Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies

Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index...

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Autores principales:	Christoph Lohrmann, Alena Lohrmann
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	classification feature selection machine learning financial market investing sustainability Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/59d3440b34574a4ca8d519d12f16af6f
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spelling	oai:doaj.org-article:59d3440b34574a4ca8d519d12f16af6f2021-11-25T19:04:01ZAccuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies10.3390/su1322127462071-1050https://doaj.org/article/59d3440b34574a4ca8d519d12f16af6f2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12746https://doaj.org/toc/2071-1050Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities.Christoph LohrmannAlena LohrmannMDPI AGarticleclassificationfeature selectionmachine learningfinancial marketinvestingsustainabilityEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12746, p 12746 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification feature selection machine learning financial market investing sustainability Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	classification feature selection machine learning financial market investing sustainability Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Christoph Lohrmann Alena Lohrmann Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
description	Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities.
format	article
author	Christoph Lohrmann Alena Lohrmann
author_facet	Christoph Lohrmann Alena Lohrmann
author_sort	Christoph Lohrmann
title	Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
title_short	Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
title_full	Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
title_fullStr	Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
title_full_unstemmed	Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
title_sort	accuracy and predictive power of sell-side target prices for global clean energy companies
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/59d3440b34574a4ca8d519d12f16af6f
work_keys_str_mv	AT christophlohrmann accuracyandpredictivepowerofsellsidetargetpricesforglobalcleanenergycompanies AT alenalohrmann accuracyandpredictivepowerofsellsidetargetpricesforglobalcleanenergycompanies
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Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies

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