This paper examines challenges in analyzing NFT transaction data.
problem Challenges in analyzing NFT transaction data due to non-fungible nature and blockchain.
method Analysis of transaction history of eight NFT collections.
result Illustrates challenges such as price differentiation, lateral swaps, and volatility.
Article examines NFT market microstructure and trading risks.
problem Difficulty in distinguishing genuine NFTs from fads and scams.
method Analyzes price formation, market structure, and transparency.
result Provides due-diligence pointers to mitigate NFT trading risk.
This paper explores using NFTs for patents, offering a framework and addressing challenges.
problem Lack of research in applying NFT to intellectual property, especially patents.
method Developed a layered conceptual NFT-based patent framework.
result Promotes transparency and liquidity in patent markets.
Cryptocurrency and NFT prices are highly correlated, mirroring historical bubbles.
problem Evaluating the wealth effect of cryptocurrency prices on real estate.
method Exploiting metaverse LAND and cryptocurrencies to track correlations and causality.
result Cryptocurrency prices Granger cause NFT LAND prices, similar to historical bubbles.
Develops a diamond price index for online auction platforms.
problem Tracking market trends of wholesale diamond prices.
method Modelling diamond prices to create a hedonic index.
result Provides a basis for constructing derivatives for collectables.
Study on price fluctuations in NFT market, showing heavy-tailed distributions and long-range memory.
problem Characterizing price fluctuations in NFT market.
method Analysis of capitalization, floor price, transactions, inter-transaction times, and volume value of NFTs.
result NFT market exhibits heavy-tailed probability distribution functions, well described by stretched exponentials, with long-range memory.
Generative model predicts NFT collection transactions based on early history.
problem Predict future transactions of newly minted NFT collections.
method Unsupervised learning to extract contexts, then generate future transactions.
result Projected market value of new NFT collections.
Study predicts NFT bubbles using LPPL model.
problem Tackles bubble prediction of NFTs.
method Applied logarithmic periodic power law (LPPL) model to NFT price data.
result NFTs, Decentraland, and ArtBlocks are in bubbles, while Ethereum Name Service is in a negative bubble.
NFTs raise concerns like scams, racism, and sexism; centralization vs decentralization debate.
problem Concerns and value judgments of stakeholders in NFT market.
method Mixed quantitative and qualitative methods: social media analysis and interviews.
result Identified financial scams, counterfeit NFTs, hacking, and unethical NFTs as major issues.
AnChain.AI detects NFT wash trading with 0.14% of transactions flagged.
problem NFT market manipulation through wash trading.
method Algorithm flags transactions within 30 days of repurchase.
result 0.14% of NFT transactions are involved in wash trading.
Sandbox LAND prices differ based on unit of account, affecting investment returns.
problem Investment returns vary based on how prices are denominated.
method Analyzed over 71,000 transactions to compare different units of account.
result Users are willing to pay more in SAND and less in wETH for transactions.
We propose a new NFT price index to track the digital art market.
problem Lack of a comprehensive NFT price index.
method Developed a new methodology to create a NFT Price Index.
result Demonstrated the dynamics and performances of NFT markets.
Deep learning predicts NFT prices with high accuracy.
problem Dynamic valuation of non-fungible tokens (NFTs).
method Trained deep learning model on Ethereum blockchain data.
result Highly accurate price predictions of NFTs.
This paper examines unfair trading practices in NFT markets.
problem Sophisticated actors exploit market inefficiencies for unfair profits.
method Analyzes three types of opportunistic trading strategies.
result Identifies and categorizes unfair trading practices in NFT markets.
Study examines NFT market dynamics using correlation and noise analysis.
problem Understanding correlations and noise in NFT market.
method Used detrended correlation coefficient and correlation matrix analysis.
result Correlation strength in NFT market is lower than in cryptocurrency markets.
NFTs with diverse rare attributes sell at higher prices.
problem Understanding how rarity affects NFT market dynamics.
method Analyzed 3.7M NFT transactions across 410 collections.
result Rarer NFTs sell for higher prices and are less risky.
NFTs revolutionize art sales by providing proof of ownership.
problem Lack of provenance and authenticity in digital art.
method Analysis of major art NFT marketplaces.
result NFTs reduce the need for intermediaries in the art trade.
NFT royalties boost creator earnings by sharing risk, reducing info asymmetry, and enabling price discrimination.
problem NFTs' royalties are criticized for being neutralized by speculators.
method Analyzes NFTs' royalties in various market conditions and their effects on creators.
result Royalties enable creators to capitalize on speculators' presence through risk sharing, info reduction, and price discrimination.
Study identifies NFT whales driving the market with consistent high returns.
problem Lack of financial analysis of NFT trading ecosystem.
method Longitudinal study of 3.8M NFT transactions, classifying traders into whales, dolphins, and minnows.
result Top 0.1% of NFT traders (whales) drive the market with consistent, high returns.
This paper detects fraudulent trading in the NFT market.
problem Fraudulent activities like wash trading in the NFT market.
method Unsupervised learning using K-means clustering on market data.
result Identified groups of traders with suspicious behavior.
Analyzes NFT market trends, trade networks, and visual features.
problem Understanding the structure and evolution of NFT market.
method Data analysis of 6.1 million trades of 4.7 million NFTs.
result NFTs form tight clusters and collections contain visually homogeneous objects.
The paper models SaaS products as insurance, offering new pricing tools.
problem Modeling capped-usage SaaS products with insurance principles.
method Frequency-severity decomposition, premium calculation, Monte Carlo simulations.
result SaaS pricing can be analyzed using insurance actuarial methods.
NFT art market shows strong preferential ties among sellers and buyers.
problem Reducing preferential ties in NFT art market.
method Analyzing NFT art sales data from multiple galleries.
result NFT art market is highly concentrated with preferential ties.
Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.
problem Historical gas fee volatility caused economic disequilibria and stakeholder challenges.
method Time-varying Granger causality analysis using data on active wallets and transaction volume.
result Dynamic bidirectional causal relationships between transaction fees and economic subsystems across Ethereum.
Digital currencies exhibit multifractality due to heavy-tailed returns and temporal correlations.
problem Understanding market inefficiencies and predicting volatility in digital currencies.
method Multifractal cross-correlation analysis (MFCCA) and multifractal detrended fluctuation analysis (MFDFA).
result Temporal correlations are the primary source of multifractality in digital currency markets.
Neural sequence generation is typically performed token-by-token and left-to-right. Whenever a token is generated only previously produced tokens are taken into consideration. In contrast, for problems such as sequence classification, bidirectional attention, which takes both past and future tokens into consideration, …
Paper reduces vocabulary losslessly for language model cooperation.
problem Language models struggle to cooperate with different tokenizations.
method Established a theoretical framework for lossless vocabulary reduction.
result Efficiently converts models with different tokenizations to cooperate with maximal common vocabulary.
This research improves capital efficiency and impermanent loss in cryptocurrency markets using multi-token trading pools.
problem Poor impermanent loss and capital efficiency in automated market makers.
method Analysis and construction of a multi-token token proactive market maker (MPMM).
result MPMM shows better impermanent loss and capital efficiency than comparable market makers.
This study examines whether tokenized assets improve liquidity and finds significant differences across categories.
problem Improving liquidity for real-world assets through tokenization.
method Examined tokenized real-world assets using Ethereum-based data, measuring liquidity through turnover, active addresses, and active-month indicator.
result Gold-backed tokens show more persistent on-chain activity than Treasury and private-credit-related products, but asset value alone does not reliably predict liquidity.
Study examines revenue from scam tokens on Ethereum, revealing key characteristics and market factors.
problem Revenue from scam tokens on Ethereum blockchain.
method Empirical analysis of Uniswap, examining characteristics and market factors.
result Revenue from scam tokens is influenced by market economic factors and community engagement.
Blockchain-based exchanges adopt based on token pair volatility and personal use.
problem Token value loss and arbitrage issues in decentralized exchanges.
method Investigation of Automated Market Makers (AMMs) using transaction-level data.
result AMMs are adopted for high personal use or highly correlated token price movements.
Blockchain fan tokens boost sports fan engagement by 50%.
problem Low fan engagement in sports decisions.
method Analyzed 3,576 fan token polls to identify determinants of participation.
result Fan tokens engage 4,003 participants per poll, 50% of token holders.
Paper introduces a method to assess liquidity risk in meme tokens using entity-linked address analysis.
problem High market volatility and vulnerability to manipulation in meme tokens.
method Multi-dimensional approach integrating fund flow analysis, behavioral similarity, and anomalous transaction detection.
result Significant disparities between apparent and actual liquidity in meme token markets.
DOS improves language model generation by considering inter-token dependencies.
problem Lack of sequence-level information and inter-token dependencies in existing decoding strategies.
method Dependency-Oriented Sampler (DOS) that uses attention matrices to approximate inter-token dependencies.
result DOS consistently achieves superior performance on code generation and mathematical reasoning tasks.
Study predicts success of crypto-tokens on Pump.fun platform.
problem Identify factors affecting the success of new crypto-tokens.
method Build predictive models using bonding curve mechanism and structural/behavioral variables.
result Conditional variables significantly improve the predictive power of token success.
Proving that next-token prediction makes language models generate coherent long documents.
problem Understanding why language models generate coherent documents despite focusing on next-token prediction.
method Proving the power of next-token prediction in learning longer-range structure using Recurrent Neural Networks (RNN).
result Optimizing next-token prediction in RNNs yields a model that closely approximates the training distribution, even for long-range coherence.
Expands MLM by masking token positions, improving performance and convergence.
problem Improving language model performance and convergence.
method Masking token positions along with [MASK] tokens, using a fully connected classifier stage.
result Shows .3% improvement and 50% faster convergence for BERT Base with position masking.
This paper compares token and equity financing for startups.
problem Understanding differences in return rates between token and equity financing.
method Developed a three-period model to analyze liquidity and return differences.
result Entrepreneurs can achieve higher payoffs by issuing tokens, especially for risk-averse investors with liquidity needs.
QA-Token improves tokenization for noisy data, boosting model performance.
problem Tokenization ignores data quality, limiting model effectiveness on noisy corpora.
method QA-Token combines signal quality with vocabulary construction through bilevel optimization and reinforcement learning.
result QA-Token achieves state-of-the-art performance on genomic and financial datasets.
Minimal token perturbations reveal how Transformer models process information.
problem Understanding information propagation in Transformer models for interpretability.
method Study of minimal token perturbations on embedding space.
result Rare tokens cause larger shifts, and input information mixes deeper.
LLM-as-a-service prices vary arbitrarily due to tokenization multiplicity.
problem Arbitrary price variation in LLM-as-a-service due to multiple tokenizations of the same output.
method Introduce canonical generation to restrict LLMs to unique tokenizations and develop an efficient sampling algorithm.
result Our sampling algorithm for canonical generation solves tokenization multiplicity and maintains comparable performance and runtime to standard sampling.
Traditional centralized energy systems have the disadvantages of difficult management and insufficient incentives. Blockchain is an emerging technology, which can be utilized in energy systems to enhance their management and control. Integrating token economy and blockchain technology, token economic systems in energy …
New insights show stochastic initialization prevents token clustering in deep Transformers.
problem Understanding token dynamics in deep stochastic Transformers.
method Analysis of deep Transformers with random initialization noise, proving convergence to an interacting-particle system on the sphere.
result Initialization noise prevents token clustering, leading to antipodal formations.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
problem Risk spillovers among AI ETFs, AI tokens, and green markets.
method R2 decomposition method
result AI ETFs and clean energy act as risk transmitters, while AI tokens and green assets act as receivers.
Study reveals risks of investing in new crypto-tokens in decentralized exchanges.
problem Risks associated with investing in newly created tokens in decentralized exchanges.
method Analysis of financial impact, market dynamics, profitability, and liquidity manipulations.
result Significant market liquidity trapped in honeypots, reducing market efficiency and misleading investors.
Tokenized RWAs face liquidity issues despite promising markets.
problem Low trading volumes and limited investor participation in tokenized assets.
method Empirical analysis of tokenized real estate, private credit, and treasury funds.
result Most tokenized assets exhibit low transfer activity and limited secondary trading.
Fan tokens surged before World Cup matches, but declined during them, revealing cognitive biases.
problem Analyzing the impact of FIFA World Cup matches on fan tokens.
method Event study and intraday analysis of blockchain-based fan tokens.
result Fan tokens experienced a surge in returns six months before the World Cup, followed by a decline during the matches, revealing asymmetries in performance.
We empirically verify that the market capitalisations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values, with the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simpl…