Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges
- URL: http://arxiv.org/abs/2404.17227v1
- Date: Fri, 26 Apr 2024 07:58:05 GMT
- Title: Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized Exchanges
- Authors: Xintong Wu, Wanling Deng, Yuotng Quan, Luyao Zhang,
- Abstract summary: The transition from centralized to decentralized trust mechanisms plays a critical role in shaping the cryptocurrency ecosystem.
This study contributes significantly to interdisciplinary research, bridging distributed systems, behavioral finance, and Decentralized Finance (DeFi)
It offers valuable insights for the distributed computing community, particularly in understanding and applying distributed trust mechanisms in digital economies.
- Score: 1.9624273277521183
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the evolving landscape of digital finance, the transition from centralized to decentralized trust mechanisms, primarily driven by blockchain technology, plays a critical role in shaping the cryptocurrency ecosystem. This paradigm shift raises questions about the traditional reliance on centralized trust and introduces a novel, decentralized trust framework built upon distributed networks. Our research delves into the consequences of this shift, particularly focusing on how incidents influence trust within cryptocurrency markets, thereby affecting trade behaviors in centralized (CEXs) and decentralized exchanges (DEXs). We conduct a comprehensive analysis of various events, assessing their effects on market dynamics, including token valuation and trading volumes in both CEXs and DEXs. Our findings highlight the pivotal role of trust in directing user preferences and the fluidity of trust transfer between centralized and decentralized platforms. Despite certain anomalies, the results largely align with our initial hypotheses, revealing the intricate nature of user trust in cryptocurrency markets. This study contributes significantly to interdisciplinary research, bridging distributed systems, behavioral finance, and Decentralized Finance (DeFi). It offers valuable insights for the distributed computing community, particularly in understanding and applying distributed trust mechanisms in digital economies, paving the way for future research that could further explore the socio-economic dimensions and leverage blockchain data in this dynamic domain.
Related papers
- The Jade Gateway to Trust: Exploring How Socio-Cultural Perspectives Shape Trust Within Chinese NFT Communities [53.778565588482294]
The emergence of non-fungible tokens (NFTs) has transformed how we handle digital assets and value.
Despite their initial popularity, NFTs face declining adoption influenced not only by cryptocurrency volatility but also by trust dynamics within communities.
Our research identifies three critical trust dimensions in China's NFT market: technological, institutional, and social.
arXiv Detail & Related papers (2025-04-16T10:03:30Z) - Collaborative Value Function Estimation Under Model Mismatch: A Federated Temporal Difference Analysis [55.13545823385091]
Federated reinforcement learning (FedRL) enables collaborative learning while preserving data privacy by preventing direct data exchange between agents.
In real-world applications, each agent may experience slightly different transition dynamics, leading to inherent model mismatches.
We show that even moderate levels of information sharing can significantly mitigate environment-specific errors.
arXiv Detail & Related papers (2025-03-21T18:06:28Z) - 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) - XDC Network Assessment: Decentralization, Scalability and Security [0.0]
XinFin, in 2019, unveiled the XDC network, an enterprise-ready hybrid blockchain platform that is open-source and specializes in tokenization for real-world decentralized finance.
Overseeing the XDC network is the XDC Foundation, a non-profit organization established to encourage the growth, enhancement, and adoption of the XDC Network.
arXiv Detail & Related papers (2024-08-05T09:01:43Z) - 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) - Decentralized Credential Status Management: A Paradigm Shift in Digital Trust [0.0]
Public key infrastructures are essential for Internet security, ensuring robust certificate management and revocation mechanisms.
The transition from centralized to decentralized systems presents challenges such as trust distribution and privacy-preserving credential management.
This paper explores the evolution of certificate status management from centralized to decentralized frameworks, focusing on blockchain technology and advanced cryptography.
arXiv Detail & Related papers (2024-06-17T13:17:56Z) - IT Strategic alignment in the decentralized finance (DeFi): CBDC and digital currencies [49.1574468325115]
Decentralized finance (DeFi) is a disruptive-based financial infrastructure.
This paper seeks to answer two main questions 1) What are the common IT elements in the DeFi?
And 2) How the elements to the IT strategic alignment in DeFi?
arXiv Detail & Related papers (2024-05-17T10:19:20Z) - 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) - Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications.
Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs.
We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
arXiv Detail & Related papers (2024-03-29T14:58:28Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - How Does Stake Distribution Influence Consensus? Analyzing Blockchain Decentralization [10.679753825744964]
This study first formalizes decentralization metrics for weighted consensus mechanisms.
We introduce the Square Root Stake Weight (SRSW) model, which effectively recalibrates staking weight distribution.
This research is a pivotal step toward a more fair and equitable distribution of staking weight, advancing the decentralization in blockchain consensus mechanisms.
arXiv Detail & Related papers (2023-12-21T15:32:20Z) - 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) - Unpacking How Decentralized Autonomous Organizations (DAOs) Work in
Practice [54.47385318258732]
Decentralized Autonomous Organizations (DAOs) have emerged as a novel way to coordinate a group of entities towards a shared vision.
In just a few years, over 4,000 DAOs have been launched in various domains, such as investment, education, health, and research.
Despite such rapid growth and diversity, it is unclear how theses actually work in practice and to what extent they are effective in achieving their goals.
arXiv Detail & Related papers (2023-04-17T01:30:03Z) - 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) - 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) - Efficiency in Digital Economies -- A Primer on Tokenomics [55.41644538483948]
cryptographic tokens are a new digital paradigm that can facilitate the establishment of economic incentives in digital ecoystems.
We show how certain principles and values that arise from the evolutionary process of digital cooperation can lead to a market economy characterized by economic efficiency of both individuals and the tokenized ecosystem as a whole.
arXiv Detail & Related papers (2020-08-06T09:31:56Z) - Regulation conform DLT-operable payment adapter based on trustless -
justified trust combined generalized state channels [77.34726150561087]
Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks.
We give an overview of current solutions that differ in their fundamental values and technological possibilities.
We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
arXiv Detail & Related papers (2020-07-03T10:45:55Z)
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.