Do Emotions Matter? An Investigation of Human Emotions and Financial Decision Making in the Digital Era - Long, Cheng (Trinity College Dublin. School of Business. Discipline of Business & Administrative Studies, 2024)
This doctoral dissertation thoroughly examines the influence of social media sentiment on financial markets, focusing particularly on the GameStop short squeeze and the cryptocurrency market behavior. The dissertation includes three main research papers that together offer new insights into how discussions on internet forums can sway market trends in both stocks and cryp- tocurrencies. The first paper in this dissertation offers a detailed investigation of the GameStop short squeeze event, emphasizing the important role of social media platforms, with a particular focus on the r/WallStreetBets subreddit, in influencing the volatility and pricing of Gamestop stock price. A standout achievement of this study is the creation of a tailored Reddit dictionary based on VADER(Hutto and Gilbert (2014)), developed to examine the complex language and sentiment among investors on the forum. This innovative tool enables a more accurate analysis of how investor sentiment, particularly towards ’meme stocks,’ can lead to significant price fluctua- tions. Over 10.8 million textual data were collected from r/WallStreetBets, through a combination of qualitative and quantitative analysis, the paper demonstrates the direct impact of collective online sentiment on the stock market, challenging traditional financial theories by illustrating the power of social media. Followed with the Reddit-tailored VADER dictionary, the second paper progresses the discussion by developing a sophisticated sentiment analysis model specifically designed for the cryptocurrency markets. There is a research gap in the field of sentiment analysis. As general sentiment analysis tools are not able to capture the sentiments of specific terms in the special alternative finance market. By adopting a machine learning-based textual analysis approach, Logistic Regres- sion, Random Forest, and XGBoost were chosen based on their ability to tackle multiclass classification, given the diverse sentiments expressed across platforms like Reddit threads, posts, and Twitter tweets. The chosen optimal model is refined with a lexicon enriched with cryptocurrency-specific terminology, making it a novel instrument for precise mapping and evaluating sentiment trends within these digi- tal markets. The development of such a tool has substantial value to practitioners in the rapidly evolving world of cryptocurrency trading. In the third paper, the initial studies are expanded to examine the broader implications of sentiment analysis across the cryptocurrency market. More than 600 million text data are collected between January 1, 2018, to June 30, 2021, through various subchannels and keywords on Reddit and Twitter, to examine the intraday interconnectedness between crypto market sentiments and cryptocurrency price volatilities. This comprehensive analysis highlights the time-varying dynamic relationship between retail investor sentiment and cryptocurrency price volatility. It details the efficacy of high-frequency data in uncovering complex market patterns, sentiment-driven trading behaviors, and the interconnectedness of different cryptocurrencies. The results show that market sentiment is the net recipient of the network shocks overtime, both at low- and high- frequency. Market volatility, especially the prices volatility from Bitcoin and Ripple, play the shocks transmitter role in the network. By illustrating the critical role of timely and detailed data in determining market trends, the paper advances the field of sentiment analysis, proposing innovative methodologies for predicting market movements. This research underscores the importance of sentiment analysis in understanding the mechanisms of market volatility, especially in the fast-paced and increasingly growing cryptocurrency market. In summary, this dissertation clarifies the powerful effect that online discussions can have on market movements, covering both stock and cryptocurrency markets. It introduces new tools and methods for analyzing markets, and examines how the online dicussion act to the market volatility, providing valuable perspectives for investors, analysts, academics, and policymakers. This thesis offers an replicable methods to develop sentiment analysis tools for any specific fields, and a ready-to-use sentiment lexicon for cryptocurrency market. This research opens doors for further exploration in the ever-changing areas of behavioral finance and market analysis, aiming to deepen the academic comprehension of how social media sentiment influences financial markets.
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