Analyzing public sentiment to gauge key stock events and determine volatility in conjunction with time and options premiums

TL;DR

This paper introduces a novel financial algorithm that uses social media sentiment analysis to improve predictions of stock earnings and volatility, focusing on media influence and sentiment differences between Wall Street and the public.

Contribution

The study presents an integrated approach combining sentiment analysis, financial data, and media comparison to enhance stock prediction accuracy and understand media's role in market volatility.

Findings

Sentiment analysis improves stock prediction accuracy.

Media influence significantly impacts stock volatility.

Differences in sentiment between Wall Street and retail investors affect market outcomes.

Abstract

Analyzing stocks and making higher accurate predictions on where the price is heading continues to become more and more challenging therefore, we designed a new financial algorithm that leverages social media sentiment analysis to enhance the prediction of key stock earnings and associated volatility. Our model integrates sentiment analysis and data retrieval techniques to extract critical information from social media, analyze company financials, and compare sentiments between Wall Street and the general public. This approach aims to provide investors with timely data to execute trades based on key events, rather than relying on long-term stock holding strategies. The stock market is characterized by rapid data flow and fluctuating community sentiments, which can significantly impact trading outcomes. Stock forecasting is complex given its stochastic dynamic. Standard traditional prediction methods often overlook key events and media engagement, focusing its practice into long-term investment options. Our research seeks to change the stochastic dynamic to a more predictable environment by examining the impact of media on stock volatility, understanding and identifying sentiment differences between Wall Street and retail investors, and evaluating the impact of various media networks in predicting earning reports.