Stock Price Prediction Using Sentiment-Based LSTM: S&P500 Versus Reddit Posts

The stock market is as volatile as it is unpredictable, the unstable nature of the stock market results in fluctuations in stock prices and invariably, the market performance of stocks. Understanding the underlying factors that contribute to the volatility of the stock market, which has its consequences on stock prices, has become important to researchers and investors alike. Some of the methods that researchers have used in the past as a gauge for understanding market performance include analyzing economic conditions, understanding company performance, following geopolitical events and market trends. To contribute to the vast research field of stock price predictions and the challenge of understanding stock price fluctuations, this study will aim to find a relationship between human sentiments on the social media platform, Reddit, and the S&P 500 stock index. In this study, we will analyze posts from five subreddits that typically discuss the stock market and stock price fluctuations. This will form the first part of our dataset. Historical stock prices for the S&P 500 index will be obtained from Yahoo Finance. This will form our final dataset. Using Valence aware dictionary and sentiment reasoner (VADER), we will extract the sentiments within the five subreddits and categorize them into positive and negative sentiments. The historical stock prices from Yahoo Finance will be matched with the aggregate sentiments for each day and this data passed through the LSTM model for training. Our findings provide strong evidence of social media’s impact on stock price predictions.