<![CDATA[The Technology sector is increasingly influenced by decentralized, real-time sentiment from retail investors disseminated via social media, fundamentally challenging market efficiency assumptions and raising systemic risk concerns (Guzman et al., 2025). This study performs a quantitative assessment to quantify the determinant influence of retail investor social media sentiment on technology sector stock price movements relative to conventional market indicators (trading volume and momentum). The investigation utilized a six-month dataset from five highly-traded technology stocks (AAPL, MSFT, NVDA, TSLA, AMD). Sentiment was accurately classified using a fine-tuned BERT model (Chen & Liu, 2024). The core analysis applied a novel PCA-Hidden Markov Model (PCA-HMM) framework used to mitigate multicollinearity and identify distinct market regimes (stable vs. volatile) followed by regime-switching multivariate regression (Zhou et al., 2025). The analysis reveals that social media sentiment is a significant predictor of daily stock returns (Novak & Smith, 2024). Crucially, the influence of sentiment was markedly magnified and more potent during the volatile market regime (Kim & Singh, 2024). This conditional effect confirms that sentiment acts as a powerful multiplier of price instability when the market is under stress. These findings necessitate the institutionalization of social media monitoring by investment practitioners for alpha generation (Taylor & Wirth, 2024) and by regulators for behavior-based surveillance to mitigate flash volatility and systemic risk (Rodriguez, 2025). The research advocates for the adoption of dynamic, regime-switching models in asset pricing and risk management.]]>
Copyrights © 2025
