Outperforming the market: a comparison of Star and NonStar analysts' investment strategies and recommendations

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Vukovic D. B., Kurbonov O. O. U., Maiti M., ÖZER M., Radovanovic M.

HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS, vol.11, no.1, 2024 (AHCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.1057/s41599-023-02527-8
  • Journal Indexes: Arts and Humanities Citation Index (AHCI), Social Sciences Citation Index (SSCI), Scopus, Index Islamicus, Directory of Open Access Journals
  • Anadolu University Affiliated: Yes


We employ StarMine to investigate the impact of analyst recommendations on stock performance. We test whether star-ranked analysts generate abnormal returns and outperform non-stars in short and long portfolios. Utilizing buy-and-hold calendar-time portfolio methodology, we calculate portfolio alphas using various asset pricing models, including CPM, the Fama and French 3-factor model, and the Carhart 4-factor model. Results indicate that all analyst groups can generate abnormal returns exceeding the market average. Star-ranked analysts outperform non-stars in short portfolios by 0.5523% in monthly alpha, though no significant difference exists in long portfolio alphas. We also conduct regressor endogeneity tests and explore investor sentiment mechanisms by utilizing the GARCH model and frequency-domain causality analysis, with NASDAQ as a proxy for investor sentiment. These tests reveal that the momentum factor is exogenous, and investor sentiments have a statistically significant positive effect on stock return volatility, with changes occurring between 5 and 10 days. This research underscores the value of analyst insights for investors, validates StarMine's ranking effectiveness, and suggests market participants can benefit from incorporating analyst recommendations into their investment decisions. Our study makes a significant contribution to the existing literature by introducing a novel approach to understanding investor sentiment mechanisms through a causality model.