By Group "FIN-TANSTIC WIZARDS"
This blog delves into the existing literature and research papers that have inspired our research topic, focusing on the impact of social media divergence on financial markets. Specifically, we explore the contrasting effects on traditional stocks versus Bitcoin. Our research process will systematically narrow down from a broad set of ideas and variables to finalize the scope of our paper, as outlined below.
Initial Idea Generation
At the outset, each team member presented their respective ideas, drawing from relevant studies in the field. One suggestion stemmed from the paper "Predicting Fraudulent ICOs Using Information Extracted from Whitepapers" by Dürr et al. (2020). This paper uses natural language processing (NLP) and machine learning (ML) techniques to predict fraudulent ICOs (Initial Coin Offerings) based on the content of whitepapers. The strength of this idea lies in the innovative application of NLP and ML for fraud detection, offering practical utility for investors. However, due to the difficulty in obtaining ICO whitepaper text data and the challenge posed by the large volume of textual information, we decided to abandon this topic after assessing its feasibility.
Another proposal was inspired by Dutta’s (2024) paper "Social Media Sentiment and Stock Market Volatility: Evidence from US Hi-Tech Companies", which examines the effects of social media sentiment—measured by Twitter-based uncertainty indexes—on the stock market volatility of US technology companies. The appeal of this idea lies in its direct relevance to market participants, given the increasing reliance on social media data for investment decision-making.
One team member suggested exploring a more nuanced topic: the impact of social media divergence on financial markets. This idea drew inspiration from "Social Media Disagreement and Financial Markets: A Comparison of Stocks and Bitcoin" by Akarsu & Yilmaz (2024), which investigates how divergence in social media discussions influences market volatility and trading volume, comparing traditional stocks with Bitcoin. This approach is unique in its emphasis on disagreement rather than sentiment and introduces a comparison between two distinct asset classes. This idea holds significant potential as it offers a more comprehensive understanding of how different types of assets react to social media discussions.
Comparison of Methodologies
The two key studies that inspired our project—Dutta's (2024) analysis of social media sentiment on stock market volatility and Akarsu & Yilmaz's (2024) investigation into social media disagreement—both examine the relationship between social media activity and financial market volatility, but they approach the analysis in different ways, each offering its own advantages.
Dutta’s study is grounded in the use of Twitter-based uncertainty indexes (TEU), which measure the degree of market uncertainty based on the frequency of uncertainty-related terms in Twitter posts. These terms include words like uncertain, uncertainty, economic, and finance. The TEU index specifically aims to quantify the level of economic and market uncertainty expressed by Twitter users by analyzing the occurrence of these keywords over time. This approach is useful in capturing broad shifts in sentiment, providing a measure of collective uncertainty in the market. However, the core of this method is its reliance on the presence of specific uncertainty-related terms, which can sometimes oversimplify the underlying complexity of market sentiment. While it highlights general ambiguity in the market, it doesn’t account for the nuanced responses from differing investor views. It reflects a broad measure of “uncertainty,” but it lacks the capacity to reveal when investors are not merely uncertain, but are actively divided in their perspectives.
In contrast, Akarsu & Yilmaz (2024) focus on disagreement in social media discussions, which is defined as the divergence of opinions or views expressed by investors on platforms like Reddit. Disagreement goes beyond the emotional intensity or sentiment polarity (positive vs. negative); it reflects the balance and tension between conflicting perspectives, particularly between optimistic and pessimistic viewpoints on a financial asset. This divergence is quantified by calculating the standard deviation of sentiment scores within Reddit discussions—specifically from the r/stocks and r/Bitcoin subreddits. Each comment is analyzed for sentiment using tools such as VADER and TextBlob, and a binary sentiment index is created for each post. The average sentiment score for each day is calculated, and the standard deviation of this index serves as the measure of disagreement. A higher standard deviation indicates greater disagreement among the participants, suggesting more polarized views on market conditions.
Why Disagreement Offers a More Comprehensive Measure
The key difference between the uncertainty captured by the Twitter-based indexes and the disagreement measured by Akarsu & Yilmaz lies in the nature of the data and the insights they provide. While the TEU uncertainty indexes quantify the frequency of uncertainty-related terms, they do not capture the balance or tension between differing opinions. A market may be uncertain, but the presence of both positive and negative views can signify something more profound—disagreement. Investors' decisions often hinge on such conflicting perspectives, which can drive greater trading volumes and volatility. The disagreement measure captures this divergence and highlights a more dynamic aspect of market psychology—when investors are not just uncertain but actively at odds with each other.
By focusing on the disagreement rather than simply the uncertainty of market sentiment, we gain a deeper understanding of market dynamics. Disagreement directly reflects the opposing forces in the market—the "resistance" between bulls and bears, optimism and pessimism. This contrast is critical for predicting market movements, as sharp divisions in opinion often lead to increased trading as investors react to opposing viewpoints. It taps into the psychological push and pull that drives markets, unlike uncertainty measures that only capture a broad sense of unease without differentiating between the strength of competing opinions.
Furthermore, the disagreement approach offers a more granular perspective, combining sentiment analysis with a focus on the balance of viewpoints. By measuring the standard deviation of sentiment, this method allows for a more nuanced interpretation of social media discussions. It can detect when sentiment is not just extreme (positive or negative), but when it is divided, signaling a potential turning point in the market. This is a valuable tool for understanding volatility, as disagreement often precedes major price swings, as traders react to or capitalize on opposing beliefs.
Further Exploration and Literature Review
After all team members presented their ideas, we thoroughly reviewed the relevant literature to gain a comprehensive understanding of the strengths, limitations, and feasibility of each concept. For the first proposal, we acknowledged the innovation in using NLP and ML, but we also noted that it's difficult to implement. The second idea has the drawback that the method of measuring market uncertainty has certain limitations.
Given these considerations, we decided to pursue the third idea—"Social Media Disagreement and Financial Markets: A Comparison of Stocks and Bitcoin". This decision was driven by several factors: the uniqueness of focusing on disagreement rather than sentiment, the comparative analysis of stocks and Bitcoin, and the practical relevance of understanding how divergent social media discussions influence market behavior. Unlike sentiment analysis, which aggregates opinions, examining divergence allows for a deeper understanding of how conflicting viewpoints can shape market outcomes, particularly in terms of volatility and trading volume.
Furthermore, our focus on comparing traditional stocks and Bitcoin provides a broader lens for assessing the dynamics of market behavior across different asset classes. This comparative approach adds richness to the study, as it allows for an exploration of how distinct assets respond differently to similar social media discourse. Additionally, the data sources—especially regarding Bitcoin and its social media community—are relatively novel and not as widely used in mainstream financial research, giving this study an edge in terms of both originality and depth.
Reference
- Dürr, A., Griebel, M., Welsch, G., & Thiesse, F. (2020). Predicting fraudulent initial coin offerings using information extracted from whitepapers. Twenty-Eighth European Conference on Information Systems (ECIS2020), Marrakesh, Morocco.
- Dutta, A. (2024). Social media sentiment and stock market volatility: Evidence from the US hi-tech companies. International Journal of Professional Business Review, 9(10), 01-17.
- Akarsu, S., & Yilmaz, N. (2024). Social media disagreement and financial markets: A comparison of stocks and Bitcoin. Economics and Business Review, 10(4), 189-213.