Understanding the Price Impact of Coordinated Retail Investors
Open Access DepositedAn NLP Study on r/WallStreetBets
This study explores the effectiveness of hybrid models in predicting meme stock prices and returns by incorporating coordinated retail investor signals from social media platforms, specifically r/WallStreetBets. Motivated by extreme market volatilities, such as the GameStop (GME) rallies in 2021 and 2024, which exposed vulnerabilities in market efficiency during coordinated retail trading. This study starts with statistical models (ARIMA and MLR) and deep learning models (RNN, LSTM, and GRU) as the baseline models. It then uses sentiment analysis models (FinBERT, LLaMA-3, GPT-2, RoBERTa, SieBERT, and VADER) to produce sentiment scores of the r/WallStreetBets posts. In parallel, participation metrics such as comment volume, upvote ratios, and post scores are used as proxies for the level of coordinated retail trading activities. These features are integrated into deep learning models and evaluated using both statistical error metrics and financial performance metrics. Empirical findings demonstrate that GRU models consistently outperform traditional baseline methods on historical price data, while the incorporation of Reddit-derived social media signals provides strong predictive accuracy and investment utility across all architectures. Notably, participation metrics exhibit stronger explanatory power than sentiment scores alone, underscoring the importance of user engagement in reflecting retail investor coordination. Hyperparameter optimization further improves model performance, particularly within LSTM architectures, yielding superior Sharpe Ratios and reduced drawdowns.
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