Stock Price Prediction Under Anomalous Circumstances

TL;DR

This paper develops models to predict stock prices during extreme, unpredictable events like the COVID-19 pandemic by combining ARIMA, LSTM, and sentiment analysis, achieving high accuracy in volatile conditions.

Contribution

It introduces a novel approach integrating outlier detection, ARIMA, LSTM, and sentiment analysis to improve stock prediction during anomalous circumstances.

Findings

Models achieve 98% average prediction accuracy during crises.

Sentiment scores from Reddit comments enhance prediction performance.

Combining outlier detection with deep learning improves robustness.

Abstract

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.