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.
Study reveals patterns in daily bank transactions.
problem Understanding systemic risk in interbank networks.
method Analysis of daily interbank transactions and a simple model.
result Distinctive network-scale dynamics emerge from stable interaction patterns.
PDE model predicts Bitcoin price using transaction network and sentiment data.
problem Predicting Bitcoin price with high accuracy.
method Partial differential equation model on Bitcoin transaction network, incorporating Google Trends Index.
result Average daily bitcoin price prediction accuracy of 0.82 over 362 days in 2017.
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.
Study examines how trading volumes and transactions affect stock volatility.
problem Understanding the impact of trading volumes and transactions on stock volatility.
method Used GARCH models to analyze daily stock data of the Tokyo Stock Exchange.
result GARCH effects are not always removed by adding trading volumes or transactions, suggesting they don't fully represent information arrivals.
Study infers volatility indicators from Bitcoin blockchain data.
problem Predicting extreme price volatility in Bitcoin.
method Non-negative decomposition of Bitcoin transaction graphs.
result EWI provides more predictive information than other methods.
Deep learning shows ETF imbalances are more informative than market imbalances.
problem Determining causality between ETF and market imbalances.
method Deep learning econometric methodology applied to stock and ETF transactions.
result ETF imbalance messages are more informative than market imbalance messages.
Stablecoins are unstable, but some are more stable than others.
problem Inconsistency in stability of stablecoins and high transaction costs.
method Analysis of market shares, transaction volumes, and exchange activity.
result USD Coin, Tether, and Dai are the most stable, but overall failure rate remains high.
The daily volume of transaction on the New York Stock Exchange and its day-to-day fluctuations are analysed with respect to power-law tails as well long-term trends. We also model the transition to a Gaussian distribution for longer time intervals, like months instead of days.
We show power-scaling behaviors for fluctuations in share volume, which no other studies have so far done. After analyzing a database of the daily transactions for all securities listed on the Tokyo Stock Exchange, we selected 1050 large companies that each had an unbroken series of daily trading activity from January …
This study compares SMPC and genetic algorithms for stock trading with transaction costs.
problem Finding the best feedback control structure for stock trading with transaction costs.
method Two classes of feedback control structures (SMPC and genetic) are tested with different prediction methods.
result SMPC controllers outperform genetic algorithms in backtesting.
The asymmetric price impact between the institutional purchases and sales of 32 liquid stocks in Chinese stock markets in year 2003 is carefully studied. We analyze the price impact in both drawup and drawdown trends with consecutive positive and negative daily price changes, and test the dependence of the price impact…
Investigates how rebalancing frequency and transaction costs affect log-optimal portfolios.
problem Impact of rebalancing frequency and transaction costs on log-optimal portfolios.
method Proved equivalence to concave program, derived optimality conditions, tested using intraday and daily data.
result Transaction costs can cause bankruptcy for frequency-dependent log-optimal portfolios, approximating to quadratic concave program.
Mobile payment incentives optimized using merchant transaction networks.
problem Optimizing marketing campaigns with limited budgets.
method Graph representation learning on transaction networks.
result Effective modeling of merchant sensitivity to incentives.
We empirically study the trading activity in the electronic on-book segment and in the dealership off-book segment of the London Stock Exchange, investigating separately the trading of active market members and of other market participants which are non-members. We find that (i) the volume distribution of off-book tran…
Asset liquidity in modern financial markets is a key but elusive concept. A market is often said to be liquid when the prevailing structure of transactions provides a prompt and secure link between the demand and supply of assets, thus delivering low costs of transaction. Providing a rigorous and empirically relevant d…
A new sequencing rule prevents miners from front-running transactions in decentralized exchanges.
problem Miners exploit their privileged position to front-run transactions, leading to unfair profits.
method Introduce verifiable sequencing rules that constrain transaction execution order and are verifiable.
result A verifiable sequencing rule ensures users receive at least fair execution prices, preventing front-running.
High-value transactions between Australian banks are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments sourced from the SWIFT, the Austraclear, and the interbank transactions e…
Benchmarking deep learning models for financial time series, focusing on risk-adjusted performance.
problem Optimizing risk-adjusted performance in financial time series prediction.
method Evaluation of various deep learning architectures including linear models, RNNs, transformers, state space models, and sequence representation approaches.
result Hybrid models like VSN with LSTM and xLSTM achieve the highest overall Sharpe ratio and superior downside adjusted characteristics.
Model predicts daily closing price distributions in call auctions.
problem Predicting price distributions in financial markets.
method Modeling price formation in call auctions with random orders and equilibrium equation.
result Model accurately predicts daily closing price distributions for financial indices.
AXI assesses bank funding costs transparently, improving loan pricing and reducing financial risk.
problem Lack of credit-sensitive funding benchmarks after LIBOR transition.
method AXI aggregates unsecured funding transactions across maturities, producing a daily credit spread.
result AXI correlates with financial conditions and market stress, reducing funding risk and offering spread discounts.
The study uses LSTM and random forests to forecast stock price movements for intraday trading.
problem Forecasting directional movements of stock prices for intraday trading.
method Employed random forests and LSTM networks to analyze S&P 500 constituent stocks.
result Multi-feature setting provided higher daily returns (0.64% using LSTM, 0.54% using random forests) compared to single-feature setting.
Study develops a multi-pair trading strategy using graph clustering and machine learning.
problem Improving risk-adjusted returns and reducing transaction costs in US equities market.
method Statistical arbitrage, graph clustering algorithms, Kelly criterion, machine learning classifiers.
result Optimal signal detection and risk management techniques outperformed benchmarks.
In this paper we consider an information theoretic approach for the accounting classification process. We propose a matrix formalism and an algorithm for calculations of information theoretic measures associated to accounting classification. The formalism may be useful for further generalizations and computer-based imp…
Deep RL optimizes US stock allocations with better performance.
problem Optimizing asset allocation in US equities markets.
method Reinforcement learning applied to asset allocation problems.
result Deep RL models outperform traditional methods in asset allocation.
The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over 2%, and it is the result of the non-Pearson type auto-correlations in the index. These non-Pearson type correlations can be viewed also as functionally dependent daily volatilities, ext…
There is intense interest in understanding the stochastic and dynamical properties of the global Foreign Exchange (FX) market, whose daily transactions exceed one trillion US dollars. This is a formidable task since the FX market is characterized by a web of fluctuating exchange rates, with subtle inter-dependencies wh…
Benchmarking deep time series models for equity portfolios
problem Selecting the best deep time series model for equity portfolios
method Using a CRSP benchmark and multi-criteria acceptability analysis
result No architecture dominates the benchmark, with TransEnc-8 having the highest rank-1 acceptability
A RL framework for hedging equity index options with realistic costs.
problem Dynamic hedging of equity index option exposures under transaction costs.
method Reinforcement Learning (RL) with a leak-free environment, cost-aware reward function, and stochastic actor-critic agent.
result The RL policy improves risk-adjusted performance compared to no-hedge, momentum, and volatility-targeting baselines.
Proposes a new framework for investing that adapts to market regimes.
problem Adapting to dynamic market regimes for better investment performance.
method Wasserstein Hidden Markov Model (HMM) with transaction-cost-aware optimization.
result Significantly higher risk-adjusted performance compared to benchmarks.
We consider a portfolio with call option and the corresponding underlying asset under the standard assumption that stock-market price represents a random variable with lognormal distribution. Minimizing the variance (hedging risk) of the portfolio on the date of maturity of the call option we find a fraction of the ass…
EXFormer predicts foreign exchange returns with high accuracy using a multi-scale self-attention mechanism and dynamic variable selection.
problem Accurately forecasting daily exchange rate returns in international finance.
method EXFormer uses a multi-scale trend-aware self-attention mechanism with dynamic variable selection and embedded squeeze-and-excitation blocks.
result EXFormer outperforms other models in forecasting daily exchange rate returns, achieving statistically significant improvements in directional accuracy.
Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, confide…
Researchers adaptively analyze market regimes to reveal investor behavior shifts.
problem Market relationships shift across different regimes, affecting investor behavior.
method Combining Kalman filtering, Markov-switching, and asymmetric response estimation.
result Foreign investors' predictive power increases during crises, while individual investors react more strongly to positive shocks.
Machine learning models predict EUR/USD currency direction with 58.52% accuracy.
problem Predicting the directional movement of EUR/USD in the Foreign Exchange market.
method Comparative analysis of machine learning models, including decorrelated and non-decorrelated feature sets, and meta-estimators.
result 58.52% accuracy for one-day ahead forecasts.
We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return volatility are not correlated, while intraday volatility is. We …
A new model captures irregularly spaced high-frequency prices and their volatility.
problem Modeling high-frequency prices with irregular spacing and market noise.
method Observation-driven model using Skellam distribution with time-varying volatility and smoothing splines.
result The model provides a good fit to IBM stock data and measures daily realized volatility.
A novel resampling technique addresses class imbalance in imbalanced datasets.
problem Class imbalance in real-world datasets, especially in rare event detection.
method Developed two oversampling algorithms: G1Nos 1-Nearest Neighbour.
result Our oversampling algorithms outperform state-of-the-art methods in all metrics.
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.
Volatility of S&P 500 daily returns increases over 60 years.
problem Why does S&P 500 daily volatility increase over time?
method Hypothetical market forces increasing volatility.
result Long-term volatility of S&P 500 daily returns will continue to increase until a threshold.
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.
DeePM is a deep-learning portfolio manager that outperforms classical strategies in diversified futures markets.
problem Maximizing risk-adjusted returns in financial markets with low signal-to-noise ratios and asynchronous data.
method Structured deep learning with a Directed Delay mechanism, Macroeconomic Graph Prior, and distributionally robust optimization.
result DeePM achieves net risk-adjusted returns roughly twice those of classical strategies and passive benchmarks.
New metric to measure liquidity position PNL, delta hedging algorithm for automated market makers.
problem Vulnerability of liquidity positions to price changes in underlying assets.
method Proposes a new metric for measuring PNL, delta hedging algorithm for various AMMs.
result New metric more accurately measures net value change due to price movement.
Using daily returns of the S&P 500 stocks from 2001 to 2011, we perform a backtesting study of the portfolio optimization strategy based on the extreme risk index (ERI). This method uses multivariate extreme value theory to minimize the probability of large portfolio losses. With more than 400 stocks to choose from, ou…
Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using the selected time series representing such divers…
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.
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
problem Predicting stock returns with AI, focusing on identifying top winners.
method Deployed a state-of-the-art LLM to autonomously search the web for stock attractiveness, avoiding look-ahead bias.
result AI can generate alpha by identifying top winners, but returns are highly concentrated.
The study examines portfolio optimization with quadratic transaction costs, complicating the optimization process.
problem Portfolio optimization with quadratic transaction costs is more challenging than with linear costs.
method Introduced numerical algorithms to solve the optimization problem with quadratic transaction costs.
result Quadratic transaction costs significantly impact the expected returns of optimized portfolios.