Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges

• URL: http://arxiv.org/abs/2404.17227v2
• Date: Fri, 20 Dec 2024 18:03:21 GMT
• Title: Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges
• Authors: Xintong Wu, Wanlin Deng, Yutong Quan, Luyao Zhang,
• Abstract summary: The collapse of FTX, a major CEX, provides a unique natural experiment to examine the measurable impacts of trust and its sudden erosion on the cryptocurrency ecosystem. This research investigates the impacts of the FTX collapse on user trust, focusing on token valuation, trading flows, and sentiment dynamics.
• Score: 1.9624273277521183
• License:
• Abstract: In the rapidly evolving cryptocurrency landscape, trust is a critical yet underexplored factor shaping market behaviors and driving user preferences between centralized exchanges (CEXs) and decentralized exchanges (DEXs). Despite its importance, trust remains challenging to measure, limiting the study of its effects on market dynamics. The collapse of FTX, a major CEX, provides a unique natural experiment to examine the measurable impacts of trust and its sudden erosion on the cryptocurrency ecosystem. This pivotal event raised questions about the resilience of centralized trust systems and accelerated shifts toward decentralized alternatives. This research investigates the impacts of the FTX collapse on user trust, focusing on token valuation, trading flows, and sentiment dynamics. Employing causal inference methods, including Regression Discontinuity Design (RDD) and Difference-in-Differences (DID), we reveal significant declines in WETH prices and NetFlow from CEXs to DEXs, signaling a measurable transfer of trust. Additionally, natural language processing methods, including topic modeling and sentiment analysis, uncover the complexities of user responses, highlighting shifts from functional discussions to emotional fragmentation in Binance's community, while Uniswap's sentiment exhibits a gradual upward trend. Despite data limitations and external influences, the findings underscore the intricate interplay between trust, sentiment, and market behavior in the cryptocurrency ecosystem. By bridging blockchain analytics, behavioral finance, and decentralized finance (DeFi), this study contributes to interdisciplinary research, offering a deeper understanding of distributed trust mechanisms and providing critical insights for future investigations into the socio-technical dimensions of trust in digital economies.

Related papers

• STORM: A Spatio-Temporal Factor Model Based on Dual Vector Quantized Variational Autoencoders for Financial Trading [55.02735046724146]
  In financial trading, factor models are widely used to price assets and capture excess returns from mispricing. We propose a Spatio-Temporal factOR Model based on dual vector quantized variational autoencoders, named STORM. Storm extracts features of stocks from temporal and spatial perspectives, then fuses and aligns these features at the fine-grained and semantic level, and represents the factors as multi-dimensional embeddings.
  arXiv  Detail & Related papers  (2024-12-12T17:15:49Z)
• Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators [2.038893829552158]
  This study introduces a machine learning approach to predict cryptocurrency prices. We make use of important technical indicators such as Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) to train and feed the XGBoost regressor model. We evaluate the model's performance through various simulations, showing promising results.
  arXiv  Detail & Related papers  (2024-07-16T14:41:27Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting [3.8965079384103865]
  This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20% in prediction accuracy.
  arXiv  Detail & Related papers  (2024-05-01T13:58:01Z)
• Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication [76.04373033082948]
  Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. We propose Exchange-of-Thought (EoT), a novel framework that enables cross-model communication during problem-solving.
  arXiv  Detail & Related papers  (2023-12-04T11:53:56Z)
• Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data [3.6390165502400875]
  We introduce novel approaches to cryptocurrency price forecasting, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques. By analysing news and social media content, we assess the impact of public sentiment on cryptocurrency markets.
  arXiv  Detail & Related papers  (2023-11-23T16:14:44Z)
• Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities [1.9624273277521183]
  This paper delves deep into the evolving discourse and sentiment dynamics on the public forums of leading DeFi projects. We show participants in decentralized communities generally express positive sentiments during Discord discussions. There is a potential interaction between discussion intensity and sentiment dynamics; higher discussion volume may contribute to a more stable sentiment from code analysis.
  arXiv  Detail & Related papers  (2023-10-31T08:23:47Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• Beyond Trading Data: The Hidden Influence of Public Awareness and Interest on Cryptocurrency Volatility [7.091344537490436]
  This study examines the various independent factors that affect the volatility of the Bitcoin-Dollar exchange rate. We propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making.
  arXiv  Detail & Related papers  (2022-02-12T21:39:29Z)
• Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
  We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias. A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-26T10:40:59Z)
• A Sentiment Analysis Approach to the Prediction of Market Volatility [62.997667081978825]
  We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
  arXiv  Detail & Related papers  (2020-12-10T01:15:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.