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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Christoph Lohrmann & Alena Lohrmann, 2021. "Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies," Sustainability, MDPI, vol. 13(22), pages 1-27, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:22:p:12746-:d:681740
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    References listed on IDEAS

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    1. Womack, Kent L, 1996. "Do Brokerage Analysts' Recommendations Have Investment Value?," Journal of Finance, American Finance Association, vol. 51(1), pages 137-167, March.
    2. Matteo Foglia & Eliana Angelini, 2020. "Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era," Sustainability, MDPI, vol. 12(23), pages 1-22, November.
    3. Ahmad, Wasim, 2017. "On the dynamic dependence and investment performance of crude oil and clean energy stocks," Research in International Business and Finance, Elsevier, vol. 42(C), pages 376-389.
    4. Kenneth Merkley & Roni Michaely & Joseph Pacelli, 2017. "Does the Scope of the Sell-Side Analyst Industry Matter? An Examination of Bias, Accuracy, and Information Content of Analyst Reports," Journal of Finance, American Finance Association, vol. 72(3), pages 1285-1334, June.
    5. Stefano Bonini & Laura Zanetti & Roberto Bianchini & Antonio Salvi, 2010. "Target Price Accuracy in Equity Research," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 37(9‐10), pages 1177-1217, November.
    6. repec:bla:jfinan:v:58:y:2003:i:5:p:1933-1968 is not listed on IDEAS
    7. repec:bla:jfinan:v:59:y:2004:i:3:p:1083-1124 is not listed on IDEAS
    8. Jegadeesh, Narasimhan & Kim, Woojin, 2006. "Value of analyst recommendations: International evidence," Journal of Financial Markets, Elsevier, vol. 9(3), pages 274-309, August.
    9. Ahmad, Wasim & Sadorsky, Perry & Sharma, Amit, 2018. "Optimal hedge ratios for clean energy equities," Economic Modelling, Elsevier, vol. 72(C), pages 278-295.
    10. Bondia, Ripsy & Ghosh, Sajal & Kanjilal, Kakali, 2016. "International crude oil prices and the stock prices of clean energy and technology companies: Evidence from non-linear cointegration tests with unknown structural breaks," Energy, Elsevier, vol. 101(C), pages 558-565.
    11. Asquith, Paul & Mikhail, Michael B. & Au, Andrea S., 2005. "Information content of equity analyst reports," Journal of Financial Economics, Elsevier, vol. 75(2), pages 245-282, February.
    12. Brad Barber & Reuven Lehavy & Maureen McNichols & Brett Trueman, 2001. "Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns," Journal of Finance, American Finance Association, vol. 56(2), pages 531-563, April.
    13. Song, Yingjie & Ji, Qiang & Du, Ya-Juan & Geng, Jiang-Bo, 2019. "The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets," Energy Economics, Elsevier, vol. 84(C).
    14. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
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