This article synthesizes and extends two recent empirical studies on the predictive capacity of the PPP-derived return framework applied to U.S. technology stocks. The first study examined a six-month horizon (August 2025–February 2026) and found that the Stock Internal Rate of Return (SIRR) explained approximately 20% of cross-sectional return variation. The second study extended the horizon to two years (February 2024–February 2026) and showed that the more comprehensive Stock Internal Rate of Return Including Price Appreciation (SIRRIPA) explained between 39% and 55% of realized performance dispersion. The present article compares these two analyses within a unified statistical framework and contrasts them with the traditional P/E ratio, whose predictive capacity remains statistically insignificant across both horizons. Using Pearson correlation, OLS regression, Spearman rank tests, quintile spreads, and formal null-hypothesis testing, we demonstrate that predictive strength increases materially with time within the return-space framework, while price-space multiples, as represented by the P/E ratio, fail to exhibit comparable converge in explanatory power. The findings support the structural superiority of PPP-derived return metrics over static valuation ratios.