Study examines financial structure's impact on non-financial firms' growth in Kenya.
problem Declining financial performance and growth of non-financial firms listed at Nairobi Securities Exchange.
method Analyzes the effect of financial structure on financial growth.
result Established the impact of financial structure on non-financial firms' growth.
Model predicts future stock market structure using social and financial network data.
problem Predicting future stock market structure with high accuracy.
method Combines financial and social media network information using a multiplex network approach.
result Up to 40% out-of-sample performance improvement in predicting future market structure.
Modeling financial institution dependence structures for systemic risk.
problem Understanding and measuring systemic risk in financial systems.
method Dynamic model of dependence structure using Markov structures of joint credit migrations.
result Different Markov structures with distinct dependence structures lead to varying systemic instability.
Develops a new method for financial term structure modeling.
problem Analyzing financial term structures with discontinuities.
method Cylindrical stochastic integration approach.
result Establishes a Heath-Jarrow-Morton framework.
Develops a framework to analyze financial structures.
problem Difficulty in systematic analysis, comparison, and verification of financial structures.
method Formalizes financial structures as structured allocation systems with explicit allocation operators.
result Specifies inputs, structural requirements, and feasibility restrictions for financial structures.
CoCos can increase financial fragility in certain network structures.
problem The effectiveness of CoCos in enhancing financial stability depends on the network structure.
method Analysis of phase transitions in a network of interconnected banks.
result CoCos can increase financial fragility under certain network structures.
Model shows financial turbulence similar to turbulence, with wealth cascading from large to small entities.
problem Understanding wealth distribution and dynamics in financial systems.
method Constructed a multiscale model for hierarchical financial structures.
result Found wealth distribution exhibits power law at large scales and Maxwellian at small scales.
Study examines how mergers and acquisitions affect Indian banks' financial performance and capital structure.
problem Impact of mergers and acquisitions on Indian banks' financial performance and capital structure.
method Statistical analysis using paired t-test on selected banks' annual reports.
result Mergers and acquisitions significantly impact financial performance and capital structure of Indian banks.
Bayesian networks model financial contagion in interconnected institutions.
problem Understanding and predicting contagion in financial networks.
method Structural default model using Bayesian network methodologies.
result Bayesian networks can detect contagion channels and measure systemic importance.
The recent financial crisis has stressed the need to understand financial systems as networks of interdependent countries, where cross-border financial linkages play the fundamental role. It has also been emphasized that the relevance of these networks relies on the representation of changes follow-on the occurrence of…
The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they shared. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviated from Kolmogorov monofractal scaling of the velocity fluctu…
Combining neural networks and multiscale decomposition for financial market analysis.
problem Financial markets' complexity and mainstream models' limitations in capturing non-linear structures.
method Neural networks for non-linear associations combined with multiscale decomposition.
result Improved understanding of financial market data substructures.
Study reveals structural differences in financial networks near and far from crises using balance theory.
problem Understanding the complex behavior of stocks and their collective behavior in financial crises.
method Investigates financial networks by triplet interaction in the framework of balance theory, focusing on higher-order interactions.
result Formation of an ordered structure in crisis networks makes them resistant to disorder, with a critical temperature measuring crisis strength.
Develops Bilateral Gamma processes for financial market modeling.
problem Modeling financial market fluctuations with Lévy processes.
method Exploration of bilateral Gamma distributions and their Lévy processes.
result Validates Bilateral Gamma processes on real financial data.
LLMs improve financial analysis by processing large data sets.
problem Traditional financial analysis methods struggle with large data volumes.
method Integrating LLMs for enhanced data processing and analysis.
result LLMs offer new capabilities for real-time financial decision-making.
Detects financial crises early using network topology.
problem Early detection of financial crises.
method Topology data analysis of correlation networks.
result Early signs of critical transitions in financial data.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
problem Detecting anomalies in complex financial time series with high-dimensional data.
method Integrates machine learning with econometric diagnostics in a refined convolutional--transformer architecture.
result Unified anomaly score without labeled data, robust to structured deviations.
Spectral denoising recovers meaningful network structure from noisy financial correlations.
problem Noise in empirical correlation matrices from financial returns obscures genuine interactions.
method Spectral decomposition to separate structured and random components.
result Structured networks derived from 10-16 eigenmodes exhibit stronger core-periphery organization and scale-free degree distributions.
Generative model for financial time series using structured noise and signature learning.
problem Creating synthetic financial data to reflect real-world market dynamics.
method Structured noise, moving average model, signature transform, reinforcement learning.
result Model effectively captures key financial characteristics and outperforms existing methods.
This study examines the evolving causal structure of equity risk factors.
problem Redundancy and risk contagion in multi-factor strategies during financial crises.
method Causal structure learning methods applied to US equity market data over 29 years.
result Statistically significant sparsifying trend of causal structure during normal times, but densification during financial stress.
Following the financial crisis of 2007-2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the…
With the network methods and random matrix theory, we investigate the interaction structure of communities in financial markets. In particular, based on the random matrix decomposition, we clarify that the local interactions between the business sectors (subsectors) are mainly contained in the sector mode. In the secto…
Study identifies key ESG variables for assessing financial risk.
problem Assessing financial risk from ESG data with many variables.
method Proposed framework for hierarchical ESG data, selecting relevant variables.
result Selected ESG variables are more relevant to financial risk than aggregated scores.
Study reveals changes in correlation between Greece's electricity and stock markets during financial crisis.
problem Understanding volatility and correlation structure between electricity and financial markets during crises.
method Applied Dynamic Conditional Correlation (DCC) GARCH model to fundamental variables and market indexes.
result Identified structural changes in correlation patterns during the Greek financial crisis.
Atoms and molecules are important conceptual entities we invented to understand the physical world around us. The key to their usefulness lies in the organization of nuclear and electronic degrees of freedom into a single dynamical variable whose time evolution we can better imagine. The use of such effective variables…
Study shows group structures are crucial for financial model explanations.
problem Inconsistent explanations from existing explainable machine learning methods.
method Examined group structures in financial datasets and developed group versions of Shapley values.
result Group versions of Shapley values provide consistent explanations.
We compare correlations and coherent structures in nuclei and financial markets. In the nuclear physics part we review giant resonances which can be interpreted as a coherent structure embedded in chaos. With similar methods we investigate the financial empirical correlation matrix of the DAX and Dow Jones. We will sho…
Study reveals multiple core-periphery structures in interbank markets, transforming during financial crises.
problem Understanding the complex structure and transformation of interbank markets during financial crises.
method Novel core-periphery detection method on eMID interbank market data.
result Interbank markets exhibit multiple core-periphery pairs and transition to bipartite structures over short time scales.
Cross-border equity and long-term debt securities portfolio investment networks are analysed from 2002 to 2012, covering the 2008 global financial crisis. They serve as network-proxies for measuring the robustness of the global financial system and the interdependence of financial markets, respectively. Two early-warni…
This paper models and evaluates contagion and stabilisation in interconnected financial markets.
problem Understanding and managing contagion and resilience in multilayer financial networks.
method Formulates an interconnected multiplex structure, models contagion mechanism, and designs minimum-cost stabilisation strategies.
result Empirically validated minimum-cost stabilisation strategies for multichannel contagion containment.
Method detects phase transitions in financial markets using eigenvalue decomposition.
problem Detecting tipping points and fluctuation patterns in financial markets.
method Eigenvalue decomposition and eigen-entropy from cross-correlation matrix.
result Market events undergo phase separation and order-disorder transitions.
NAS for financial time series forecasts using chain-structured architectures.
problem Optimizing neural architectures for financial time series forecasting.
method Comparison of three NAS strategies (Bayesian optimization, hyperband, reinforcement learning) on chain-structured search spaces for simple and complex architectures.
result Bayesian optimization and hyperband outperform other strategies, and RNN and 1D CNN perform best among architectures.
Review of correlation-based financial networks and entropy measures.
problem Understanding the dynamics of financial markets through correlation networks.
method Analysis of empirical correlation matrices and entropy measures.
result Entropy measures help in continuous monitoring of financial networks.
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…
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.
The paper surveys network methods for understanding economic and financial systems.
problem Understanding interconnectedness among economic and financial entities.
method Survey of network theory, measures, and structures for economic and financial networks.
result Network methods provide tools to quantify structural properties of economic systems.
We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex data sets. In particular, we consider multiplex networks made of four layers corresponding respectively to linear, non-linear, tail, and partial correlations among a set of financial time series. We const…
Model forecasts market structure from financial networks using machine learning.
problem Predicting market correlation structure from financial networks.
method Dynamic Asset Graph (DAG), Dynamic Minimal Spanning Tree (DMST), Dynamic Threshold Networks (DTN).
result Model improves market structure forecasting by up to 40% over benchmarks.
Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.
problem Lack of interpretability and trust in AI for finance.
method Supervised fine-tuning and reinforcement learning with a curriculum.
result Improved risk-adjusted returns and lower drawdowns compared to other models.
New method identifies extreme risk propagation in financial networks.
problem Understanding extreme risk in financial networks.
method Max-linear structural equation model, hard-thresholding, Hamming distance.
result Sparse DAG for extreme risk propagation estimated.
TransBoost improves financial inclusion by evaluating individual financial risk.
problem Evaluating individual financial risk for new users and companies with limited data.
method Combines tree-based models and kernel methods, with parallel tree structure and efficient weights updating.
result Outperforms other transfer learning algorithms in prediction accuracy and robustness.
AI threatens financial stability through misuse and stealth adoption.
problem Misuse and stealth adoption of AI in financial regulations.
method Analysis of AI's potential risks and criteria for AI suitability.
result AI will likely become widely used by stealth, affecting high-level financial functions.
This paper extends financial theory to measure learnable market structure under computational constraints.
problem Understanding learnable market structure under bounded computational capacity.
method Introduces financial epiplexity as a measure of learnable market structure, extending classical information theory.
result Proves that equal entropy does not imply equal epiplexity and derives thresholds for useful regimes.
Graph Neural Networks improve financial fraud detection.
problem Complex financial transactions pose challenges in fraud detection.
method Unified framework of GNN methodologies applied to financial fraud detection.
result GNNs excel at capturing complex relational patterns in financial networks.
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
problem Capturing regime-dependent volatility in stock markets.
method Developed a regime-switching framework using the Financial Chaos Index (FCIX) and elastic net regression.
result Identified three market regimes: low-chaos, intermediate-chaos, and high-chaos, each with distinct volatility characteristics.
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.
Quantum computing offers financial industry new optimization and risk management tools.
problem Traditional computing limits financial industry's problem-solving capabilities.
method Structured review of quantum computing platforms, algorithms, and use cases.
result Quantum computing can enhance financial industry applications like optimization and risk management.
New techniques identify shifts in financial market sectors.
problem Identifying shifts in financial market structure and composition.
method Developed new mathematical techniques to identify nonlinear shifts in market sectors.
result Identified meaningful sector-to-sector mappings and optimal portfolio styles.