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.
This paper examines SVB's failure and its impact on bank stocks.
Model shows how banks' hidden-to-maturity accounting can mask run risk and lead to financial instability.
Stablecoin liquidity was affected by the SVB collapse, with USDC's transparency leading to market reactions.
Cryptos remained resilient after SVB's collapse, contrary to expectations.
AXI assesses bank funding costs transparently, improving loan pricing and reducing financial risk.
Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.
Groups of firms often achieve a competitive advantage through the formation of geo-industrial clusters. Although many exemplary clusters, such as Hollywood or Silicon Valley, have been frequently studied, systematic approaches to identify and analyze the hierarchical structure of the geo-industrial clusters at the glob…
Looped transformers outperform standard transformers in complex reasoning tasks due to a specific loss landscape geometry.
We present novel empirical observations regarding how stochastic gradient descent (SGD) navigates the loss landscape of over-parametrized deep neural networks (DNNs). These observations expose the qualitatively different roles of learning rate and batch-size in DNN optimization and generalization. Specifically we study…
Neural networks provide a rich class of high-dimensional, non-convex optimization problems. Despite their non-convexity, gradient-descent methods often successfully optimize these models. This has motivated a recent spur in research attempting to characterize properties of their loss surface that may explain such succe…
This paper explores loss landscapes of sparse neural networks, finding unique characteristics compared to dense networks.
This paper shows that every sublevel set of the loss function of a class of deep over-parameterized neural nets with piecewise linear activation functions is connected and unbounded. This implies that the loss has no bad local valleys and all of its global minima are connected within a unique and potentially very large…
We take a closer look at the life and legacy of Micheal Milken. We discuss why Michael Milken, also know as the Junk Bond King, was not just any other King or run-of-the-mill Junk Dealer, but "The Junk Dealer". We find parallels between the three parts to any magic act and what Micheal Milken did, showing that his acco…
Despite the non-convex nature of their loss functions, deep neural networks are known to generalize well when optimized with stochastic gradient descent (SGD). Recent work conjectures that SGD with proper configuration is able to find wide and flat local minima, which have been proposed to be associated with good gener…
LoRA-Curve connects independent LoRA optima through continuous low-loss valleys, improving Bayesian model averaging.
A new pruning method reduces neural network computation without retraining.
A D-Wave quantum annealer (QA) having a 2048 qubit lattice, with no missing qubits and couplings, allowed embedding of a complete graph of a Restricted Boltzmann Machine (RBM). A handwritten digit OptDigits data set having 8x7 pixels of visible units was used to train the RBM using a classical Contrastive Divergence. E…
A new Kolmogorov-Arnold network improves function approximation and optimization.
A common difficulty in applications of machine learning is the lack of any general principle for guiding the choices of key parameters of the underlying neural network. Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear…
We identify a class of over-parameterized deep neural networks with standard activation functions and cross-entropy loss which provably have no bad local valley, in the sense that from any point in parameter space there exists a continuous path on which the cross-entropy loss is non-increasing and gets arbitrarily clos…
Proposes a method to partition univariate data into unimodal subsets.
New framework reveals thermodynamic principles for LLM training.
WSD schedule improves model training efficiency by adapting learning rates dynamically.
Synthetic data can be used to ask more questions and accelerate discovery with provable validity guarantees.
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
An asset network systemic risk (ANWSER) model is presented to investigate the impact of how shadow banks are intermingled in a financial system on the severity of financial contagion. Particularly, the focus of this study is the impact of the following three representative topologies of an interbank loan network betwee…
Quantization-aware training can recover accuracy lost by post-training quantization.
The study compares profitability of conventional and Islamic banks in Bangladesh.
Study on electronic banking satisfaction in Nigeria.
This paper proposes a new optimization algorithm called Entropy-SGD for training deep neural networks that is motivated by the local geometry of the energy landscape. Local extrema with low generalization error have a large proportion of almost-zero eigenvalues in the Hessian with very few positive or negative eigenval…
Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.
The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit…
Solves local minima problems on smooth manifolds.
We report a study of a stylized banking cascade model investigating systemic risk caused by counter party failure using liabilities and assets to define banks' balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to d…
We consider the problem of governing systemic risk in an assets-liabilities dynamical model of banking system. In the model considered each bank is represented by its assets and its liabilities.The capital reserves of a bank are the difference between assets and liabilities of the bank. A bank is solvent when its capit…
This study uses high-frequency data to identify early warning signals for bank crises.
Analysis finds no evidence of banks managing deposit run risk prior to 2023 Regional Banking Crisis.
Study examines factors influencing lending to SMEs by Kenyan banks.
Bangladesh's banking sector improved through financial reforms, but challenges remain.
Study evaluates profitability of Islamic banks in Bangladesh using ROA, ROE, and ROD.
A modern version of Monetary Circuit Theory with a particular emphasis on stochastic underpinning mechanisms is developed. It is explained how money is created by the banking system as a whole and by individual banks. The role of central banks as system stabilizers and liquidity providers is elucidated. It is shown how…
Unbalanced data arises in many learning tasks such as clustering of multi-class data, hierarchical divisive clustering and semisupervised learning. Graph-based approaches are popular tools for these problems. Graph construction is an important aspect of graph-based learning. We show that graph-based algorithms can fail…
Modeling financial contagion through bank networks, revealing solvency correlations.
We analyze the influence and interactions of 60 largest world banks for 195 world countries using the reduced Google matrix algorithm for the English Wikipedia network with 5 416 537 articles. While the top asset rank positions are taken by the banks of China, with China Industrial and Commercial Bank of China at the f…
In the wake of the still ongoing global financial crisis, bank interdependencies have come into focus in trying to assess linkages among banks and systemic risk. To date, such analysis has largely been based on numerical data. By contrast, this study attempts to gain further insight into bank interconnections by tappin…
The paper discusses fairness in bank stress tests, comparing various methods to address institutional differences.
Oil prices affect Russian banks' stability, with negative impacts from decreases.
Based on an empirical analysis of the network structure of the Austrian inter-bank market, we study the flow of funds through the banking network following exogenous shocks to the system. These shocks are implemented by stochastic changes in variables like interest rates, exchange rates, etc. We demonstrate that the sy…