This paper documents novel investment value in analyst report text. Using 1.2 million reports from 2000-2023, I embed narratives with large language models (LLMs) and fit machine learning (ML) forecasts of future long-term returns. Portfolios formed on the report narrative forecasts earn sizable and significant performance that is incremental to analysts' numerical outputs and to a broad set of established factors and characteristic-based predictors. The effect is stronger after adverse news and is amplified for growth stocks with aggressive investment. To open the black box, I apply a Shapley decomposition that attributes portfolio performance to distinct topics. Analysts' strategic outlook contributes the most to portfolio performance, especially forward-looking fundamental assessments. Beyond providing direct evidence that analyst narratives contain value-relevant assessments that diffuse into price over time, this study illustrates how interpretable LLM-plus-ML pipelines can scale and augment human judgment in investment decisions.