A Sea of Coins: The Proliferation of Cryptocurrencies in UniswapV2
- URL: http://arxiv.org/abs/2502.10512v1
- Date: Fri, 14 Feb 2025 19:18:39 GMT
- Title: A Sea of Coins: The Proliferation of Cryptocurrencies in UniswapV2
- Authors: Manuel Naviglio, Francesco Tarantelli, Fabrizio Lillo,
- Abstract summary: We analyze the financial impact of newly created tokens, assessing their market dynamics, profitability and liquidity manipulations.
Applying a simple buy-and-hold strategy, we are able to uncover some major risks associated with investing in newly created tokens.
Our study provides insights into the risks and financial dynamics of decentralized markets and their challenges for investors.
- Score: 0.8192907805418583
- License:
- Abstract: Blockchain technology has revolutionized financial markets by enabling decentralized exchanges (DEXs) that operate without intermediaries. Uniswap V2, a leading DEX, facilitates the rapid creation and trading of new tokens, offering high return potential but exposing investors to significant risks. In this work, we analyze the financial impact of newly created tokens, assessing their market dynamics, profitability and liquidity manipulations. Our findings reveal that a significant portion of market liquidity is trapped in honeypots, reducing market efficiency and misleading investors. Applying a simple buy-and-hold strategy, we are able to uncover some major risks associated with investing in newly created tokens, including the widespread presence of rug pulls and sandwich attacks. We extract the optimal sandwich amount, revealing that their proliferation in new tokens stems from higher profitability in low-liquidity pools. Furthermore, we analyze the fundamental differences between token price evolution in swap time and physical time. Using clustering techniques, we highlight these differences and identify typical patterns of honeypot and sellable tokens. Our study provides insights into the risks and financial dynamics of decentralized markets and their challenges for investors.
Related papers
- A Deep Learning Approach to Predict the Fall [of Price] of Cryptocurrency Long Before its Actual Fall [0.0]
The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market.
Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience.
arXiv Detail & Related papers (2024-11-20T08:09:35Z) - Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models [6.39158540499473]
This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market.
It is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands.
The results show a buy/sell signal accuracy of over 92%.
arXiv Detail & Related papers (2024-10-09T14:29:50Z) - 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) - 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) - Diffusion Variational Autoencoder for Tackling Stochasticity in
Multi-Step Regression Stock Price Prediction [54.21695754082441]
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility.
Current solutions to multi-step stock price prediction are mostly designed for single-step, classification-based predictions.
We combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction.
Our model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance.
arXiv Detail & Related papers (2023-08-18T16:21:15Z) - Uniswap Liquidity Provision: An Online Learning Approach [49.145538162253594]
Decentralized Exchanges (DEXs) are new types of marketplaces leveraging technology.
One such DEX, Uniswap v3, allows liquidity providers to allocate funds more efficiently by specifying an active price interval for their funds.
This introduces the problem of finding an optimal strategy for choosing price intervals.
We formalize this problem as an online learning problem with non-stochastic rewards.
arXiv Detail & Related papers (2023-02-01T17:21:40Z) - A Game of NFTs: Characterizing NFT Wash Trading in the Ethereum Blockchain [53.8917088220974]
The Non-Fungible Token (NFT) market experienced explosive growth in 2021, with a monthly trade volume reaching $6 billion in January 2022.
Concerns have emerged about possible wash trading, a form of market manipulation in which one party repeatedly trades an NFT to inflate its volume artificially.
We find that wash trading affects 5.66% of all NFT collections, with a total artificial volume of $3,406,110,774.
arXiv Detail & Related papers (2022-12-02T15:03:35Z) - Rogue Protocol: A Framework For NFT Royalties Tokenisation [0.0]
We propose a cryptographic system that ties the price of tokens to the success of a decentralised activity.
This guarantees the fair distribution of tokens, and rewards founders and participants in the system in line with the amount of risk they are taking.
arXiv Detail & Related papers (2022-10-21T13:02:04Z) - Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective.
We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
arXiv Detail & Related papers (2021-12-08T14:55:21Z) - The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations [50.521292491613224]
This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump.
The pump and dump scheme is a fraud as old as the stock market. Now, it got new vitality in the loosely regulated market of cryptocurrencies.
We report on three case studies related to pump and dump groups.
arXiv Detail & Related papers (2021-05-03T10:20:47Z) - Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations [50.521292491613224]
We perform an in-depth analysis of pump and dump schemes organized by communities over the Internet.
We observe how these communities are organized and how they carry out the fraud.
We introduce an approach to detect the fraud in real time that outperforms the current state of the art.
arXiv Detail & Related papers (2020-05-04T21:36:18Z)
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.