News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.
problem Drivers of the excess bond premium (EBP).
method News attention to 180 topics captures up to 80% of EBP variation and forecasts macroeconomic movements.
result News attention to financial intermediaries and crises drives up the EBP and predicts macroeconomic downturns.
SPAC data shows premium investors get better terms, non-premium get quid pro quo deals.
problem Agency problems and informational frictions in securities issuance.
method Analysis of SPAC data to identify premium and non-premium investors.
result Non-premium investors engage in quid pro quo relationships with issuers and intermediaries.
A new method for ILO with transition model disparity using an intermediary policy.
problem Learning tasks from expert observations with different transition dynamics.
method Training an intermediary policy to match the state transitions of the expert dataset.
result Our method outperforms existing ILO approaches with transition model mismatch.
Integrates inductive biases into VAEs using intermediary latent variables.
problem Ineffective mechanisms for incorporating inductive biases into VAEs.
method InteL-VAEs use an intermediary latent space to control encoding, with a parametric function to enforce desired properties.
result InteL-VAEs lead to better generative models and representations.
Study quantifies contributions of market participants to volatility using Hawkes processes.
problem Quantifying contributions of different market participants to volatility.
method Leveraged Hawkes point processes to analyze the branching properties of market participant behaviors.
result High-frequency traders are more endogenously driven than other types of agents.
DPFact preserves privacy while collaboratively factorizing EHR tensors.
problem Privacy-preserving tensor factorization for EHRs.
method Differential privacy and collaborative learning.
result DPFact achieves higher accuracy and efficiency under privacy constraints.
We compute the convex hull in C2 of an arbitrary finite subgroup of C∗2. The combinatorics are dictated by continued fractions in a natural way. This reproves a theorem of Smilansky, with a slightly stronger intermediary step.
The main purpose of this paper is to formalize the modelling process, analysis and mathematical definition of corruption when entering into a contract between principal agent and producers. The formulation of the problem and the definition of concepts for the general case are considered. For definiteness, all calculati…
Reduces data leakage in distributed deep learning models.
problem Prevents reconstruction of sensitive raw data patterns during client communications.
method Reduces distance correlation between raw data and learned representations.
result Resilient to reconstruction attacks while maintaining model accuracy.
We classify nonsingular holomorphic foliations of dimension and codimension one on certain Hopf manifolds. More general, we prove that all nonsingular codimension one distributions on intermediary or generic Hopf manifolds are integrable and has holomorphic integral first. Also, we prove some results about singular hol…
Business cycles affect startup valuations, both directly and indirectly.
problem How do business cycles impact startup valuations?
method Structural Equation Model approach using a dataset of 1,089 venture capital investments.
result Business cycles impact startup valuations both directly and indirectly.
The study tests a functional-form restriction on risk exposure dynamics using margin debt data.
problem Understanding risk exposure dynamics under capital constraints and slack.
method Testing a regime-conditional functional-form restriction on aggregate risk-exposure dynamics implied by VaR-constrained intermediary models.
result The contraction and growth of exposures under capital constraints and slack are observed and tested.
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…
Research proposes a decentralized invoice discounting system using Kelly criterion.
problem Persistent funding gap for SMEs and inefficiencies in traditional factoring.
method Automated Market Maker (AMM) with Kelly criterion for premium calculation.
result Resilient decentralized system with optimal profit distribution policies.
Ergodicity proven for certain hyperbolic 3-manifold diffeomorphisms.
problem Proving ergodicity for conservative partially hyperbolic diffeomorphisms in hyperbolic 3-manifolds.
method Analyzing accessibility and deducing ergodicity from conservative C1+ partially hyperbolic diffeomorphisms. result Ergodicity of conservative C1+ partially hyperbolic diffeomorphisms in hyperbolic 3-manifolds. An assistant learns to mediate decisions between humans and experts, balancing risk and learning.
problem Learning to mediate decisions between imperfect humans and expert knowledge.
method Formalizes online decision mediation, proposes a policy to balance immediate loss and future generalization.
result Consistent gains over benchmarks in decision-making performance.
Study insurance pricing under correlation ambiguity without increasing prices or reducing utility.
problem Understanding the dependence structure between insurance and financial risks.
method Dynamic equilibrium analysis of insurance pricing with worst-case beliefs.
result Correlation ambiguity does not necessarily increase insurance prices or reduce insurers' utility.
Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a…
This paper assesses risks in DeFi investments.
problem Risks in decentralized finance investments.
method Overview of DeFi components and risk quantification methodology.
result Proposes an allocation methodology to integrate and quantify risks.
New machine learning model identifies key drivers of market troughs.
problem Misrepresentation of market trough drivers by simpler models.
method Flexible DML average partial effect causal machine learning framework.
result Volatility of options-implied risk appetite and market liquidity are key drivers.
Suppliers (including companies and individual prosumers) may wish to protect their private information when selling items they have in stock. A market is envisaged where private information can be protected through the use of differential privacy and option contracts, while privacy-aware suppliers deliver their stock a…
Study finds significant premium for low-beta stocks in firm-level idiosyncratic return distributions.
problem Understanding the role of common idiosyncratic quantile factors in asset pricing.
method Quantile factor analysis to extract common idiosyncratic quantile factors with asymmetric pricing effects.
result Significant premium for innovations to the lower-tail factor: high-beta stocks outperform low-beta stocks by around 7-8% per year.
Trade networks for maize, rice, soy, and wheat are more vulnerable to shocks.
problem Increased complexity in international crop trade networks makes them more susceptible to cascades of demand failures.
method Analyzed FAO data from 176 countries over 21 years to construct higher-order trade dependency networks.
result Trade networks are more prone to failure cascades caused by exogenous shocks.
This study reviews decentralized prediction markets, identifying key design variants and open problems.
problem Designing and implementing decentralized prediction markets with desirable properties.
method Modular workflow comprising eight stages: infrastructure, market topic, share structure, pricing, market initialization, trading, resolution, settlement, and archiving. Analysis of design variants and trade-offs.
result Identification of open problems for researchers in the field of decentralized prediction markets.
Q-groupoids and Q-algebroids are, respectively, supergroupoids and superalgebroids that are equipped with compatible homological vector fields. These new objects are closely related to the double structures of Mackenzie; in particular, we show that Q-groupoids are intermediary objects between Mackenzie's LA-groupoids a…
Framework learns image dynamics between time steps using latent variables.
problem Challenges in capturing evolving image patterns and temporal information.
method Estimates intermediary image stages using a physical latent variable model.
result Demonstrates robustness and effectiveness in geoscientific imagery.
Model for hedging price and quantity risks in electricity markets.
problem Hedging risks for energy retailers in a regulated electricity market.
method Closed-form solution for optimal portfolio using financial instruments based on price and weather indexes.
result Closed-form solution for mean-var model in discrete setting without distributional assumptions.
Flatland is a simple 2D environment for reinforcement learning.
problem Testing and prototyping reinforcement learning agents.
method Simple, lightweight, customizable environment with physical properties.
result Baseline reinforcement learning agents can solve a navigation task in Flatland.
Proposes a decentralized insurance protocol for DeFi.
problem Over-insurance and inefficiencies in DeFi collateral.
method Smart contract-based economic model without external dependencies.
result Solves over-insurance and capital inefficiencies.
PLS-Lasso integrates dimension reduction into regression for financial index tracking.
problem Dimension reduction and regression are traditionally treated separately in multivariate data analysis.
method PLS-Lasso integrates dimension reduction directly into the regression process, presenting two formulations: PLS-Lasso-v1 and PLS-Lasso-v2.
result PLS-Lasso-v1 and PLS-Lasso-v2 outperform Lasso in financial index tracking.
Characterizes no Butterfly arbitrage in SVI model parameters.
problem No Butterfly arbitrage in SVI implied total variance formula.
method Characterization using intermediary condition from Fukasawa (2012) and rescaling of SVI parameters.
result Simple range conditions on SVI parameters ensure no Butterfly arbitrage.
FedGAN trains GANs across distributed data sources with reduced communication.
problem Training GANs across non-independent data sources with privacy and communication constraints.
method FedGAN uses local generators and discriminators synced via an intermediary, proving convergence under standard assumptions.
result FedGAN converges and performs similarly to general distributed GANs with reduced communication complexity.
We propose a new method for assessing agents' influence in financial network structures, which takes into consideration the intensity of interactions. A distinctive feature of this approach is that it considers not only direct interactions of agents of the first level and indirect interactions of the second level, but …
Optimizes AIS hyperparameters for efficient marginal likelihood estimation.
problem Limited computation budget affects AIS performance.
method Flexible intermediary distributions defined by residual density, parameter sharing, and fix linear schedule.
result Optimized-Path AIS reduces sampling iterations and improves performance.
Framework integrates financial and annual report data for better corporate credit ratings.
problem Lack of insights from non-financial data in credit rating models.
method Uses FinBERT to extract features from annual reports and combines them with financial data.
result Improves credit rating accuracy by 8-12%.
Closed Riemannian 4 or 5-manifolds contain branched immersed closed minimal surfaces.
problem Existence of classical minimal surfaces in 4 and 5-manifolds
method Harmonic replacement method
result Proves the existence of branched immersed closed minimal surfaces in 4 and 5-manifolds
Study proposes optimal risk-aware interest rates for crypto lending protocols.
problem Determining optimal interest rates for decentralized lending protocols to maximize profit and minimize risk.
method Agent-based model, Riccati-type ODEs for linear behaviors, Monte-Carlo estimator and deep learning for nonlinear behaviors.
result Calibrated model shows superior risk-adjusted performance compared to industry-standard interest rate models.
Paper proposes evaluation methods for climate change illustrations.
problem Lack of metrics to compare realism in conditional generative models.
method Adapted and assessed several existing metrics, including FID.
result FID with Inception-V3 embeddings correlates best with human realism.
This study examines how banks and securities markets coevolved in 19th century Belgium.
problem The role of banks and markets in the evolution of financial architecture.
method Case study of Belgium in the 1830s, focusing on the development of secondary securities markets and banks' activities.
result Cyclical market conditions influenced banks' activities and vice versa, suggesting non-neutral coevolution.
We consider a general discrete-time financial market with proportional transaction costs as in [Kabanov, Stricker and Rásonyi Finance and Stochastics 7 (2003) 403--411] and [Schachermayer Math. Finance 14 (2004) 19--48]. In addition to the usual investment in financial assets, we assume that the agents can invest part …
Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.
problem Analyzing efficiency and profitability of Chinese banks over time.
method Used Data envelopment analysis (Super-SBM-UND-VRS based DEA) model considering non-performing loans as undesired output.
result State-owned banks and Rural/City Commercial Banks have better profitability super-efficiency than Joint-stock Banks.
Designing AI market for content creation
problem Balancing technological progress and individual incentives for content creation
method Dynamic Stackelberg game model
result Inducing greater reliance on AI-assisted creation
The study examines Kernel Ridge Regression error rates across noiseless and noisy conditions.
problem Characterizing Kernel Ridge Regression error rates in different noise levels.
method Unified analysis of Kernel Ridge Regression under various noise and regularization conditions.
result A crossover from noiseless to noisy error rates is observed as sample complexity increases.
The paper analyzes sterling bills of exchange during the first globalization, revealing their global financial role.
problem Understanding the global financial role of sterling bills of exchange during the first globalization.
method Descriptive statistics and network analysis of a unique data set of 23,493 bills re-discounted by the Bank of England in 1906.
result Sterling bills of exchange had a truly global dimension and were crucial for overcoming information asymmetries.
Model analyzes trading frictions in cap-and-trade markets, showing how they interact to affect market effectiveness.
problem Analyzing how trading frictions impact cap-and-trade market effectiveness.
method Developed a dynamic stochastic model with multiple trading frictions, characterized access choices in closed form, and quantified using EU ETS data.
result Trading frictions interact to amplify or dampen market responses, and their combined effect is non-additive.
New method learns sparse distributions by thresholding samples, improving performance and efficiency.
problem Sparse coding optimization in high-dimensional problems is computationally expensive and inefficient.
method Proposes a new variational sparse coding approach that learns sparse distributions by thresholding samples.
result Shows superior performance, statistical efficiency, and gradient estimation compared to other sparse distributions.
The existence of asymmetric information has always been a major concern for financial institutions. Financial intermediaries such as commercial banks need to study the quality of potential borrowers in order to make their decision on corporate loans. Classical methods model the default probability by financial ratios u…
A new Monte Carlo sampling method derived from reverse diffusion.
problem Sampling from complex distributions, especially multi-modal ones.
method Transforming score matching into mean estimation; estimating means of regularized posterior distributions.
result rdMC can approximate sampling with any desired accuracy and is significantly faster than MCMC for complex distributions.