This paper analyzes the value of analyst stock recommendations and changes in recommendations. It shows that analyst recommendations are generally biased towards "glamour" stocks—those with positive momentum, high growth, high volume, and relatively expensive characteristics. However, the level of analyst recommendations adds value only among stocks with favorable quantitative characteristics (e.g., high value and positive momentum). In contrast, changes in consensus recommendations are a robust predictor of returns, containing information orthogonal to other predictive variables. The study finds that analysts do not fully utilize available information signals in forming recommendations, and their recommendations are influenced by economic incentives. Analysts tend to favor stocks with growth characteristics, which may not always align with the interests of the investing public. Analyst recommendations often fail to incorporate the predictive power of contrarian indicators, which can negatively affect the investment value of recommendations. The study also finds that recommendation changes provide incremental value over recommendation levels. This is because recommendation changes are less affected by the growth bias that affects recommendation levels. The results suggest that recommendation changes may contain information that is largely orthogonal to other signals, or that they create their own price momentum as opinion leaders. The paper compares the predictive ability of consensus recommendation levels and changes. It finds that recommendation changes are more robust predictors of returns than recommendation levels. The study concludes that analyst recommendations and changes in recommendations can provide incremental value for investment strategies, particularly when combined with other predictive signals. However, the value of analyst recommendations is limited when used in isolation, as they are often biased towards glamour stocks and fail to incorporate contrarian indicators. The findings have implications for both academic research and practical investment strategies.This paper analyzes the value of analyst stock recommendations and changes in recommendations. It shows that analyst recommendations are generally biased towards "glamour" stocks—those with positive momentum, high growth, high volume, and relatively expensive characteristics. However, the level of analyst recommendations adds value only among stocks with favorable quantitative characteristics (e.g., high value and positive momentum). In contrast, changes in consensus recommendations are a robust predictor of returns, containing information orthogonal to other predictive variables. The study finds that analysts do not fully utilize available information signals in forming recommendations, and their recommendations are influenced by economic incentives. Analysts tend to favor stocks with growth characteristics, which may not always align with the interests of the investing public. Analyst recommendations often fail to incorporate the predictive power of contrarian indicators, which can negatively affect the investment value of recommendations. The study also finds that recommendation changes provide incremental value over recommendation levels. This is because recommendation changes are less affected by the growth bias that affects recommendation levels. The results suggest that recommendation changes may contain information that is largely orthogonal to other signals, or that they create their own price momentum as opinion leaders. The paper compares the predictive ability of consensus recommendation levels and changes. It finds that recommendation changes are more robust predictors of returns than recommendation levels. The study concludes that analyst recommendations and changes in recommendations can provide incremental value for investment strategies, particularly when combined with other predictive signals. However, the value of analyst recommendations is limited when used in isolation, as they are often biased towards glamour stocks and fail to incorporate contrarian indicators. The findings have implications for both academic research and practical investment strategies.