Traditional asset‐pricing frameworks like the Capital Asset Pricing Model (CAPM) systematically fail to capture extreme price swings driven by retail‐led “meme” stock frenzies, broader coordination effects, and behavioral biases in financial markets. Jensen’s Alpha is a measure of the abnormal return of an asset relative to its expected CAPM return given its systematic risk (beta), with positive values indicating outperformance and negative values indicating underperformance, making it a direct gauge of CAPM mispricing. This thesis quantifies CAPM’s failure by estimating Jensen’s Alpha through rolling CAPM regressions on daily, weekly, and monthly data from 2014–2023 for five flagship meme stocks and a custom equal‐weighted meme‐stock index (MSI-EW). We complement descriptive statistics of Jensen’s Alpha with correlation analysis and univariate OLS regressions to examine the explanatory power of speculator-driven herding metrics, namely, the average cross-sectional standard deviation (CSSD) and average cross-sectional absolute deviation (CSAD) computed over the same rolling windows as Jensen’s Alpha, both benchmarked to a broad-market ETF (VTI) and to MSI-EW for comparison, alongside trading-volume proxies. Our findings reveal that average CSSD offers stronger explanatory power than average CSAD, with average CSSD benchmarked to the US total-market index ETF (VTI) explaining over 95 % of Alpha variation in GameStop and KOSS across all sampling frequencies, surpassing the explanatory power of average CSSD benchmarked to MSI-EW, while volatility and volume metrics add minimal incremental insight. Time-series visualizations further confirm that spikes in return dispersion coincide with abnormal return episodes. These results illuminate the behavioral drivers behind CAPM pricing errors and introduce a versatile, dispersion-based tool for understanding herding phenomena broadly, empowering businesses and investors to enhance their equity cost of capital calculations and forecasting accuracy in any market environment. Future research could extend this framework by incorporating multivariate behavioral and liquidity factors, leveraging intraday data and sentiment indices, and applying Granger-causality analysis to untangle the directional interplay between dispersion and mispricing.