in nowcasting euro area GDP growth. Journal of Forecasting, 35(5), 400–418. https://doi.org/10.1002/for.2383
Harvey, D. I., Leybourne, S. J., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4
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Alexandridis, A. K., Kampouridis, M., & Cramer, S. (2017). A comparison of wavelet networks and genetic programming in the context of temperature derivatives. International Journal of Forecasting, 33(1), 21–47. https://doi.org/10.1016/j.ijforecast.2016.07.002
Altug, S., & Çakmakli, C. (2016). Forecasting inflation using survey expectations and target inflation: Evidence from Brazil and Turkey. International Journal of Forecasting, 32(1), 138–153. https://doi.org/10.1016/j.ijforecast.2015.03.010
Álvarez-Díaz, M. (2020). Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods. Empirical Economics, 59(3), 1285–1305. https://doi.org/10.1007/s00181-019-01665-w
Álvarez-Díaz, M., & Álvarez, A. (2003). Forecasting exchange rates using genetic algorithms. Applied Economics Letters, 10(6), 319–322. https://doi.org/10.1080/13504850210158250
Álvarez-Díaz, M., & Álvarez, A. (2005). Genetic multi-model composite forecast for non-linear prediction of exchange rates. Empirical Economics, 30(3), 643–663. https://doi.org/10.1007/s00181-005-0249-5
Ardia, D., Bluteau, K., & Boudt, K. (2019). Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values. International Journal of Forecasting, 35(4), 1370–1386. https://doi.org/10.1016/j.ijforecast.2018.10.010
Breitung, J., & Schmeling, M. (2013). Quantifying survey expectations: What’s wrong with the probability approach? International Journal of Forecasting, 29(1), 142–154. https://doi.org/10.1016/j.ijforecast.2012.07.005
Chen, X., Pang, Y., & Zheng, G. (2012). Macroeconomic forecasting using GP based vector error correction model. In J. Wang (Ed.), Business intelligence in economic forecasting: technologies and techniques (pp. 1–15). IGI Global. https://doi.org/10.4018/978-1-61350-456-7.ch317
Claveria, O., Monte, E., & Torra, S. (2017). Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis. Journal of Applied Economics, 20(2), 329–349. https://doi.org/10.1016/S1514-0326(17)30015-6
Claveria, O., Monte, E., & Torra, S. (2018). A data-driven approach to construct survey-based indicators by means of evolutionary algorithms. Social Indicators Research, 135(1), 1–14. https://doi.org/10.1007/s11205-016-1490-3
Claveria, O., Monte, E., & Torra, S. (2019). Evolutionary computation for macroeconomic forecasting. Computational Economics, 53(2), 833–849. https://doi.org/10.1007/s10614-017-9767-4
Claveria, O., Monte, E., & Torra, S. (2020). Economic forecasting with evolved confidence indicators. Economic Modelling, 93, 576–585. https://doi.org/10.1016/j.econmod.2020.09.015
Claveria, O., Pons, E., & Ramos, R. (2007). Business and consumer expectations and macroeconomic forecasts. International Journal of Forecasting, 23(1), 47–69. https://doi.org/10.1016/j.ijforecast.2006.04.004
Cuevas, E., Zaldivar, D., & Perez-Cisneros, M. (2016). Applications of evolutionary computation in image processing and pattern recognition. Springer International Publishing. https://doi.org/10.1007/978-3-319-26462-2
de Carvalho Filho, A. O., de Sampaio, W. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M. (2014). Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artificial Intelligence in Medicine, 60(3), 165–177. https://doi.org/10.1016/j.artmed.2013.11.002
Diebold, F. X., & Mariano, R. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13(3), 253–263. https://doi.org/10.1080/07350015.1995.10524599
Doronina Koltan, N., Girdzijauskas, S., & Štreimikienė, D. (2013). Market capacity and consumer behaviour from logistic analysis view. Technological and Economic Development of Economy, 19(3), 448–464. https://doi.org/10.3846/20294913.2013.823134
Dubinskas, P., & Stungurienė, S. (2010). Alterations in the financial markets of the Baltic countries and Russia in the period of economic downturn. Technological and Economic Development of Economy, 16(3), 502–515. https://doi.org/10.3846/tede.2010.31
Duda, J., & Szydło, S. (2011). Collective intelligence of genetic programming for macroeconomic forecasting. In P. Jędrzejowicz, N.T. Nguyen, & K. Hoang (Eds.), Computational collective intelligence. Technologies and applications (pp. 445–454). Springer. https://doi.org/10.1007/978-3-642-23938-0_45
Eliiyi, D. T., Korkmaz, A. G., & Çiçek, A. E. (2009). Operational variable job scheduling with eligibility constraints: A randomized constraint-graph-based approach. Technological and Economic Development of Economy, 15(2), 245–266. https://doi.org/10.3846/1392-8619.2009.15.245-266
European Commission. (2019). Business and consumer surveys. https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-databases/business-and-consumer-surveys_en
Eurostat. (2019). Database. http://ec.europa.eu/eurostat/web/lfs/data/database
Fernández, E., Rangel-Valdez, N., Cruz-Reyes, L., Gomez-Santillan, C., Rivera-Zarate, G., & SanchezSolis, P. (2019). Inferring parameters of a relational system of preferences from assignment examples using an evolutionary algorithm. Technological and Economic Development of Economy, 25(4), 693–715. https://doi.org/10.3846/tede.2019.9475
Fortin, F. A., De Rainville, F. M., Gardner, M. A., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(1), 2171–2175. http://www.jmlr.org/papers/v13/fortin12a.html
Gelper, S., & Croux, C. (2010). On the construction of the European economic sentiment indicator. Oxford Bulletin of Economics and Statistics, 72(1), 47–62. https://doi.org/10.1111/j.1468-0084.2009.00574.x
Girardi, A., Gayer, C., & Reuter, A. (2015). The role of survey data in nowcasting euro area GDP growth. Journal of Forecasting, 35(5), 400–418. https://doi.org/10.1002/for.2383
Harvey, D. I., Leybourne, S. J., & Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2), 281–291. https://doi.org/10.1016/S0169-2070(96)00719-4
Hastie, T., Tibshirani, R., & Friedman, J. (2017). The elements of statistical learning: Data mining, inference and prediction (2nd ed). Springer Series in Statistics. https://doi.org/10.1007/978-0-387-84858-7
Ibrahim, D. (2016). An overview of soft computing. Procedia Computer Science, 102, 34–38. https://doi.org/10.1016/j.procs.2016.09.366
Jović, S., Danesh, A. S., Younesi, E., Aničić, O., Petković, D., & Shamshirband, S. (2016). Forecasting of underactuated robotic finger contact forces by support vector regression methodology. International Journal of Pattern Recognition and Artificial Intelligence, 30(7), 1–11. https://doi.org/10.1142/S0218001416590199
Juhro, S. M., & Iyke, B. N. (2020). Consumer confidence and consumption in Indonesia. Economic Modelling, 89, 367–377. https://doi.org/10.1016/j.econmod.2019.11.001
Katebi, J., Shoaei-parchin, M., Shariati, M., Trung, N. T., & Khorami, M. (2020). Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures. Engineering with Computers, 36, 1539–1558. https://doi.org/10.1007/s00366-019-00780-7
Kotanchek, M. E., Vladislavleva, E. Y., & Smits, G. F. (2010). Symbolic regression via genetic programming as a discovery engine: Insights on outliers and prototypes. In R. Riolo, U. M., O’Reilly, & T. McConaghy (Eds.), Genetic and evolutionary computation: Vol. 8. Genetic programming theory and practice VII (pp. 55–72). Springer Science+Business Media, LLC. https://doi.org/10.1007/978-1-4419-1626-6_4
Koza, J. R. (1992). Genetic programming: On the programming of computers by means of natural selection. MIT Press. https://mitpress.mit.edu/books/genetic-programming
Kronberger, G., Fink, S., Kommenda, M., & Affenzeller, M. (2011). Macro-economic time series modeling and interaction networks. In C. Di Chio et al. (Eds.), Lecture notes in computer science: Vol. 6625. Applications of evolutionary computation (pp. 101–110). Springer. https://doi.org/10.1007/978-3-642-20520-0_11
Kwiatkowski, J. W. (1992). Algorithms for index tracking. IMA Journal of Management Mathematics, 4(3), 279–299. https://doi.org/10.1093/imaman/4.3.279
Lacová, Ž., & Král, P. (2015). Measurement and characteristics of enterprise inflation expectations in Slovakia. Procedia Economics and Finance, 30, 505–512. https://doi.org/10.1016/S2212-5671(15)01262-9
Larkin, F., & Ryan, C. (2008). Good news: Using news feeds with genetic programming to predict stock prices. In M. O’Neil et al. (Eds.), Lecture notes in computer science. Vol. 4971. Genetic programming (pp. 49–60). Springer-Verlag. https://doi.org/10.1007/978-3-540-78671-9_5
Macias-Escobar, T., Cruz-Reyes, L., Dorronsoro, B., Fraire-Huacuja, H., Rangel-Valdez, N., & GomezSantillan, C. (2019). Application of population evolvability in a hyper-heuristic for dynamic multiobjective optimization. Technological and Economic Development of Economy, 25(5), 951–978. https://doi.org/10.3846/tede.2019.10291
Marković, D., Petković, D., Nikolić, V., Milovančević, M., & Petković, B. (2017). Soft computing prediction of economic growth based in science and technology factors. Physica A, 465, 217–220. https://doi.org/10.1016/j.physa.2016.08.034
Nikolić, V., Mitić, V. V., Kocić, L., & Petković, D. (2017). Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique. Knowledge and Information Systems, 52, 255–265. https://doi.org/10.1007/s10115-016-1006-0
Pan, X., Uddin, M. K., Ai, B., Pan, X., & Saima, U. (2019). Influential factors of carbon emissions intensity in OECD countries: Evidence from symbolic regression. Journal of Cleaner Production, 220, 1194–1201. https://doi.org/10.1016/j.jclepro.2019.02.195
Petković, D., Jovic, S., Anicic, O., Nedic, B., & Pejovic, B. (2016). Analyzing of flexible gripper by computational intelligence approach. Mechatronics, 40, 1–16. https://doi.org/10.1016/j.mechatronics.2016.09.001
Petković, D., Nikolić, V., Mitić, V. V., & Kocić, L. (2017). Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorithms. Flow Measurement and Instrumentation, 54, 172–176. https://doi.org/10.1016/j.flowmeasinst.2017.01.007
Safa, M., Sari, P. A., Shariati, M., Suhatril, M., Trung, N. T., Wakil, K., & Khorami, M. (2020). Development of neuro-fuzzy and neuro-bee predictive models for prediction of the safety factor of ecoprotection slopes. Physica A: Statistical Mechanics and its Applications, 550, 124046. https://doi.org/10.1016/j.physa.2019.124046
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