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.
Study examines financial structure's impact on non-financial firms' growth in Kenya.
Develops a new method for financial term structure modeling.
Develops a framework to analyze financial structures.
CoCos can increase financial fragility in certain network structures.
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data. Market structure is measured by quantifying the co-movement of asset prices returns, while social structure is meas…
In this paper we show similarities between turbulence and financial systems. Motivated by similarities between the two systems, we construct a multiscale model for hierarchical financial structures that exhibits a constant cascade of wealth from large financial entities to small financial entities. According to our mod…
We propose a dynamic model of dependence structure between financial institutions within a financial system and we construct measures for dependence and financial instability. Employing Markov structures of joint credit migrations, our model allows for contagious simultaneous jumps in credit ratings and provides flexib…
Study examines how mergers and acquisitions affect Indian banks' financial performance and capital structure.
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…
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…
Combining neural networks and multiscale decomposition for financial market analysis.
Study reveals structural differences in financial networks near and far from crises using balance theory.
LLMs improve financial analysis by processing large data sets.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
Generative model for financial time series using structured noise and signature learning.
Spectral denoising recovers meaningful network structure from noisy financial correlations.
This study examines the evolving causal structure of equity risk factors.
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.
We develop a structural default model for interconnected financial institutions in a probabilistic framework. For all possible network structures we characterize the joint default distribution of the system using Bayesian network methodologies. Particular emphasis is given to the treatment and consequences of cyclic fi…
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.
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…
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…
Liberalization of electricity markets has increasingly created the need for understanding the volatility and correlation structure between electricity and financial markets. This work reveals the existence of structural changes in correlation patterns among these two markets and links the changes to both fundamentals a…
NAS for financial time series forecasts using chain-structured architectures.
Review of correlation-based financial networks and entropy measures.
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…
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…
A network-based approach identifies financial factors from asset interactions, explaining market dynamics.
Model forecasts market structure from financial networks using machine learning.
New method identifies extreme risk propagation in financial networks.
Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.
TransBoost improves financial inclusion by evaluating individual financial risk.
AI threatens financial stability through misuse and stealth adoption.
Graph Neural Networks improve financial fraud detection.
This paper extends financial theory to measure learnable market structure under computational constraints.
The Financial Chaos Index models stock market volatility across three regimes based on mutual price fluctuations.
Quantum computing offers financial industry new optimization and risk management tools.
Quality-designed consumer products are easy to recognize. Wouldn't it be great if the quality of financial products became just as apparent? This paper is addressed to financial practitioners. It provides an informal introduction to Quantitative Structuring -- a technology of manufacturing quality financial products (i…
This study identifies financial risk paths in digital-transformed enterprises.
New techniques identify shifts in financial market sectors.
Algorithm improves SLR efficiency in financial narratives.
The theory of multilayer networks is in its early stages, and its development provides vital methods for understanding complex systems. Multilayer networks, in their multiplex form, have been introduced within the last three years to analysing the structure of financial systems, and existing studies have modelled and e…
We develop a topology data analysis-based method to detect early signs for critical transitions in financial data. From the time-series of multiple stock prices, we build time-dependent correlation networks, which exhibit topological structures. We compute the persistent homology associated to these structures in order…
Study financial market graphs with Laplacian constraints.
Hyperbolic geometry reveals financial network structure and systemic importance.