Social Media

Intraday Trading Algorithm for Predicting Cryptocurrency Price Movements Using Twitter Big Data Analysis ArXiv ID: 2401.00603 “View on arXiv” Authors: Unknown Abstract Cryptocurrencies have emerged as a novel financial asset garnering significant attention in recent years. A defining characteristic of these digital currencies is their pronounced short-term market volatility, primarily influenced by widespread sentiment polarization, particularly on social media platforms such as Twitter. Recent research has underscored the correlation between sentiment expressed in various networks and the price dynamics of cryptocurrencies. This study delves into the 15-minute impact of informative tweets disseminated through foundation channels on trader behavior, with a focus on potential outcomes related to sentiment polarization. The primary objective is to identify factors that can predict positive price movements and potentially be leveraged through a trading algorithm. To accomplish this objective, we conduct a conditional examination of return and excess return rates within the 15 minutes following tweet publication. The empirical findings reveal statistically significant increases in return rates, particularly within the initial three minutes following tweet publication. Notably, adverse effects resulting from the messages were not observed. Surprisingly, sentiments were found to have no discerni-ble impact on cryptocurrency price movements. Our analysis further identifies that inves-tors are primarily influenced by the quality of tweet content, as reflected in the choice of words and tweet volume. While the basic trading algorithm presented in this study does yield some benefits within the 15-minute timeframe, these benefits are not statistically significant. Nevertheless, it serves as a foundational framework for potential enhance-ments and further investigations. ...

Social Media as a Bank Run Catalyst ArXiv ID: ssrn-4422754 “View on arXiv” Authors: Unknown Abstract After the run on Silicon Valley Bank (SVB) in March 2023, U.S. regional banks entered a period of significant distress. We quantify social media’s role in this Keywords: Silicon Valley Bank, Social media, Bank runs, Regional banks, Contagion Complexity vs Empirical Score Math Complexity: 3.0/10 Empirical Rigor: 8.0/10 Quadrant: Street Traders Why: The paper uses extensive Twitter data and robust econometric specifications (e.g., regression analyses with numerous controls, specification curves) to link social media exposure to bank run outcomes, demonstrating high empirical rigor. The mathematical content is relatively light, focusing on regression models and standard financial metrics rather than advanced theoretical derivations. flowchart TD A["Research Goal Quantify social media's role in SVB bank run"] --> B["Key Methodology High-frequency data analysis"] B --> C["Data / Inputs Social media volume & sentiment Bank stock prices & CDS spreads"] C --> D["Computational Process Causal inference & time-series regression models"] D --> E["Key Findings 1. Social media predicts withdrawals 2. Amplifies deposit flight 3. Material impact on bank stability"]