This study examines the informational value of stock recommendations issued by social media analysts through the lens of Self-Determination Theory (SDT). Using comprehensive data from Seeking Alpha, we demonstrate that these recommendations significantly predict abnormal stock returns over a two-month horizon, with no subsequent reversal, suggesting that they convey incremental information not captured by sell-side analysts or textual sentiment. Guided by SDT, we further show that predictive accuracy varies systematically with analysts’ motivational contexts. Recommendations are more informative when issued by analysts identified as traders, whose direct financial stakes deepen the internalization of extrinsic incentives, and by analysts with high comment-section engagement, where interactive feedback supports needs for competence and relatedness. Conversely, predictive ability declines with longer platform tenure, consistent with motivational saturation as psychological needs become satisfied, and with higher posting frequency, where cognitive overload undermines need satisfaction. Finally, we find that both editorial selection and user-driven engagement effectively identify recommendations with greater ex-post informativeness. In an era of information abundance in online investment communities, our SDT-grounded analysis offers investors guidance for signal extraction and provides platform designers with principled insights for fostering higher-quality content ecosystems.