SFPAD: Sentiment-driven Firm Pricing Anomaly Detection Algorithm and Application

In January of 2021, the stock price of GameStop Corp ascended 20 times under the influence of social media in a mere two weeks’ time, becoming the first-ever case of sentiment-driven short squeeze. This caused certain stock prices to deviate significantly from their fundamental value, and hence, had an impact on the market efficiency. To help finance policymakers, regulators, and investment analysts to prepare for cases alike in the future, our study investigates a Machine Learning method of detecting sentiment-driven firm pricing anomaly using data collected from the GameStop frenzy. The proposed method combines anomaly pattern detection with sentiment analysis, which the latter is used to identify conditions that are considered crucial to the presence of sentiment-driven pricing. In our experiments, the adapted Machine Learning (SFPAD) method was able to outperform traditional detection technique when applied to historical time series.