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

Christoph Lohrmann and Alena Lohrmann
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Christoph Lohrmann: School of Business & Management, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland
Alena Lohrmann: School of Business & Management, LUT University, Yliopistonkatu 34, 53850 Lappeenranta, Finland

Sustainability, 2021, vol. 13, issue 22, 1-27

Abstract: 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.

Keywords: classification; feature selection; machine learning; financial market; investing; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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