Model analyzes Proof-of-Stake network dynamics and speculative capital effects on token prices.
problem Understanding and managing price dynamics in Proof-of-Stake networks.
method Developed an open-economy macroeconomic model to analyze Proof-of-Stake dynamics and speculative capital effects.
result Speculative capital can shift staked-token ownership, potentially improving consensus decentralization.
This paper analyzes Ethereum blockchain using topology and geometry to uncover crypto-token price dynamics.
problem Lack of understanding on crypto-token price dynamics from blockchain data.
method Topological data analysis and functional data depth.
result Ethereum blockchain provides insights into crypto-token price dynamics not accessible with conventional methods.
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.
Paper proposes a framework for token economy simulation and wealth distribution.
problem Simulation and regulation of token economies.
method Formal analysis framework for tokenomics, defining mechanisms for wealth distribution and stability.
result Algorithmic regulatory controls for token economies to achieve desired wealth distribution.
Proof-of-Stake networks with EIP-1559 exhibit stable token prices and secure network security.
problem Token valuation and institutional capital in Proof-of-Stake networks under EIP-1559
method Open-economy macroeconomic equilibrium model
result Stable steady-state equilibrium price for ETH
Model predicts stock price changes and forecasts using tokenized data.
problem Challenges in stock price forecasting and prediction due to dynamic data and statistical differences.
method Introduces PCIE model with tokenization to handle both forecasting and prediction.
result PCIE model outperforms state-of-the-art models in forecast and prediction tasks.
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.
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.
Cryptocurrencies are distributed systems that allow exchanges of native tokens among participants, or the exchange of such tokens for fiat currencies in markets external to these public ledgers. The availability of their complete historical bookkeeping opens up the possibility of understanding the relationship between …
Derives pricing formulas for liquidity tokens in CPMMs, showing riskless growth.
problem Liquidity token pricing and hedging in CPMMs.
method Derives risk-neutral pricing and hedging formulas for CPMM liquidity tokens using derivative pricing perspective.
result Shows that hedging CPMM liquidity tokens should grow at the risk-free rate, contradicting empirical observations.
Derives token price process for AMM tokens, finds leverage effect and pricing discrepancies.
problem Derives token price process for AMM tokens.
method Derives CEV process for token price, derives closed-form option prices, introduces liquidity-adjusted Greeks.
result Token price process is CEV, with leverage effect and pricing discrepancies.
Study compares Web3 tokens to traditional assets, finding similar statistical properties.
problem Understanding statistical properties of Web3 tokens compared to traditional financial assets.
method Statistical analysis of various Web3 tokens across multiple time scales, comparing with traditional financial assets.
result Most Web3 tokens exhibit similar stylized facts to traditional financial assets, including heavy tails and volatility clustering.
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.
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.
ByteGen models LOB dynamics without tokenization, achieving realistic market metrics.
problem Modeling high-frequency LOB dynamics in finance.
method Autoregressive next-byte prediction on packed binary data, using H-Net architecture.
result Successfully reproduces stylized facts of financial markets.
Tab-TRM uses recursive model for insurance pricing on tabular data.
problem Insurance pricing on tabular data.
method Adapts recursive latent reasoning to insurance modeling using a compact, parameter-efficient network.
result Improves insurance pricing accuracy using iterative refinement of latent tokens.
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.
Extends DeTEcT framework for token economies with dynamic and probabilistic parameters.
problem Modeling wealth distribution in token economies with dynamic and probabilistic parameters.
method Introduces four parametrization techniques: dynamic vs static, probabilistic vs non-probabilistic.
result Derives existing wealth distribution models from DeTEcT framework with added restrictions.
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.
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.
Dynamic Vocabulary Pruning stabilizes LLM training by removing low-probability tokens.
problem Training Large Language Models (LLMs) with Reinforcement Learning (RL) causes numerical divergence between inference and training.
method Dynamic Vocabulary Pruning (DVP) constrains the RL objective to a safe vocabulary that excludes low-probability tokens.
result DVP stabilizes training by reducing systematic bias introduced by the extreme tail of the token distribution.
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.
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.
The paper analyzes the dynamics of tokens in transformer models at moderate interaction levels.
problem Understanding the evolution of tokens in transformer models at moderate interaction levels.
method Modeling transformer models as a system of particles interacting in a mean-field way and studying the corresponding dynamics.
result Characterization and convergence of the limiting dynamics in different phases of the system.
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.
New dynamic curves improve cryptocurrency exchange liquidity.
problem Low liquidity and arbitrage opportunities in existing AMMs.
method Dynamic curves adjust AMM function based on market prices.
result Maintains liquidity and total LP value over wide market price ranges.
Study uses sentiment analysis to predict cryptocurrency token returns in virtual reality.
problem Predicting cryptocurrency token returns in virtual reality economies.
method Used BERT for sentiment analysis and developed LSTM models integrating multi-modal features.
result Multi-modal model significantly outperforms price-only baseline in prediction accuracy.
We review the evidence that the erratic dynamics of markets is to a large extent of endogenous origin, i.e. determined by the trading activity itself and not due to the rational processing of exogenous news. In order to understand why and how prices move, the joint fluctuations of order flow and liquidity - and the way…
Particles representing tokens cluster in Transformers, influenced by initial tokens and matrix spectrum.
problem Understanding the geometry of learned representations in Transformers.
method Viewing Transformers as particle systems, applying dynamical systems and partial differential equations.
result Particles cluster towards limiting objects, confirming context-awareness and the emergence of leaders.
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.
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.
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.
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.
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.
This paper introduces strategies to maximize arbitrage profits in decentralized exchanges.
problem Maximizing profits from arbitrage loops in decentralized exchanges.
method Three strategies: MaxPrice, MaxMax, and Convex Optimization.
result The Convex Optimization strategy yields the highest monetized arbitrage profit in theory and practice.
SLERP interpolation optimizes dynamic weight rebalancing in AMMs.
problem Optimizing dynamic weight rebalancing in automated market makers (AMMs).
method Riemannian geometry and SLERP interpolation.
result SLERP interpolation minimizes the KL divergence loss in dynamic weight rebalancing.
This study explains RL training dynamics in LLMs, focusing on token-level optimization and reasoning pattern reshaping.
problem Understanding the training dynamics of RL in LLMs to improve their reasoning capabilities.
method Empirical analysis and theoretical modeling of RL training process, focusing on reasoning patterns and token optimization.
result RL primarily optimizes a sparse subset of critical tokens, reshaping reasoning pattern distributions and affecting model performance.
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.
Study reveals widespread manipulation of meme coins, leading to significant economic losses.
problem Widespread manipulation of meme coins leading to economic losses.
method Cross-chain analysis of 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base.
result 82.8% of high-return tokens show evidence of artificial growth strategies.
Study reveals AI's spontaneous topic changes in text prediction.
problem AI's inability to spontaneously switch topics like humans.
method Defined topic as Token Priority Graphs (TPGs) and analyzed self-attention models.
result AI can only switch topics if lower-priority tokens outnumber higher-priority ones.
SSMs combined with neural networks match Transformers in dynamic token selection.
problem Understanding the capabilities of SSMs in dynamic token selection.
method Exploring SSMs combined with fully connected neural networks.
result SSMs combined with nonlinear layers can efficiently solve challenging tasks and estimate functions.
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.
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.
Derives a size premium from automated market makers in decentralized AI subnets.
problem Determining the profitability and risk of decentralized AI subnets.
method Analyzes daily data on 128 subnets, tests the size premium, and calculates transaction costs.
result The size premium is reduced by a halving of token emissions but remains profitable only below a certain asset threshold.
DRFormer uses dynamic tokenization and multi-scale transformer to forecast long time series.
problem Forecasting long-term time series data across diverse scales.
method Dynamic tokenizer, multi-scale transformer, dynamic sparse learning, rotary position encoding.
result DRFormer outperforms existing methods in forecasting accuracy.
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…
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.
We define what "Price Impact" means, and how it is measured and modelled in the recent literature. Although this notion seems to convey the idea of a forceful and intuitive mechanism, we discuss why things might not be that simple. Empirical studies show that while the correlation between signed order flow and price ch…