**1. Introduction**

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. Brokerage firms spend a considerable amount of resources, including money, on stock analysis, recommendations, and target prices, which suggests that these institutions and their clients see value in such research [1,2]. For that reason, investors and academics alike have been interested in the value of sell-side analysts' reports [3]. In this context, sell-side analyst refers to analysts employed by financial institutions such as banks, brokers, and asset management firms, which also sell securities such as stocks to their clients. These analysts provide research reports on stocks to the clients of their institution [4], which contain information about the future of these companies [5]. Their reports frequently include three elements: (1) an earnings forecast, (2) a stock recommendation, and (3) a target price for the stock [5–7], which are the result of their own evaluation of a company [6]. Stock recommendations usually come in five distinct levels ("Strong Buy", "Buy", "Hold", "Sell", "Strong Sell") [1,4,5,8], whereas the target price is provided as a support for the stock recommendation and is explicitly mentioning the expected stock value [3,6,9], usually, for the next 12 months [2,7]. Target prices often accompany stock recommendations, but previous research suggests that not all analyst reports contain target prices [5]. In particular, their inclusion in reports is more likely in case of positive recommendations (e.g., 70% for upgrades vs. 35% for downgrades [3] or 84% for "Strong Buy"/79% "Buy" vs. 27% for "Hold" [6]). However, when target prices are included in a report, it is intuitive that higher target prices for stocks are generally associated with more favorable stock recommendations [6].

**Citation:** Lohrmann, C.; Lohrmann, A. Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies. *Sustainability* **2021**, *13*, 12746. https://doi.org/10.3390/su132212746

Academic Editors: Julian Scott Yeomans and Mariia Kozlova

Received: 5 October 2021 Accepted: 10 November 2021 Published: 18 November 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Previous research has covered different aspects of stock recommendations and target prices. This includes investigating the individual analyst's ability to make recommendations and set target prices [7,10,11] as well as the performance of recommendations of different institutions [8], and the value or abnormal returns associated with stock recommendations [1,12] even when analysts face conflicts of interest [13].

It was shown that, even though analysts appear to be reluctant to make "Sell" (and "Strong Sell") and "Hold" recommendations and tend to focus on "Buy" recommendations (and "Strong Buy") [3,5,14] (e.g., "Buy" and "Strong Buy" account for 70.8% [5] or 68% [3] of all recommendations), their recommendations appear to have value. In particular, there are stock price reactions to recommendations (and recommendation revisions) [14] and investors can benefit from such recommendations [1,4] e.g., by buying highly rated stocks and by selling lowly rated ones [1].

In terms of target prices, the link between target prices and stock recommendations [6], factors affecting the accuracy of target prices [2], the impact of price targets and recommendation revisions [3–5], the impact of different valuation models on the target price [9], and the dispersion of target prices as a risk measure [15] are examples of research works found in the literature. Moreover, research has indicated that target prices and target price revisions contain new and valuable information [3,5]. However, the fact that target prices may contain relevant information for the stock market and investors does not necessarily mean that target prices are accurate [11]. Moreover, as pointed out by Bonini et al. [2], the ability to forecast future stock prices using analyst target prices is a neglected topic in the literature. The accuracy of target prices, meaning whether stock price meet target prices after or during the forecast period (e.g., a 12-month period), as well as their (absolute) forecast error, meaning how far the stock prices are away from the predicted target prices, depends on different factors. First, in terms of the institutions issuing target prices, highly reputable institutions tend to issue more accurate target prices (those target prices with positive implied return only) [11]. The evidence towards individual analysts' ability to suggest accurate target prices is limited. Bradshaw, Brown, and Huang [7] find some statistical evidence supporting a persistent differential ability of analysts in terms of accurate target price predictions, but these were shown to be trivial economically. Besides, as may be expected, analyst-specific optimism has a negative impact on the accuracy of target prices [11]. This may be linked to the fact that analysts' target prices may be used strategically [11] e.g., to create a "hype" around a stock [5] and may not always reflect the actual belief of analysts (e.g., similar for recommendations where a "Buy" recommendation is issued instead of a more suitable "Hold"/"Sell" one [13]). In terms of analyst research, the level of detail of research reports is positively affecting the target price accuracy [11] and the number of analysts providing research appears to improve the information quality [16], which may potentially also affect the target price accuracy positively. In terms of the company covered, recommendations for stocks associated with a larger price-to-book value (P/B), which can be called "glamour" stocks (e.g., technology companies) show lower forecast accuracy [11], which may be problematic given that research suggests that sell-side analysts tend to recommend such stocks more often [12]. Apart from that, setting accurate target prices appears to be especially challenging for companies that are loss-making (not earning profits) [2]. Volatility appears to impact target price accuracy as well, with lower volatility of the stock price leading to a higher accuracy [7,11]. The positive development of the stock market as a whole also affects the accuracy of target prices positively [7], which is in line with the finding that the forecast error of analysts increases during negative market environments [17]. Lastly, in terms of the target price, the accuracy and magnitude of the forecast error seem to be higher the larger the difference between the target price and current stock price (implied growth in stock price) [2,5,11].

This research work focuses on the accuracy and predictive power of target prices, specifically consensus information, meaning mean target prices. As mentioned previously, research on target price accuracy is very limited. Apart from that, the vast majority of previous research on target price accuracy has centered on individual analysts and/or

individual target prices. There is some research on using consensus recommendations (e.g., the mean of recommendations) [1,12] but no research appears to have been done on using the consensus of target prices and determining the accuracy of such an aggregate estimate for the future stock price. In recent years private investors have also had easy and free access to many financial websites (e.g., Yahoo Finance, finanzen.net) that provide such mean target prices and related information [6] and make such an investigation also relevant for private investors, as well as academics and practitioners. Apart from that, no work appears to have been done using classification algorithms with target prices, which are very intuitive from an investors' perspective since they can be used for the binary decision (yes/no) whether to invest in a stock or to refrain from doing so. 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 (with embedded feature selection) to build a model to predict when mean target prices will be met and when they might be missed. Moreover, the variables that are relevant for the prediction will be determined to gain further insights into potential factors that may affect the probability that a mean target price is met.

The emphasis of this work is on clean energy stocks which have attracted increased attention due to the Paris Agreement [18] and the rise of clean energy technologies as a response to the threat imposed by climate change. The road to the Paris Agreement extended multiple years, starting from around 2009 with the Copenhagen Accord [19]. The agreement was adopted by 196 Parties (almost every nation) in December 2015 to address climate change and its harmful impacts, and about 190 of those countries formally approved it [20]. The agreement sets up an ambitious target to limit the increase in mean global temperature to well below 2 ◦C above pre-industrial levels by reducing global greenhouse gas emissions. Among other measures, this includes ramping up efforts to accelerate the implementation of clean and sustainable energy technologies.
