Study finds managers' tenure and education influence their choice between in-court and out-of-court restructuring.
problem Exploring managers' characteristics and their impact on restructuring decisions.
method Empirical investigation using upper echelons theory and data from 342 managers of French firms.
result Managers with longer tenure and higher education levels prefer private restructuring over court involvement.
The paper analyzes XVA reduction strategies in financial crises using Mandatory Breaks, Restructuring, and Resets.
problem Challenges in client XVA management during crises when continuous collateralization is not feasible.
method Compares multiple trade strategies including Mandatory Breaks, Restructuring, and Resets.
result Resets can be twice as effective as Mandatory Breaks/Restructuring if there is no credit recovery. When recovery is at least 1/3, Mandatory Breaks/Restructuring can be more effective.
Improves parallel deep model performance by restructuring and pruning.
problem Latency in parallel deep model execution due to interdependency among sub-models.
method Layer-wise model restructuring and pruning, using ℓ0 optimization and Munkres assignment algorithm. result Significantly improves efficiency of distributed inference in terms of communication and computational complexity.
New activation networks improve model efficiency and performance.
problem Creating hardware-efficient deep learning models.
method Restructurable Activation Networks (RANs) with RAN-explicit and RAN-implicit methods.
result RANs achieve state-of-the-art results with improved hardware efficiency.
We show how to restructure the counterparty risk faced by the originator of a securitization or covered bond arising from an interest rate hedging swap assisted by a "one-way" collateral agreement. This risk emerges when the swap is negotiated between the special purpose vehicle and a third party that covers itself thr…
In this paper, we introduce an insurance ruin model with adaptive premium rate, thereafter refered to as restructuring/refraction, in which classical ruin and bankruptcy are distinguished. In this model, the premium rate is increased as soon as the wealth process falls into the red zone and is brought back to its regul…
Simplified feature selection using a single agent with restructured choice strategy.
problem Efficiency and cost issues in multi-agent reinforced feature selection.
method Single-agent approach with restructured choice strategy, including scanning method, feature prioritization, state representation, and reward scheme.
result Improved efficiency and effectiveness of feature selection.
One of the main issues affecting the Italian NHS is the healthcare deficit: according to current agreements between the Italian State and its Regions, public funding of regional NHS is now limited to the amount of regional deficit and is subject to previous assessment of strict adherence to constraint on regional healt…
Machine learning refactors knowledge to improve learning efficiency.
problem Inductive program synthesis efficiency through knowledge restructuring.
method Introduces Knorf, a system that refactors knowledge bases using constraint optimization.
result Learning from refactored knowledge improves predictive accuracy fourfold and reduces learning time by half.
New principle reveals how neural networks learn complex interactions.
problem Understanding neural networks' success and complexity.
method Infinite-width networks, focusing on frequency and space.
result Fine-grained eigenstructure improves network learnability.
We show how the cost of funding the collateral in a particular set up can be equal to the Bilateral Valuation Adjustment with the "funded" probability of default, leading to the definition of a Funded Bilateral Valuation Adjustment (FBVA). That set up can also be viewed by an investor as an effective way to restructure…
Counterparty Risk FAQ: Credit VaR, PFE, CVA, DVA, Closeout, Netting, Collateral, Re-hypothecation, WWR, Basel, Funding, CCDS and Margin Lendingq-fin.PR We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral …
Extends knot concordance invariant to balanced spatial graphs using grid homology.
problem Defining a concordance invariant for balanced spatial graphs.
method Using grid homology to extend the invariant from knots to spatial graphs.
result The combinatorial Υ invariant is a concordance invariant for balanced spatial graphs. European steel industry shifts to electric arc furnaces, reducing scrap imports and increasing competition.
problem Reducing CO2 emissions in the European steel industry through electric arc furnaces.
method Combining trade data with business intelligence to model the impact of EAF capacity on scrap trade.
result Scrap imports decrease as EAF capacity increases, highlighting the need for a new business ecosystem.
We introduce an innovative theoretical framework to model derivative transactions between defaultable entities based on the principle of arbitrage freedom. Our framework extends the traditional formulations based on Credit and Debit Valuation Adjustments (CVA and DVA). Depending on how the default contingency is accoun…
Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid p…
We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve…
New method for individual claims reserving using machine learning.
problem Traditional claims reserving methods are limited in individual claim prediction.
method Restructured data utilization for CL prediction, using multi-period factors.
result Neural networks applied for individual claims reserving.
Within the last two decades, Foreign Direct Investment (FDI) has been observed as one of the prime instruments in the process of restructuring the European economies in transition. Many scholars argue that FDI is expected to be a source of valuable technology transfer thus might certainly have positive effects on host …
In a densely populated city like Dhaka (Bangladesh), a growing number of high-rise buildings is an inevitable reality. However, they pose mental health risks for citizens in terms of detachment from natural light, sky view, greenery, and environmental landscapes. The housing economy and rent structure in different area…
We propose a simple quantitative model of Schumpeterian economic dynamics. New goods and services are endogenously produced through combinations of existing goods. As soon as new goods enter the market they may compete against already existing goods, in other words new products can have destructive effects on existing …
This paper presents a fast Bayesian filtering technique for state estimation.
problem Bottleneck in Bayesian inference for state estimation from noisy sensor data.
method Processor-native uncertainty tracking for uncertainty propagation and inference.
result Deterministic approximate filtering with up to 805x speedup and competitive accuracy.
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage. In order to alleviate the computation and storage burdens, we propose a novel dataflow-based joint quantization approach with the hypothesis…
Online news media provides aggregated news and stories from different sources all over the world and up-to-date news coverage. The main goal of this study is to have a solution that considered as a homogeneous source for the news and to represent the news in a new conceptual framework. Furthermore, the user can easily …
We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model sparsity or the loading vector representing the hypothesis. Providing asymptotically valid methods for testing general linear functions of the …
Bayesian DOE accelerates experimental design with improved efficiency.
problem Enhancing experimental design efficiency and reliability.
method Bayesian framework, conditional density estimation, informative data selection.
result Significantly improved computational efficiency of experimental design.
This research formalizes inductive generalization and proposes a new learning paradigm called Inductive Learning.
problem Generalization from easy to hard tasks, especially out-of-domain generalization.
method Formalizes inductive generalization, introduces Inductive Learning, and outlines steps to adapt techniques for learning model successors.
result A new learning paradigm (Inductive Learning) that emphasizes induction and universal properties of learning and computation.
A model predicts visual motion by learning from natural videos.
problem Temporal prediction accuracy in visual perception.
method Self-supervised representation learning using Fourier shift theorem.
result Achieves better prediction performance than traditional methods.
New memory allocation scheme improves image generation performance.
problem Improving episodic and semantic memory representation in neural networks.
method Developed a hierarchical latent variable model with differentiable, locally block allocated latent memory.
result Improved conditional likelihood values on various datasets.
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…
Financial markets are exposed to systemic risk (SR), the risk that a major fraction of the system ceases to function, and collapses. It has recently become possible to quantify SR in terms of underlying financial networks where nodes represent financial institutions, and links capture the size and maturity of assets (l…
Paper presents a multi-label topic model for financial texts with high performance and insights into market reactions.
problem Analyzing financial text data for market reactions and understanding topic interactions.
method Trained a multi-label topic model on a financial text database, achieved high macro F1 score, and investigated topic interactions.
result Model achieves high performance (macro F1 > 85%) and reveals significant market reactions to topic co-occurrences.
This work presents the results of an empirical research with the target of modeling the stylized facts of the daily expost System Marginal Price (SMP) of the Greek wholesale electricity market, using data from January 2004 to December of 2011. SMP is considered here as the footprint of an underline stochastic and nonli…
This paper proposes a boosting-based solution addressing metric learning problems for high-dimensional data. Distance measures have been used as natural measures of (dis)similarity and served as the foundation of various learning methods. The efficiency of distance-based learning methods heavily depends on the chosen d…
We study spaces with a cuspidal (or horn-like) singularity embedded in a smooth Riemannian manifold and analyze the geodesics in these spaces which start at the singularity. This provides a basis for understanding the intrinsic geometry of such spaces near the singularity. We show that these geodesics combine to natura…
The study examines spectral dynamics in deep neural networks, predicting how outliers evolve during training.
problem Understanding spectral evolution in deep neural networks during training.
method Developed a two-level dynamical mean-field theory (DMFT) to track spectral dynamics.
result The theory predicts how outliers evolve with training time, width, output scale, and initialization variance.
Study models stock price recovery during COVID-19, distinguishing V and L-shape recoveries.
problem Analyzing stock price recovery during the COVID-19 pandemic.
method Developed a stock price model based on net-fund-flow and financial antifragility.
result Quality stocks with higher financial antifragility show V-shape recovery, while those with lower antifragility show L-shape recovery.
Reinforcement learning (RL) algorithms allow artificial agents to improve their selection of actions to increase rewarding experiences in their environments. Temporal Difference (TD) Learning -- a model-free RL method -- is a leading account of the midbrain dopamine system and the basal ganglia in reinforcement learnin…
Deep learning predicts M&A events in industry networks.
problem Predicting M&A behaviors in competitive industries with complex interdependencies.
method Temporal Dynamic Industry Network (TDIN) model using temporal point processes and deep learning.
result Effective M&A event prediction and actionable recommendations.
Improved power arbitrage through domain-adapted reinforcement learning.
problem Optimizing profit in the Dutch power market through arbitrage opportunities.
method Dual-agent reinforcement learning with imitation of power traders' behaviors.
result Significant improvement in cumulative profit and loss (P&L) with a three-fold increase.
Deep Neural Networks (DNNs) have become a powerful toolfor a wide range of problems. Yet recent work has found an increasing variety of adversarial samplesthat can fool them. Most existing detection mechanisms against adversarial attacksimpose significant costs, either by using additional classifiers to spot adversaria…
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
problem Traditional bankruptcy prediction models rely on accounting ratios, which may not capture early warning signals.
method Developed a PB Stress Score based on distress-specific language in 10-K narratives, evaluated against accounting and dictionary benchmarks.
result Adding the PB Stress Score increases AUC from 0.8323 to 0.9019 and improves top-decile bankruptcy capture from 44.12% to 64.71%.
Mathematical framework using Riemannian geometry for intelligence and consciousness.
problem Lack of a unified mathematical framework for intelligence and consciousness.
method Conceptualizes intelligence as tokens in a high-dimensional space, using Riemannian geometry to describe structure and dynamics.
result Integrates geometric concepts to offer a unified framework for intelligence and consciousness.
The study analyzes aftershocks of stock market crashes using statistical methods.
problem Understanding the aftershocks of stock market crashes during crises.
method Structural break analysis and statistical methods applied to 1987 crash, 2008 financial crisis, and 2020 COVID-19 pandemic.
result The recovery of stock price during the COVID-19 pandemic may be faster than the financial crisis of 2008.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.