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Portfolio Performance of European Target Prices

Author

Listed:
  • Joana Almeida

    (KPMG, 1069-006 Lisbon, Portugal
    Disclaimer: The opinions here expressed are those of J.A. and not necessarily those of KPMG.)

  • Raquel M. Gaspar

    (ISEG, Universidade de Lisboa, 1200-781 Lisboa, Portugal
    REM/Cemapre Research Center, 1200-780 Lisboa, Portugal)

Abstract

This paper examines the performance of actively managed portfolios constructed using target price recommendations provided by analysts. We propose two methods for constructing portfolios based on Bloomberg’s 12-month target price consensus, which serves as a signal to buy or sell assets. Using a sample of 50 European stocks over a 19-year period (from 1 April 2004 to 31 March 2023), we compare the performance of target-price-based portfolios to traditional alternatives, such as a naïve homogeneous portfolio and the Eurostoxx 50 index, as well as to passive portfolios based on average recommendations. We also look into the mean-variance efficiency of these portfolios and find that all exhibit similar levels of efficiency, which are well below the performance of the theoretical tangent portfolios. Our results indicate that target-price-based portfolios show performance very close to that of the naïve homogeneous portfolio. Even the passive “average” target price portfolios, which require previous knowledge of targets for the entire investment period, are unable to outperform the naïve portfolio. Our main findings are based on a 15-year investment horizon but are robust when considering smaller maturities and out-of-sample data. We also investigate the impact of rebalancing on portfolio performance and find that it does pay off in the long run (over an 8-year investment period), but the frequency of rebalancing matters. Rebalancing only once a year is as detrimental to performance as not rebalancing at all. However, it is unclear whether the transaction costs associated with frequent rebalancing would offset any relative outperformance. Overall, our study contributes to the literature on portfolio management and market efficiency by demonstrating the potential benefits and limitations of using target price recommendations to construct portfolios, highlighting the importance of carefully considering rebalancing strategies to achieve optimal performance.

Suggested Citation

  • Joana Almeida & Raquel M. Gaspar, 2023. "Portfolio Performance of European Target Prices," JRFM, MDPI, vol. 16(8), pages 1-28, July.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:8:p:347-:d:1202231
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    References listed on IDEAS

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    1. Admati, Anat R & Pfleiderer, Paul, 1997. "Does It All Add Up? Benchmarks and the Compensation of Active Portfolio Managers," The Journal of Business, University of Chicago Press, vol. 70(3), pages 323-350, July.
    2. Reitz, Stefan, 2006. "On the predictive content of technical analysis," The North American Journal of Economics and Finance, Elsevier, vol. 17(2), pages 121-137, August.
    3. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    4. Steven J. Jordan, 2014. "Is momentum a self-fulfilling prophecy?," Quantitative Finance, Taylor & Francis Journals, vol. 14(4), pages 737-748, April.
    5. Menkhoff, Lukas, 1997. "Examining the Use of Technical Currency Analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 2(4), pages 307-318, October.
    6. Tao Li & Wenxiu (Vince) Nan & Jahangir Sultan, 2023. "Internal Relevance between Analysts’ Forecasts and Target Prices - Informativeness and Investment Value," Applied Economics, Taylor & Francis Journals, vol. 55(42), pages 4890-4910, September.
    7. Charfeddine, Lanouar & Benlagha, Noureddine & Maouchi, Youcef, 2020. "Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors," Economic Modelling, Elsevier, vol. 85(C), pages 198-217.
    8. Oberlechner, Thomas, 2001. "Importance of Technical and Fundamental Analysis in the European Foreign Exchange Market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 81-93, January.
    9. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    10. Bingxu Fang & Ole-Kristian Hope & Zhongwei Huang & Rucsandra Moldovan, 2020. "The effects of MiFID II on sell-side analysts, buy-side analysts, and firms," Review of Accounting Studies, Springer, vol. 25(3), pages 855-902, September.
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    More about this item

    Keywords

    target prices; portfolio performance; mean-variance theory;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles

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