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.
Unified AI system for data quality control and governance in regulated environments.
DAISYnt evaluates synthetic data quality and privacy in regulated domains.
Not all types of supervision signals are created equal: Different types of feedback have different costs and effects on learning. We show how self-regulation strategies that decide when to ask for which kind of feedback from a teacher (or from oneself) can be cast as a learning-to-learn problem leading to improved cost…
A method uses Wasserstein clustering to simplify financial data analysis.
DP-FedTabDiff generates private synthetic tabular data using diffusion models and differential privacy.
Develops a framework for synthetic banking microdata evaluation.
Synthetic data improves financial models without real data.
Domain shift is unavoidable in real-world applications of object detection. For example, in self-driving cars, the target domain consists of unconstrained road environments which cannot all possibly be observed in training data. Similarly, in surveillance applications sufficiently representative training data may be la…
Model proposes how regulators should oversee complex algorithms in high-stakes applications.
Regulated curves on Banach manifolds with continuous projections and regulated derivatives are studied.
Appropriate traffic regulations, e.g. planned road closure, are important in congested events. Crowd simulators have been used to find appropriate regulations by simulating multiple scenarios with different regulations. However, this approach requires multiple simulation runs, which are time-consuming. In this paper, w…
We show that any objective risk measurement algorithm mandated by central banks for regulated financial entities will result in more risk being taken on by those financial entities than would otherwise be the case. Furthermore, the risks taken on by the regulated financial entities are far more systemically concentrate…
New mechanism designs regulate herding in financial markets.
Unified framework for intersectionally fair AI models using MIO.
In a market system, regulations are designed to prevent or rectify market failures that inhibit fair exchange, such as monopoly or transactions with hidden costs. Because regulations reduce profits to those possessing unfair advantage, these advantaged corporations (whether individuals, companies, or other collective o…
MiCA regulation led to a shift in stablecoin dominance.
Risk statistic is a critical factor not only for risk analysis but also for financial application. However, the traditional risk statistics may fail to describe the characteristics of regulator-based risk. In this paper, we consider the regulator-based risk statistics for portfolios. By further developing the propertie…
Federated Learning solves privacy and data distribution challenges in machine learning.
Synthetic tabular data synthesis models balance utility and risk.
Proposes a game-theoretic framework for ML trust regulation.
This paper studies a Value-at-Risk (VaR)-regulated optimal portfolio problem of the equity holders of a participating life insurance contract. In a setting with unhedgeable mortality risk and complete financial market, the optimal solution is given explicitly for contracts with mortality risk using a martingale approac…
Paper tackles reinforcement learning generalization through invariant policy optimization.
The FCA improved insider trading regulation after 2012, reducing abnormal returns.
A deterministic trading strategy by a representative investor on a single market asset, which generates complex and realistic returns with its first four moments similar to the empirical values of European stock indices, is used to simulate the effects of financial regulation that either pricks bubbles, props up crashe…
Proposes guidelines for developing medical AI products.
Study shows group structures are crucial for financial model explanations.
We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maximum likelihood estimation algorithms. We discuss models of this type and their pro…
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…
Modeling pollution from competing firms using mean-field games.
This paper tackles interpretability of LLMs in finance.
Develops new methods for isospectral orbifolds and regulator quotients.
Modern physics has demonstrated that matter behaves very differently as it approaches the speed of light. This paper explores the implications of modern physics to the operation and regulation of financial markets. Information cannot move faster than the speed of light. The geographic separation of market centers means…
This study examines how ChiNext IPOs' initial returns are influenced by regulation regime changes.
Regulated Bitcoin futures led to higher volatility and trading volume.
We show that the regulator, which is the difference between the homology torsion and the combinatorial Ray-Singer torsion, of fnite abelian coverings of a fixed complex has sub-exponential growth rate.
We investigate a randomization procedure undertaken in real option games which can serve as a basic model of regulation in a duopoly model of preemptive investment. We recall the rigorous framework of [M. Grasselli, V. Leclère and M. Ludkovsky, Priority Option: the value of being a leader, International Journal of Theo…
As regulators pay more attentions to losses rather than gains, we are able to derive a new class of risk statistics, named regulator-based risk statistics with scenario analysis in this paper. This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou et al. \ci…
We present a machine learning approach to the solution of chance constrained optimizations in the context of voltage regulation problems in power system operation. The novelty of our approach resides in approximating the feasible region of uncertainty with an ellipsoid. We formulate this problem using a learning model …
Study optimal liquidation strategies in lit and dark pools with and without regulation.
Trade finance history traced from medieval origins to modern markets.
Regulating causal effects through averaged constraints fails to enforce conditional independence.
DRL improves ESG financial portfolio management by regulating returns based on ESG scores.
The study enhances financial rule matching using NLP without datasets.
Mapping the economy to the some statistical physics models we get strong indications that, in contrary to the pure stock market, the stock market with derivatives could not self-regulate.
We show that some specific market risk measures implied by current international capital regulation (the Basel Accords and the Capital Adequacy Directive of the European Union) violate the obvious requirement of convexity in some regions in the space of portfolio weights.
Improved stochastic clocks for financial models without increasing trades.
Self-regulating annealing improves sampling from heavy-tailed datasets.
Introduces an artificial cyber lab to test and identify cyber resilience measures.