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.
Highlights
Investors aiming to invest in the stock market to buy a company’s stock face the challenge to select companies that will be successful in the future and whose stock will appreciate over time
This study aims to address this research gap by using mean target prices and measuring the accuracy of these consensus estimates as well as using classification methods to build a model to predict when mean target prices will be met and when they might be missed
From an investor’s perspective, it is interesting to note that the Year-End returns represent the returns achieved by investing in a stock at the time where the mean target price is updated and holding it for the 12-month period
Summary
Investors aiming to invest in the stock market to buy a company’s stock face the challenge to select companies that will be successful in the future and whose stock will appreciate over time. Taking an investor’s perspective, only the information related to target prices from 1 porting tools for ESG rating agencies (for instance, to provide higher quality and mor January 2009 until 30 June 2020 were considered (a year shorter than the entire period) and comprehensive data to better fit the measurement systems). Apart from that, it could compared with the actual stock prices after one year (1 January 2010 to 30 June 2021). Using these two perspectives for the accuracy of target price was taken in [2,7], whereas a focus on any point during the year—which is termed in this study “Year-Highest”—was pursued in [5,11]
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