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 finds financial constraints explain zero-leverage firms.
Study on Leverage Ratio in European banks during financial crises.
Modeling bank leverage dynamics to understand systemic risk in financial markets.
Model shows how relaxed leverage can lead to asset price bubbles.
Common asset holdings are widely believed to have been the primary vector of contagion in the recent financial crisis. We develop a network approach to the amplification of financial contagion due to the combination of overlapping portfolios and leverage, and we show how it can be understood in terms of a generalized b…
The article presents a translation of some widespread financial terminology into the language of decision theory. For instance, financial leverage can be regarded as an object of choice or a decision. We show how the optics of decision theory allows perceiving the recently introduced metrics of see-through-leverage, wh…
LLMs prefer Bitcoin under crisis frames, affecting financial decisions.
This paper provides a framework for modeling the financial system with multiple illiquid assets when liquidation of illiquid assets is caused by failure to meet a leverage requirement. This extends the network model of Cifuentes, Shin & Ferrucci (2005) which incorporates a single asset with fire sales and capital adequ…
We use bank-level balance sheet data from 2005 to 2010 to study interactions within the banking system of five emerging countries: Argentina, Brazil, Mexico, South Africa, and Taiwan. For each country we construct a financial network based on the leverage ratio dependence between each pair of banks, and find results th…
With the daily and minutely data of the German DAX and Chinese indices, we investigate how the return-volatility correlation originates in financial dynamics. Based on a retarded volatility model, we may eliminate or generate the return-volatility correlation of the time series, while other characteristics, such as the…
We model leverage as stochastic but independent of return shocks and of volatility and perform likelihood-based inference via the recently developed iterated filtering algorithm using S&P500 data, contributing new evidence to the still slim empirical support for random leverage variation.
This work develops an agent-based model for the study of how the leverage through the use of repurchase agreements can function as a mechanism for the propagation and amplification of financial shocks in a financial system. Based on the analysis of financial intermediaries in the repo and interbank lending markets duri…
Leverage is strongly related to liquidity in a market and lack of liquidity is considered a cause and/or consequence of the recent financial crisis. A repurchase agreement is a financial instrument where a security is sold simultaneously with an agreement to buy it back at a later date. Repurchase agreements (repos) ma…
GraphShield uses dynamic graph learning to detect and visualize financial risks.
The SV-GARCH-EVT model improves risk assessment in financial markets.
The paper introduces a new volatility model for state heterogeneous financial markets using high-frequency data.
Study examines strategies to reduce volatility in leveraged ETF markets.
Survey of LLMs in finance tasks, including adoption and performance.
Background: For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not…
Excessive leverage, i.e. the abuse of debt financing, is considered one of the primary factors in the default of financial institutions. Systemic risk results from correlations between individual default probabilities that cannot be considered independent. Based on the structural framework by Merton (1974), we discuss …
This paper characterizes the equilibrium in a continuous time financial market populated by heterogeneous agents who differ in their rate of relative risk aversion and face convex portfolio constraints. The model is studied in an application to margin constraints and found to match real world observations about financi…
BreakGPT predicts asset price surges using LLMs.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
We investigate the spatial and temporal structures of four financial markets in Greater China. In particular, we uncover different characteristics of the four markets by analyzing the sector and subsector structures which are detected through the random matrix theory. Meanwhile, we observe that the Taiwan and Hongkong …
Study shows how financial report sentiment impacts bank profitability.
This research shows that under certain mathematical conditions, a threshold autoregressive model (TAR) can represent the leverage effect based on its conditional variance function. Furthermore, the analytical expressions for the third and fourth moment of the TAR model are obtained when it is weakly stationary.
The practice of valuation by marking-to-market with current trading prices is seriously flawed. Under leverage the problem is particularly dramatic: due to the concave form of market impact, selling always initially causes the expected leverage to increase. There is a critical leverage above which it is impossible to e…
HedgeNet uses neural networks to reduce hedging errors for financial options.
This study uses AI to analyze financial market coverage from YouTube videos.
Deep learning solves and estimates complex financial models.
Novel framework uses causality for financial forecasting.
We introduce two types of ordinal pattern dependence between time series. Positive (resp. negative) ordinal pattern dependence can be seen as a non-paramatric and in particular non-linear counterpart to positive (resp. negative) correlation. We show in an explorative study that both types of this dependence show up in …
FinEAS models financial sentiment using BERT embeddings.
This paper explores deep learning for financial trading, integrating sentiment analysis.
Unified LLM-agent with RL improves financial trading performance.
System detects financial misinformation and generates clear explanations.
We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of this review is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econo…
In addition to constraining bilateral exposures of financial institutions, there are essentially two options for future financial regulation of systemic risk (SR): First, financial regulation could attempt to reduce the financial fragility of global or domestic systemically important financial institutions (G-SIBs or D…
Methodology measures financial impacts using existing credit loss infrastructure.
Modeling bank leverage dynamics using dynamical systems and neural networks.
Survey of AI in finance covering models, strategies, and knowledge systems.
German FinBERT improves financial text analysis performance.
This paper uses LLMs to improve equity stock ratings by ingesting diverse financial and news data.
I sketch a program for a microeconomic theory of the main component of the business cycle as a recurring disequilibrium, driven by incompleteness of the financial market and by information asymmetries between borrowers and lenders. This proposal seeks to incorporate five distinct but connected processes that have been …
Study analyzes deep learning models for financial sentiment in earnings calls.
Study uses LLMs to improve financial forecasting by integrating textual and numerical data.
M2VN forecasts financial volatility by fusing time series data with news embeddings.
TGN outperforms static GNNs in detecting financial fraud.