We develop an axiomatic theory of balance functions (future value functions) in the theory of interest that is derived from financial considerations and which applies to general regulated payment streams, including continuous payment streams. Balance functions exist and are unique up to an initial choice of deposit and…
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
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48 results for “internal balancing”
Paper optimizes internal balancing of wind and hydropower to reduce intraday market volatility.
problem Reduction of intraday market volatility for power producers with wind and hydropower assets.
method Internal balancing within the same river system and sales/purchase in a pay-as-bid intraday market.
result Reduction in short-term marginal cost and risk through internal balancing.
Optimized execution model using interbank and internal liquidity.
problem Minimizing market impact in trading.
method Integrates interbank limit and market orders with internal market-making liquidity.
result Reduces market impact and improves execution efficiency.
Study uses Google matrix analysis to show how COVID-19 changed international trade flows.
problem Impact of COVID-19 on international trade patterns.
method Google matrix analysis of World Trade Network (WTN), including PageRank, CheiRank, and reduced Google matrix.
result Significant changes in international trade flows due to the pandemic, affecting export and import balances.
This paper studies four trading algorithms of a professional trader at a multilateral trading facility, observing a realistic two-sided limit order book whose dynamics are driven by the order book events. The identity of the trader can be either internalizing or regular, either a hedge fund or a brokery agency. The spe…
The paper analyzes trade dynamics among G7 countries, revealing unequal exchange and degenerate equilibrium states.
problem Unequal exchange and degenerate equilibrium states in international trade among G7 countries.
method Analysis based on a model of international trade with supply and demand structures.
result Found relative equilibrium price vector is very degenerate, indicating unequal exchange.
Market making by an FX dealer: tiers, pricing ladders and hedging rates for optimal risk controlq-fin.TR
This paper offers a framework for FX dealers to decide between internalizing and externalizing their market making to balance risk control and costs.
problem FX dealers face risk from flow uncertainty and need to decide on internalization vs. externalization strategies.
method Develops an optimal control framework that balances pricing and hedging strategies.
result Provides insights into the trade-off between risk control and transaction costs in market making.
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomaliescs.SI
Unified model for signed networks separates balance and anomaly effects.
problem Ignoring sign information in signed networks leads to inaccurate analysis.
method Low rank plus sparse matrix decomposition with regularized formulation.
result The model accurately detects communities and anomalies in signed networks.
Fuzzy eIX method evolves classifiers for online data streams.
problem Handling time-varying classifiers in online data streams.
method Develops evolving Internal-eXternal Fuzzy granules for numerical data.
result Fuzzy eIX maintains high accuracy in dynamic scenarios.
New categories from TQFTs interpret skein relations.
problem Interpreting skein relations in TQFTs.
method Constructing half-braided algebras and their bimodules.
result Stated skein relations correspond to a TQFT.
Automatically balances blockchain network resources to boost market efficiency.
problem Extractable value leakage and execution frictions in blockchain networks.
method Systematically uses idle network resources for arbitrage, incentivizing transactions.
result Reduces network inventory risk while enhancing price formation and liquidity.
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deliver the same examples to a diverse group of students. Their diversity stems from differences in their initial internal states as well as their learning rates. We prove that a teacher with full knowledge about the learnin…
Enhances fairness in predictions without sacrificing accuracy.
problem Balancing fairness and predictive performance in machine learning.
method Model ensemble-based post-processing framework.
result Framework effectively enhances fairness while maintaining predictive accuracy.
HybridCGAN improves portfolio analysis by balancing trend prediction and market uncertainty.
problem Markowitz framework's overemphasis on market uncertainty and trend prediction.
method A hybrid approach combining deep generative models to balance trend prediction and market uncertainty.
result HybridCGAN leads to better portfolio allocation compared to existing methods.
We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.
problem Fairness and robustness in principal component analysis for consequential domains.
method Distributionally robust optimization over the Stiefel manifold with a Riemannian subgradient descent.
result The proposed method achieves better performance on real-world datasets compared to state-of-the-art baselines.
We address a class of schemes for the Euler equations with the following features: the space discretization is staggered, possible upwinding is performed with respect to the material velocity only and the internal energy balance is solved, with a correction term designed on consistency arguments. These schemes have bee…
Neural Markov models improve time series analysis by balancing deep learning and classical models.
problem Modeling non-stationary time series with high data sparsity.
method Hybrid approach using neural networks to parameterize stochastic matrices, estimating time-inhomogeneous Markov chains.
result Reduction of Chapman-Kolmogorov discrepancy and superior likelihood in financial markets.
Neural network models colloidal particle dynamics in non-equilibrium systems.
problem Analyzing non-equilibrium dynamics of many-body colloidal systems.
method Combining power functional theory and machine learning, training a neural network to predict internal force fields.
result The neural network accurately predicts dynamics in non-equilibrium systems, in good agreement with simulations.
How crude oil prices shape the global division of labourphysics.soc-ph
Our work sheds new light on the role of oil prices in shaping the world economy by investigating flows of goods and services through global value chains between 1960 and 2011, by means of Markov Chain and network analysis. We show that over that time period the international division of labor and trade patterns are tig…
Nonlinear soft-tissue elasticity, remodeling, and degradation described by an extended Finsler geometrycond-mat.soft
Theory models nonlinear soft tissue elasticity and remodeling using extended Finsler geometry.
problem Understanding and predicting the behavior of nonlinear soft tissues, especially in biologic contexts.
method Formulated a continuum mechanical theory incorporating extended Finsler geometry to describe the complex behaviors of fibrous soft solids.
result The model quantifies residual strains from growth, remodeling, and degradation, and predicts equilibrium configurations.
Internal Lagrangians derived from variational principles.
problem Reproducing the principle of stationary action in variational geometry.
method Introducing stationary points of internal Lagrangians, establishing connections with symmetries and conservation laws, and investigating relations between non-degenerate and internal Lagrangians.
result Noether's theorem reformulated in terms of internal Lagrangians.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
problem Predicting and mitigating internal fraud losses in retail banking.
method Developed a dynamic model considering internal factors and macroeconomic indicators.
result Internal fraud losses are pro-cyclical and positively affected by corruption perceptions.
TS-Insight visualizes Thompson Sampling for better debugging and trust.
problem Thompson Sampling's black box nature hinders debugging and trust.
method TS-Insight is a visual analytics tool that traces evolving posteriors and evidence counts.
result Visualizations help in verifying, diagnosing, and explaining Thompson Sampling dynamics.
ML Compass helps organizations choose AI models that balance utility, cost, and compliance.
problem Selecting AI models that meet user utility, deployment costs, and compliance requirements.
method Develops ML Compass, a framework for constrained optimization over a capability-cost frontier, using internal measures and empirical data.
result ML Compass produces deployment-aware recommendations that differ from capability-only rankings, clarifying trade-offs between capability, cost, and safety.
Optimizes SGD for anytime neural networks, improving accuracy.
problem Training networks that produce increasingly accurate outputs over time.
method Orthogonalized SGD optimizer for nested architectures.
result Significantly improves generalization accuracy of anytime networks.
Examines international taxation's impact on Georgian businesses.
problem Impact of international taxation on Georgian business entities.
method Analyzes types, features, and approaches of international taxation.
result Provides recommendations to correct current taxation issues.
Impact of shocks to economies on the efficiency and robustness of the international pesticide trade networksphysics.soc-ph
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
problem Economic shocks impact the efficiency and robustness of international pesticide trade networks.
method Simulations were used to quantify efficiency and robustness under different economic shocks. Three strategies were tested: descending, random, and ascending node removal.
result The international pesticide trade networks became more efficient and robust except for clustering coefficient. Import-oriented economies were more vulnerable to shocks.
We present SEBOOST, a technique for boosting the performance of existing stochastic optimization methods. SEBOOST applies a secondary optimization process in the subspace spanned by the last steps and descent directions. The method was inspired by the SESOP optimization method for large-scale problems, and has been ada…
There are few papers about the international trade of flowers, so it is believed that this paper, with this topic, could be an important contribution to the international scientific community. It is intended to analyze if the international trade flowers tendencies and policies are adapted to the actual world global con…
Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their internal mechanisms in order to make them more reliable. These models appear to store vast amounts of knowledge from their training data, and…
Spain uses DEA to select international markets for exports.
problem Choosing the best international markets for Spanish exports.
method Output-oriented DEA model considering multiple validated inputs and outputs.
result Identifies efficient regions generating higher benefits with lower costs.
Improved speech recognition model with better performance.
problem Speech recognition accuracy on Librispeech.
method Integrates an external language model with an internal LM correction.
result Over 14% relative improvement in performance.
Introduces internal Lagrangians for differential equations and connects them to presymplectic structures.
problem Understanding the geometry of differential equations and their solutions.
method Develops a spectral sequence related to internal Lagrangians and investigates connections to presymplectic structures.
result Interprets a term in Vinogradov's spectral sequence for gauge theories.
Behavioral analysis of support vector machine classifier with Gaussian kernel and imbalanced datacs.LG
Analyzes SVM classifier behavior with different parameters and data types.
problem Tuning SVM parameters for balanced and imbalanced data.
method Behavioral analysis of SVM with different parameters and data types, proposing a novel search algorithm.
result Proposed search algorithm reduces computational time and provides expected kernel function range.
New findings show that some knotted surfaces remain distinct even after many stabilizations.
problem Understanding the behavior of knotted surfaces after internal stabilizations.
method Analyzing the properties of smoothly knotted surfaces and their behavior under internal stabilizations.
result There is no upper bound on the number of internal stabilizations required for some knotted surfaces to remain distinct.
Uncertainty quantification of neural network models of evolving processes via Langevin samplingcs.LG
A scalable framework uses Langevin sampling to approximate neural network models of evolving processes.
problem Uncertainty quantification in neural network models of dynamic systems.
method Flexible data model based on NODE, joint learning of data model and posterior parameters, Langevin sampling.
result Demonstrated performance on chemical reaction and material physics data, compared favorably to variational inference.
Conformal Surface Splinesmath.DG
New boundary and point constraints for controlling conformal surfaces.
problem Controlling the geometry of surfaces defined by minimizers of conformal variational problems.
method Introducing new boundary conditions, point constraints, and flux constraints to control the metric and conformal scale factor.
result Introduces intuitive controls for exploring a subspace of conformal immersions.
This paper develops a non-Bayesian methodology to analyze the time-varying structure of international linkages and market efficiency in G7 countries. We consider a non-Bayesian time-varying vector autoregressive (TV-VAR) model, and apply it to estimate the joint degree of market efficiency in the sense of Fama (1970, 1…
Approach for modeling EHR data with rare features, improving prediction and interpretation.
problem Challenges in modeling rare binary features in EHR data.
method Tree-guided feature selection and logic aggregation for large-scale regression.
result Improved prediction and model interpretation of suicide risk in EHR data.
Improved clustering accuracy with disentangled latent code representation.
problem Improving k-Means clustering performance.
method Optimizing the entanglement of autoencoder latent code representation using soft nearest neighbor loss with annealing temperature.
result 96.2% test clustering accuracy on MNIST, 85.6% on Fashion-MNIST, and 79.2% on EMNIST Balanced datasets.
Investigates optimal strategies for market makers using internal liquidity.
problem Optimizing strategies for market makers with internal liquidity.
method Investigates optimal multi-objective strategy for market makers with internal liquidity.
result Draws important qualitative insights for real-world trading.
Mathematical model predicts international trade and global economy dynamics.
problem Understanding complex international trade and economy interactions.
method Developed a mathematical model for non-equilibrium processes in open systems.
result Predicted model accurately reflects international trade and economy.
Random feature maps improve forecasting with cheaper computation.
problem Improving forecasting accuracy with random feature maps.
method Developed a hit-and-run algorithm to select optimal internal weights.
result Optimal internal weights lead to superior forecasting skill.
Study uses AI to predict changes in international public finances based on US markets.
problem Understanding correlations between US and international public finances.
method Artificial intelligence and neural networks to model and predict changes.
result Neural network model achieved MSE of 2.79, indicating significant correlation and impact of US market volatility on international markets.
The International Trade Networkq-fin.GN
Bilateral trade relationships in the international level between pairs of countries in the world give rise to the notion of the International Trade Network (ITN). This network has attracted the attention of network researchers as it serves as an excellent example of the weighted networks, the link weight being defined …
Improves LSTM performance by initializing states via manifold learning.
problem Improving LSTM performance through better initialization.
method Learning an intrinsic data manifold to initialize LSTM internal states.
result Improved LSTM performance through consistent initialization.
To meet the Basel II regulatory requirements for the Advanced Measurement Approaches in operational risk, the bank's internal model should make use of the internal data, relevant external data, scenario analysis and factors reflecting the business environment and internal control systems. One of the unresolved challeng…
This paper evaluates and validates cluster results using external and internal evaluation methods.
problem Evaluating and validating the quality of clustering results.
method External evaluation using Homogeneity, Correctness, and V-measure scores; internal evaluation using Silhouette Index and Sum of Square Errors.
result Validation of the number of clusters using dendrogram and statistical frequency distribution.