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.
Cohort analysis speeds up Bitcoin blockchain data queries.
problem Efficiently querying Bitcoin blockchain data for economic insights.
method Cohort analysis applied to Bitcoin transaction data.
result Creation of datasets and visualizations for key Bitcoin transaction indicators.
TLMG4Eth combines language and graph models for Ethereum fraud detection.
problem Current fraud detection methods fail to consider semantic and similarity patterns in Ethereum transactions.
method TLMG4Eth uses a transaction language model and graph-based methods to capture semantic, similarity, and structural features.
result TLMG4Eth detects anomalies in Ethereum transactions more effectively than existing methods.
This survey analyzes knowledge discovery in cryptocurrency transactions.
problem Understanding user behaviors and collective actions in cryptocurrency transactions.
method Data mining techniques and literature review.
result Classified existing research into three aspects and discussed major findings.
Deep models for financial transactions are vulnerable to adversarial attacks, especially when adding transaction tokens.
problem Vulnerability of deep models to adversarial attacks on financial transaction records.
method Examine adversarial attacks and defenses on transaction records data, considering black-box attacks and adding transaction tokens.
result A few generated transactions can fool a deep-learning model, highlighting the need for robustness improvements.
Money flow models are essential tools to understand different economical phenomena, like saving propensities and wealth distributions. In spite of their importance, most of them are based on synthetic transaction networks with simple topologies, e.g. random or scale-free ones, as the characterisation of real networks i…
The study examines pricing American options with both exogenous and endogenous transaction costs.
problem Pricing American options with transaction costs and liquidity risks.
method Modeling liquidity risks as a mean-reverting process and transaction costs as proportional to trading amount. Two nonlinear PDEs are used to characterize option values. Numerical solution via ADI method and model calibration using maximum likelihood estimation.
result The model incorporating liquidity risks significantly outperforms the Leland model.
Transactional network data can be thought of as a list of one-to-many communications(e.g., email) between nodes in a social network. Most social network models convert this type of data into binary relations between pairs of nodes. We develop a latent mixed membership model capable of modeling richer forms of transacti…
Paper generates synthetic financial transactions for AML model testing.
problem Insufficient real data for AML model training.
method Agent-based generator calibrated to real transactions.
result Synthetic datasets improve model comparison by providing complete ground truth.
The study extracts market direction from transaction data.
problem Extracting market direction from transaction data.
method Dynamic equation with time scale selection from past transactions.
result Automatic determination of time scale for price calculation.
We examine the relationship between trading volumes, number of transactions, and volatility using daily stock data of the Tokyo Stock Exchange. Following the mixture of distributions hypothesis, we use trading volumes and the number of transactions as proxy for the rate of information arrivals affecting stock volatilit…
Paper uses GANs to simulate consumer transactions with SKU constraints.
problem Simulating realistic consumer transactions in retail systems.
method Integrates GANs with consumer behavior and SKU availability constraints.
result Demonstrates enhanced realism in simulated transactions.
New method improves blockchain analysis by handling temporal changes and scalability.
problem Limited focus on evolving nature and scalability of blockchain transaction networks.
method Incremental approach with Metropolis-Hastings random walks.
result Comparable performance in node classification tasks with reduced computational overhead.
The study examines cryptocurrency market activity, revealing multifractal inter-transaction times and challenging traditional statistical models.
problem Analyzing long-range autocorrelations and multifractality in cryptocurrency market activity.
method Analysis of tick-by-tick data from multiple cryptocurrency trading platforms, focusing on inter-transaction times, transaction volumes, and volatility.
result Inter-transaction times exhibit multifractality, indicating periods of increased market activity are more complex than quiet periods.
Bank transactions help predict macroeconomic indexes faster and more accurately.
problem Lag in macroeconomic index availability and autoregressive models' limitations in complex scenarios.
method Use financial transactions data to estimate macroeconomic indexes using neural networks and smart sampling.
result Neural network approach outperforms baseline methods on hand-crafted features based on transactions.
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.
We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized …
This study compares decentralized banks and finds some lack decentralization.
problem Decentralized banks do not fully decentralize transactions as expected.
method Network analysis of transaction data from four banks using core-periphery features.
result MakerDao and Compound are more decentralized than Aave and Liquity.
Nowcasts consumption and investment from Turkish bank transactions for real-time GDP.
problem Lack of timely data for GDP nowcasting in Turkey.
method Used aggregated bank transactions to replicate quarterly national accounts data.
result Bank transaction data successfully nowcasts Turkish GDP, especially useful for countries with long data lags.
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech compani…
Study recovers investor preferences from portfolio data using synthetic data and robust optimization.
problem Recovering latent investor preferences from observed portfolio allocations under uncertainty.
method Inverse portfolio optimization framework integrating robust optimization and regret-based inference.
result Accurate recovery of transaction cost parameters and partial identifiability of ESG penalties under preference misspecification and market shocks.
Study XRP network, propose Flow Index to analyze transaction frequencies.
problem Analyze transaction frequencies in XRP network.
method Analyze XRP transaction history, propose Flow Index.
result Flow Index reveals bow-tie/walnut structure in XRP network.
This paper presents a large Bitcoin transaction graph dataset for research.
problem Lack of a comprehensive dataset for Bitcoin research.
method Created a large-scale transactions graph dataset with timestamps.
result Established a large publicly available dataset for advanced research.
The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only macroscopic properties (e.g. the result…
In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transact…
A new model forecasts optimal portfolio weights from high-frequency data.
problem Forecasting optimal portfolio weights from high-frequency data.
method Dynamic Conditional Weights (DCW) model for portfolio weights dynamics.
result DCW model outperforms other models in portfolio allocations and measures.
This paper examines the intra-day seasonality of transacted limit and market orders in the DEM/USD foreign exchange market. Empirical analysis of completed transactions data based on the Dealing 2000-2 electronic inter-dealer broking system indicates significant evidence of intraday seasonality in returns and return vo…
Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active l…
Proposes a multi-stream RNN model for predicting merchant transactions.
problem Predicting future transaction statistics of merchants.
method Multi-stream RNN model tailored for multivariate time series and multi-step predictions.
result Outperforms existing state-of-the-art methods in merchant transaction predictions.
Study on-chain peak shaving to reduce Ethereum transaction costs.
problem Reducing transaction costs in blockchain networks, especially during congested periods.
method Analyzing transaction-level data from multiple firms across various industries to understand scheduling responses and cost management strategies.
result Firms' scheduling responses to congestion vary, leading to different fee savings and residual costs.
We conduct a market experiment with human agents in order to explore the structure of transaction networks and to study the dynamics of wealth accumulation. The experiment is carried out on our platform for 97 days with 2,095 effective participants and 16,936 times of transactions. From these data, the hybrid distribut…
A competition increases financial transaction models' robustness against attacks.
problem Neural networks used by banks are vulnerable to adversarial attacks in financial transaction data.
method A novel competition where participants propose attacks and defenses, simulating real-world conditions.
result Participants' strategies and outcomes provide insights into improving financial transaction models' robustness.
New method calculates super-hedging prices with transaction costs.
problem Super-hedging European contingent claims under proportional transaction costs.
method Explicit recursive scheme based on convex duality and Legendre-Fenchel transform.
result Computes super-hedging price and optimal strategy without martingale arguments.
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspect…
Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start …
This research develops heuristics to detect CoinJoin transactions on Bitcoin blockchain.
problem Compromised privacy in Bitcoin transactions due to CoinJoin.
method Analyzed open-source CoinJoin implementations to develop heuristics.
result Refined heuristics for identifying CoinJoin transactions on the blockchain.
The study analyzes XRP transaction networks to understand market dynamics.
problem Understanding market dynamics of XRP through transaction data.
method Weekly weighted directed networks are embedded into a vector space using network embedding techniques. A correlation tensor is calculated and analyzed using singular value decomposition.
result The correlation tensor provides insights into the system's behavior and dependence on model parameters.
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectiv…
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised…
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
problem Detecting and preventing bank transaction fraud caused by data breaches.
method A distributed alternating algorithm that assigns probabilities to different locations being compromised.
result BreachRadar achieves over 90% precision and recall in detecting compromised cards.
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and crypto-tokens are permanently recorded on distributed ledgers and are publicly available. As …
Investment strategy optimized in markets with transaction costs and search delays.
problem Maximizing wealth in an illiquid market with transaction costs and search frictions.
method Characterized no-trade region and provided asymptotic expansions of value function for small transaction costs.
result The effects of transaction costs are more pronounced in illiquid markets.
Neural networks improve loss reserving with case estimates and transaction data.
problem Improving loss reserving accuracy using neural networks.
method Comparison of feed-forward and recurrent neural networks trained on case estimates and transaction data.
result Case estimates significantly improve predictions, but memory-equipped neural networks offer minimal additional benefit.
The darknet markets are notorious black markets in cyberspace, which involve selling or brokering drugs, weapons, stolen credit cards, and other illicit goods. To combat illicit transactions in the cyberspace, it is important to analyze the behaviors of participants in darknet markets. Currently, many studies focus on …
Cryptocurrencies use blockchain tech for secure transactions, offering new research opportunities.
problem Misunderstanding of cryptocurrency technology and lack of empirical data.
method Analyzing detailed transaction data and summarizing statistics.
result Opportunity for academic research in financial economics.
Paper proposes a deep reinforcement learning model for forex trading that considers transaction costs.
problem Trading in forex markets with high transaction costs and non-stationary data.
method Deep reinforcement learning model considering transaction costs and online learning.
result Maximizes profit while keeping transaction costs low in non-stationary markets.
Transaction costs appear in financial markets in more than one form. There are several results in the literature on small proportional transaction cost and not that many on fixed transaction cost. In the present work, we heuristically study the effect of both types of transaction cost by focusing on a portfolio optimiz…
This paper tackles cost-sensitive portfolio optimization under ambiguous return distributions.
problem Tackles cost-sensitive distributionally robust log-optimal portfolio problem with ambiguous return distributions.
method Uses Wasserstein metric for distributional ambiguity, incorporates convex transaction costs, and approximates infinite-dimensional problem with finite convex program.
result Establishes conditions for robustly survivable trades and validates theoretical framework with empirical studies.