  The stock market is volatile and complicated, especially in 2020. Because of
a series of global and regional "black swans," such as the COVID-19 pandemic,
the U.S. stock market triggered the circuit breaker three times within one week
of March 9 to 16, which is unprecedented throughout history. Affected by the
whole circumstance, the stock prices of individual corporations also plummeted
by rates that were never predicted by any pre-developed forecasting models. It
reveals that there was a lack of satisfactory models that could predict the
changes in stocks prices when catastrophic, highly unlikely events occur. To
fill the void of such models and to help prevent investors from heavy losses
during uncertain times, this paper aims to capture the movement pattern of
stock prices under anomalous circumstances. First, we detect outliers in
sequential stock prices by fitting a standard ARIMA model and identifying the
points where predictions deviate significantly from actual values. With the
selected data points, we train ARIMA and LSTM models at the single-stock level,
industry level, and general market level, respectively. Since the public moods
affect the stock market tremendously, a sentiment analysis is also incorporated
into the models in the form of sentiment scores, which are converted from
comments about specific stocks on Reddit. Based on 100 companies' stock prices
in the period of 2016 to 2020, the models achieve an average prediction
accuracy of 98% which can be used to optimize existing prediction
methodologies.
