{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:11:16Z","timestamp":1775067076443,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>As climate change has become of eminent importance in the last two decades, so has interest in industry-wide carbon emissions and policies promoting a low-carbon economy. Investors and policymakers could improve their decision-making by producing accurate forecasts of relevant green finance market indices: carbon efficiency, clean energy, and sustainability. The purpose of this paper is to compare the performance of single-step univariate forecasts produced by a set of selected statistical and regression-tree-based predictive models, using large datasets of over 2500 daily records of green market indices gathered in a ten-year timespan. The statistical models include simple exponential smoothing, Holt\u2019s method, the ETS version of the exponential model, linear regression, weighted moving average, and autoregressive moving average (ARMA). In addition, the decision tree-based machine learning (ML) methods include the standard regression trees and two ensemble methods, namely the random forests and extreme gradient boosting (XGBoost). The forecasting results show that (i) exponential smoothing models achieve the best performance, and (ii) ensemble methods, namely XGBoost and random forests, perform better than the standard regression trees. The findings of this study will be valuable to both policymakers and investors. Policymakers can leverage these predictive models to design balanced policy interventions that support environmentally sustainable businesses while fostering continued economic growth. In parallel, investors and traders will benefit from an ease of adaptability to rapid market changes thanks to the computationally cost-effective model attributes found in this study to generate profits.<\/jats:p>","DOI":"10.3390\/computation13030076","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T13:07:51Z","timestamp":1741957671000},"page":"76","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluating Predictive Models for Three Green Finance Markets: Insights from Statistical vs. Machine Learning Approaches"],"prefix":"10.3390","volume":"13","author":[{"given":"Sonia","family":"Benghiat","sequence":"first","affiliation":[{"name":"Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3G IM8, Canada"}]},{"given":"Salim","family":"Lahmiri","sequence":"additional","affiliation":[{"name":"Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3G IM8, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.jclimf.2022.100002","article-title":"Using machine learning to predict clean energy stock prices: How important are market volatility and economic policy uncertainty?","volume":"1","author":"Sadorsky","year":"2022","journal-title":"J. Clim. Financ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"68976","DOI":"10.1007\/s11356-022-20670-8","article-title":"Can green finance improve carbon emission efficiency?","volume":"29","author":"Zhang","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"32926","DOI":"10.1007\/s11356-022-24514-3","article-title":"Has green finance optimized the industrial structure in China?","volume":"30","author":"Hu","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inteco.2022.05.004","article-title":"Green energy indices & financial markets: An in-depth look","volume":"171","author":"Nobletz","year":"2022","journal-title":"Int. Econ."},{"key":"ref_5","first-page":"187","article-title":"Deep Learning for Financial Time Series Prediction: A State-of-the-Art Review of Standalone and Hybrid Models","volume":"139","author":"Chen","year":"2024","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_6","first-page":"6924","article-title":"A systematic review on the relationship between stock market prediction model using sentiment analysis on Twitter based on machine learning method and features selection","volume":"95","author":"Kamaruddin","year":"2017","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_7","first-page":"100864","article-title":"Stock market prediction using artificial intelligence: A systematic review of systematic reviews","volume":"9","author":"Lin","year":"2024","journal-title":"Soc. Sci. Humanit. Open"},{"key":"ref_8","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2021). Exponential Smoothing. Forecasting: Principles and Practice, OTexts. [3rd ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/0304-4076(86)90063-1","article-title":"Generalized autoregressive conditional heteroskedasticity","volume":"31","author":"Bollerslev","year":"1986","journal-title":"J. Econom."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1016\/j.ins.2010.01.014","article-title":"A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting","volume":"180","author":"Cheng","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1016\/j.neucom.2008.09.029","article-title":"A combination of hidden Markov model and fuzzy model for stock market forecasting","volume":"72","author":"Hassan","year":"2009","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Majhi, R., Panda, G., Sahoo, G., Panda, A., and Choubey, A. (2008, January 1\u20136). Prediction of S&P 500 and DJIA stock indices using particle swarm optimization technique. Proceedings of the 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), Hong Kong, China.","DOI":"10.1109\/CEC.2008.4630960"},{"key":"ref_13","first-page":"2","article-title":"A comparison between regression, artificial neural networks and support vector machines for predicting stock market index","volume":"7","author":"Sheta","year":"2015","journal-title":"Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1061\/(ASCE)0887-3801(2001)15:3(208)","article-title":"Model induction with support vector machines: Introduction and applications","volume":"15","author":"Dibike","year":"2001","journal-title":"J. Comput. Civ. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1002\/ep.10317","article-title":"Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: A case study of Mashhad","volume":"28","author":"Noori","year":"2009","journal-title":"Environ. Prog. Sustain. Energy Off. Publ. Am. Inst. Chem. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.2166\/hydro.2013.134","article-title":"Improved annual rainfall-runoff forecasting using PSO\u2013SVM model based on EEMD","volume":"15","author":"Wang","year":"2013","journal-title":"J. Hydroinform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102732","DOI":"10.1016\/j.resourpol.2022.102732","article-title":"Forecasting the Chinese low-carbon index volatility","volume":"77","author":"Mei","year":"2022","journal-title":"Resour. Policy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"136959","DOI":"10.1016\/j.jclepro.2023.136959","article-title":"Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms","volume":"405","author":"Zhang","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118601","DOI":"10.1016\/j.apenergy.2022.118601","article-title":"Carbon price forecasting based on CEEMDAN and LSTM","volume":"311","author":"Zhou","year":"2022","journal-title":"Appl. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127783","DOI":"10.1016\/j.energy.2023.127783","article-title":"Carbon price forecasting based on secondary decomposition and feature screening","volume":"278","author":"Li","year":"2023","journal-title":"Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113692","DOI":"10.1016\/j.chaos.2023.113692","article-title":"An ensemble self-learning framework combined with dynamic model selection and divide-conquer strategies for carbon emissions trading price forecasting","volume":"173","author":"Yang","year":"2023","journal-title":"Chaos Solitons Fractals"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121977","DOI":"10.1016\/j.apenergy.2023.121977","article-title":"Incremental nonlinear trend fuzzy granulation for carbon trading time series forecast","volume":"352","author":"Xian","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Random Forests. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Boosting and Additive Trees. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1007\/s10462-019-09754-z","article-title":"A systematic review of fundamental and technical analysis of stock market predictions","volume":"53","author":"Nti","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_27","unstructured":"Box, G.E.P., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Linear Stationary Models. Time Series Analysis: Forecasting and Control, John Wiley and Sons Inc.. [5th ed.]."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S0169-2070(01)00110-8","article-title":"A state space framework for automatic forecasting using exponential smoothing methods","volume":"18","author":"Hyndman","year":"2002","journal-title":"Int. J. Forecast."},{"key":"ref_29","unstructured":"Breiman, L. (1998). Regression Trees. Classification and Regression Trees, Chapman & Hall."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201916, New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J. (2010). Moving Averages. International Encyclopedia of Statistical Science, Springer.","DOI":"10.1007\/978-3-642-04898-2_380"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/for.3980040103","article-title":"Exponential smoothing: The state of the art","volume":"4","author":"Gardner","year":"1985","journal-title":"J. Forecast."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.ijforecast.2006.03.005","article-title":"Exponential smoothing: The state of the art\u2014Part II","volume":"22","author":"Gardner","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_34","unstructured":"Shmueli, G., and Lichtendahl, K.C. (2018). Smoothing Methods. Practical Time Series Forecasting with R: A Hands-On Guide, Axelrod Schnall Publishers. [2nd ed.]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Additive Models, Trees, and Related Methods. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). Ensemble Learning. The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_37","unstructured":"(2024, June 30). XGBoost for Regression. Available online: https:\/\/machinelearningmastery.com\/xgboost-for-regression\/."},{"key":"ref_38","first-page":"200111","article-title":"A comprehensive review on multiple hybrid deep learning approaches for stock prediction","volume":"16","author":"Shah","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"ref_39","unstructured":"Bengio, Y. (2011, January 2). Deep learning of representations for unsupervised and transfer learning. Proceedings of the ICML Workshop on Unsupervised and Transfer Learning, JMLR Workshop and Conference Proceedings, 2012, Bellevue, WA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113464","DOI":"10.1016\/j.eswa.2020.113464","article-title":"Stock market movement forecast: A systematic review","volume":"156","author":"Bustos","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"115225","DOI":"10.1016\/j.eswa.2021.115225","article-title":"AutomaticAI\u2014A hybrid approach for automatic artificial intelligence algorithm selection and hyperparameter tuning","volume":"182","author":"Czako","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_42","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_43","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., and K\u00e9gl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, MIT Press. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2011\/file\/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3187","DOI":"10.1016\/j.matpr.2020.11.399","article-title":"A systematic review of stock market prediction using machine learning and statistical techniques","volume":"49","author":"Kumar","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"012065","DOI":"10.1088\/1742-6596\/2161\/1\/012065","article-title":"Machine Learning Approaches in Stock Price Prediction: A Systematic Review","volume":"2161","author":"Soni","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Makridakis, S., Spiliotis, E., and Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194889"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/3\/76\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:53:57Z","timestamp":1760028837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/3\/76"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["computation13030076"],"URL":"https:\/\/doi.org\/10.3390\/computation13030076","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]}}}