New approach identifies offshore financial centers in global corporate network.
problem Political scrutiny of offshore financial centers facilitating tax avoidance.
method Data-driven approach using a global corporate ownership network.
result Identification of 24 sink-OFCs and a set of five conduit-OFCs.
China uses two Renminbi markets to hedge cross-border risks, leading to a price discrepancy.
problem China's two Renminbi markets (onshore and offshore) create a price discrepancy for currency forwards.
method Joint equilibrium model for spot and forward trading with transaction costs and segmented supply.
result The model explains the observed forward price discrepancy in terms of offshore liquidity stress.
Machine learning analyzes AIS data to track offshore wind turbine installations.
problem Tracking and optimizing the installation of offshore wind turbines using jack-up vessels.
method Applied machine learning to AIS data from jack-up vessels to identify turbine locations and installation times.
result Automated identification of turbine locations and installation times using only AIS data.
DeepMIDE forecasts wind speeds across space, time, and height for offshore wind energy.
problem Forecasting wind speeds across multiple heights for large offshore wind turbines.
method Statistical deep learning model that jointly models wind speeds at different heights using a multi-output integro-difference equation.
result DeepMIDE forecasts outperform traditional methods in real-world offshore wind energy data.
Study reveals lead-lag patterns between onshore and offshore RMB exchange rates.
problem Understanding the lead-lag relationship between onshore and offshore RMB exchange rates.
method Employed the thermal optimal path method to analyze daily and minute-scale data.
result Lead-lag patterns are influenced by market factors and US dollar appreciation.
RAGuard improves safety in LLMs for offshore wind maintenance.
problem Conventional LLMs fail with specialised or unexpected scenarios in offshore wind maintenance.
method Integrates safety-critical documents alongside technical manuals in RAG framework.
result RAGuard increases safety recall from almost 0% to over 50% while maintaining technical recall above 60%.
Deep learning models predict call center volumes with seasonal patterns.
problem Forecasting call center volumes with complex seasonal behavior.
method Investigated recurrent neural networks (RNNs) including Elman, LSTM, and GRU models.
result Optimal RNN configurations outperform other forecasting techniques.
US firms improve ESG performance in response to China trade shock.
problem Impact of China trade shock on US ESG performance.
method Trade policy experiment exploiting tariff changes.
result Greater import competition from China increases US firm ESG performance.
A network-based approach identifies financial factors from asset interactions, explaining market dynamics.
problem Characterizing joint financial asset behavior through underlying drivers.
method Modeling market as coupled iterated maps, where asset returns depend on past returns and interactions.
result Stable patterns of co-movement (financial factors) emerge from asset interactions, explaining asset variance.
A simple Ising spin model which can describe the mechanism of price formation in financial markets is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes th…
Shapelet transform improves time series classification for earthquake, wind, and wave events.
problem Autonomous detection of specific events from large time series datasets in civil engineering.
method Shapelet transform for local similarity in time series subsequences, combined with machine learning.
result Shapelet transform yields a new feature representation for time series signals in civil engineering.
DCE learns customer embeddings from digital activity and financial context.
problem Comprehensive customer understanding in financial services.
method Leverages customers' digital activity and financial context to learn dense representations.
result DCE showed performance lift in three prediction problems.
Regshock visualizes financial risks to help regulators manage systemic shocks.
problem Managing systemic risks in financial networks.
method Risk-island visualization algorithm and regshock visual exploration approach.
result Demonstrated improved risk management and control capabilities.
Basic peculiarities of market price fluctuations are known to be well described by a recently developed random walk model in a temporally deforming quadric potential force whose center is given by a moving average of past price traces [Physica A 370, pp91-97, 2006]. By analyzing high-frequency financial time series of …
Study tests how U.S. equity prices align with global asset frequencies using financial variables.
problem Testing whether U.S. equity prices align with global asset frequencies using financial variables.
method Examines SPX and RUT gaps, uses OIS-based funding, volatility, trading-friction, financial-condition variables, and residual information.
result Gains in fit survive broad-dollar neutralization, alternative blocks, PCA, residualization, and nested horizon selection, supporting reduced-form P-Q alignment.
The possibility of latency arbitrage in financial markets has led to the deployment of high-speed communication links between distant financial centers. These links are noisy and so there is a need for coding. In this paper, we develop a gametheoretic model of trading behavior where two traders compete to capture laten…
Modern physics has demonstrated that matter behaves very differently as it approaches the speed of light. This paper explores the implications of modern physics to the operation and regulation of financial markets. Information cannot move faster than the speed of light. The geographic separation of market centers means…
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Benchmark detects decision-time leakage in financial backtests.
problem Detecting decision-time leakage in financial machine-learning backtests.
method Toggles one evaluation convention at a time around a clean t+1-open reference, holding other factors fixed. result Inflation is highly selective, affecting specific features and execution methods.
GPU acceleration speeds up financial machine learning training time.
problem Time-intensive classifier training in financial machine learning.
method Deployed NVIDIA GPUs for parallel high-speed arithmetic operations.
result Significantly faster training time achieved.
This work uses transfer entropy to reconstruct influential network between global stock markets.
problem Noise in network structure due to mismatch of scale between single market and global markets.
method Reconstructing influential network between ten typical stock markets using transfer entropy.
result Before financial crises, connection strength between markets reaches a maximum, acting as an early warning signal.
Deep learning improves portfolio management by optimizing asset weights.
problem Traditional portfolio managers are outperformed by deep learning models in trading.
method Proposes a deep reinforcement learning portfolio manager that allocates weights to assets.
result The proposed portfolio manager outperforms conventional managers in risk-adjusted returns.
Study reveals a hidden cost in derivatives markets through option-implied discount factors.
problem The hidden cost in derivatives markets, not visible in price space.
method Minute-level NBBO data on options, reduced-form specification linking carry gap to implementation risk, trading frictions, and financial conditions.
result An annualized carry gap exists, linked to implementation risk and financial conditions.
This review explores XAI in finance, highlighting common techniques and areas needing improvement.
problem Balancing accuracy and transparency in financial AI models.
method Bibliometric and content analysis of XAI applications in finance.
result Post-hoc interpretability techniques are most used in financial XAI.
Study causal financial signals for non-stationary markets, improving short-term forecasts.
problem Short-term forecasting in non-stationary financial markets under causal constraints.
method Construct causal signals from heterogeneous micro-features using causal centering, linear aggregation, Kalman filter, and forward-like operator.
result Causally constructed observables can exhibit substantial economic relevance in specific regimes but degrade under regime shifts.
Study arbitrage in financial markets with trading restrictions.
problem Arbitrage in financial markets with trading constraints.
method Portfolio optimization problems and discrete-time setup.
result Solvability of portfolio optimization problems equivalent to absence of first kind arbitrage.
Survey of large language models in financial prediction and trading.
problem Improving predictability and robustness of financial predictions and trading decisions.
method Task-centered taxonomy, review of empirical evidence, design patterns, benchmarks, and challenges analysis.
result Improved predictability and robustness of financial predictions and trading decisions through large language models.
FinReflectKG benchmarks financial QA by linking relevant context from a financial KG, improving model performance and efficiency.
problem Efficiently retrieving and navigating relevant financial information across diverse sources and years.
method A benchmark built on a temporally indexed financial KG, generating QA pairs via pattern-specific prompts and quality control, evaluating retrieval scenarios.
result KG-guided retrieval yields substantial gains in correctness and token utilization, improving model performance by 24%.
The k-means++ algorithm is generalized by choosing the most distant point from the nearest center.
problem Improving the initialization of k-means clustering.
method Generalizing the center initialization process by selecting the most distant point from the nearest center.
result Choosing the most distant point from the nearest center achieves similar clustering quality to k-means++.
A new method centers outliers in robust PCA without manual intervention.
problem Outliers in robust PCA require manual centering, complicating the analysis.
method Introduces a 'bias trick' to automatically center non-outliers.
result First optimal RPCA algorithm with automatic centering.
Center identified in stated skein algebra for quantum traces.
problem Understanding the center of the stated skein algebra.
method Analyzing the algebra as a generalization of Kauffman bracket skein algebra, focusing on the case when the quantum parameter is a root of unity.
result Simple description and dimension calculation of the center over the center module.
Uniform bounds on center leaves volume for codimension one center foliations.
problem Bounding the volume of center leaves in codimension one center foliations.
method Analyzing dynamically coherent partially hyperbolic diffeomorphisms with one-dimensional unstable bundle.
result Volume of center leaves is uniformly bounded.
The object of our investigation is a point that gives the maximum value of a potential with a strictly decreasing radially symmetric kernel. It defines a center of a body in Rm. When we choose the Riesz kernel or the Poisson kernel as the kernel, such centers are called a radial center or an illuminating center, respec…
Refines geometric center of mass analysis for Einstein field equations.
problem Analyzing the geometric center of mass of Willmore surfaces in initial data for Einstein field equations.
method Refined Lyapunov-Schmidt analysis to study geometric center of mass of area-constrained Willmore surfaces.
result The geometric center of mass agrees with the Hamiltonian center of mass under specific conditions.
The paper proves the existence of a center in disks within Riemannian manifolds.
problem Proving the existence of a center in disks within Riemannian manifolds.
method Analyzing the existence of a center in C1 embedded k-disks in Riemannian n-manifolds. result Existence and equivariance of centers in certain conditions, non-existence in others.
Method reconstructs missing wind farm data using graph theory and nearest neighbors.
problem Missing data in wind farm records due to sensor failures.
method Combines spectral graph theory and k-Nearest Neighbors to estimate missing data.
result Significant improvement in data reconstruction over existing methods.
Bayesian method corrects misspecified volatility estimation in high-frequency financial data.
problem Volatility estimation in financial data with infinite jump activity and microstructure noise.
method Proposes a misspecified posterior corrected by a simple estimate of the location shift and re-scaling of the log likelihood.
result Establishes a Bernstein-von Mises theorem for the adjusted posterior, showing asymptotic Gaussianity and consistent estimation.
Proves existence of equilibrium in limited participation economy.
problem Existence of an equilibrium in an economy with limited financial market access.
method Proves global existence of Radner equilibrium using BSDEs with unique solution.
result Proves existence of Radner equilibrium with limited participation.
Financial markets provide an ideal frame for the study of crossing or first-passage time events of non-Gaussian correlated dynamics mainly because large data sets are available. Tick-by-tick data of six futures markets are herein considered resulting in fat tailed first-passage time probabilities. The scaling of the re…
Let f:M->M be a partially hyperbolic diffeomorphism such that all of its center leaves are compact. We prove that Sullivan's example of a circle foliation that has arbitrary long leaves cannot be the center foliation of f. This is proved by thorough study of the accessible boundaries of the center-stable and the center…
A new method for clustering using graph connectivity centers.
problem Inappropriate scale leads to unreasonable cluster centers.
method Convert data similarity to graph connectivity, define connection center as cluster center, and use powers of similarity matrix for dynamic evolution.
result Cluster centers evolve naturally from local to global, suggesting appropriate scales and skipping unreasonable clusters.
We investigate centers of a body (the closure of a bounded open set) defined as maximum points of potentials. In particular, we study centers defined by the Riesz potential and by Poisson's integral. These centers, in general, depend on parameters and move with respect to the parameters. We give a necessary and suffici…
A new method for brain tissue segmentation across medical centers using a smoothness prior.
problem Tissue segmentation challenges due to center-specific acquisition protocols.
method Developed a smoothness prior that is fit to segmentations from another medical center, integrated into an unsupervised Bayesian model.
result Segmentations are similarly smooth across centers, improving generalization.
This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …
The paper quantizes concatenated noisy vectors to a common cluster center, improving performance over naive methods.
problem Clustering concatenated noisy vectors from multiple sources.
method Asymptotic analysis of weighted sum of distances to a common cluster center.
result The clustering approach outperforms naive methods in terms of average distortion.
Bayesian approach estimates sub-resolution reservoir properties from seismic data.
problem Estimating sub-resolution reservoir properties from seismic data.
method Bayesian evidential learning approach, direct relation between seismic data and reservoir properties.
result Efficient estimation of reservoir properties with uncertainty quantification.
In this study, we investigate the locus of the centers of the Meusnier spheres. Just as focal curve is the locus of the centers of the osculating spheres, we investigate the geometrical interpretation on the locus of the centers of the Meusnier spheres. We proved that if the curve is a principal line, the locus of the …
Over-the-counter markets can change drastically due to portfolio compression, affecting their size and structure.
problem Understanding and managing changes in over-the-counter markets due to portfolio compression.
method Analysis of transaction-level data on credit-default swaps markets, study of mandate effects on central clearing and portfolio compression.
result Portfolio compression can lead to significant reductions in market notional and network structure, and mandates for central clearing can exacerbate these effects.