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.
IEBN normalizes noise by enhancing instance-specific information, improving deep learning performance.
Modeling pollution from competing firms using mean-field games.
Self-regulating annealing improves sampling from heavy-tailed datasets.
Study on variance of policy gradient in simple RL environments.
This work establishes safe reinforcement learning for LQR with nonlinear baselines.
We build an agent-based model for the order book with three types of market participants: informed trader, noise trader and competitive market makers. Using a Glosten-Milgrom like approach, we are able to deduce the whole limit order book (bid-ask spread and volume available at each price) from the interactions between…
The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control. An adaptive policy needs to address both the estimation of unknown dynamics parameters (exploration), as well as the regulation of the underlying system (exploitation). To this end, optimism-based…
Noise in linear networks minimizes sharpness and leads to shrinkage-thresholding.
Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable …
Optimal control strategy uses random noise to adaptively control systems with unknown parameters.
Synthetic tabular data synthesis models balance utility and risk.
Model proposes how regulators should oversee complex algorithms in high-stakes applications.
Strong inductive biases prevent harmless interpolation in overparameterized models.
SymNoise improves language model fine-tuning by 6.7% over NEFTune, using symmetric noise.
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.
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…
New robust control method for uncertain systems using bootstrapped noise.
The linear quadratic regulator (LQR) problem has reemerged as an important theoretical benchmark for reinforcement learning-based control of complex dynamical systems with continuous state and action spaces. In contrast with nearly all recent work in this area, we consider multiplicative noise models, which are increas…
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…
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…
Enhanced financial forecasting with supervised autoencoders for S&P 500 and cryptocurrencies.
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.
Biophysical models explain deep learning in gene regulation.
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…
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.
According to recent findings [1,2], empirical covariance matrices deduced from financial return series contain such a high amount of noise that, apart from a few large eigenvalues and the corresponding eigenvectors, their structure can essentially be regarded as random. In [1], e.g., it is reported that about 94% of th…
Contingent Convertible bonds (CoCos) are debt instruments that convert into equity or are written down in times of distress. Existing pricing models assume conversion triggers based on market prices and on the assumption that markets can always observe all relevant firm information. But all Cocos issued so far have tri…
Regulating causal effects through averaged constraints fails to enforce conditional independence.
Paper explains how tree ensembles improve predictions by smoothing and regulating smoothness.
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.
Paper improves SVaR estimation for stress testing under macro scenarios using a hybrid GPR-HS framework.