A Momentum-Based Normalization Framework for Generating Profitable Analyst Sentiment Signals

The diverse rating scales used by brokerage firms pose significant challenges for aggregating analyst recommendations in financial research. We develop a momentum-based normalization framework that transforms heterogeneous rating changes into standardized sentiment signals using firm-relative, past-only empirical distribution functions with event-based lookback and expanding global quantile classification. Using 68,660 rating events from 270 brokerage firms covering 106 large-cap U.S. stocks (2019–2025), our approach generates statistically significant Buy–Sell spreads at all horizons: 1-month (0.96%, t = 3.07, p = 0.002), 2-month (1.36%, t = 3.07, p = 0.002), and 3-month (1.94%, t = 3.66, p < 0.001). Fama–French six-factor regressions confirm 13.6% annualized alpha for Buy signals (t = 3.81) after controlling for market, size, value, profitability, investment, and momentum factors. True out-of-sample validation on May–September 2025 data achieves 107% retention of in-sample 1-month performance (four of five months positive), indicating robust signal generalization. The framework provides a theoretically grounded and empirically validated methodology for standardizing analyst sentiment suitable for quantitative investment strategies and academic research.