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.
Paper analyzes CCT sensitivity in constrained power systems, offering insights into system stability and parameter changes.
Sequential processing biases asset allocation in artificial stock markets.
Study of stock market dynamics using statistical physics, showing critical temperature and boosting effects.
We study the optimal placement problem of a stock trader who wishes to clear his/her inventory by a predetermined time horizon t, by using a limit order or a market order. For a diffusive market, we characterize the optimal limit order placement policy and analyze its behavior under different market conditions. In part…
KineticSim: A lightweight, high-performance execution engine for real-time market simulators
CLEAR calibrates both aleatoric and epistemic uncertainties for better predictive intervals.
KineticSim accelerates financial market simulations 3406x over CPU.
We discuss a simple model based on the Minority Game which reproduces the main stylized facts of anomalous fluctuations in finance. We present the analytic solution of the model in the thermodynamic limit and show that stylized facts arise only close to a line of critical points with non-trivial properties. By a simple…
Method reconstructs financial networks from aggregate data, revealing critical link density.
A new model calculates optimal clearing payments in dynamic financial networks.
Optimal control solves multi-period liability clearing problems.
Single image dehazing is a critical stage in many modern-day autonomous vision applications. Early prior-based methods often involved a time-consuming minimization of a hand-crafted energy function. Recent learning-based approaches utilize the representational power of deep neural networks (DNNs) to learn the underlyin…
Recent innovations in Information and Communication Technologies (ICT) provide new opportunities and challenges for integration of distributed energy resources (DERs) into the energy supply system as active market players. By increasing integration of DERs, novel market platform should be designed for these new market …
Blockchain markets with paid-priority trading can lead to biased prices and reduced liquidity.
Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However, it remains less clear whether such "neural" policy gradient methods converge to globally optimal policies and whether they even converge at …
Paper finds efficient algorithms for computing fixed points in financial networks.
Study explores reinforcement learning in a complex game environment, analyzing rule inference and policy learning.
Deep neural networks predict CVCM track circuit failures early.
Deep learning solves complex PA mean field games with market-clearing conditions.
Study on deep neural networks for reward modeling with pairwise comparison data.
The call auction is a widely used trading mechanism, especially during the opening and closing periods of financial markets. In this paper, we study a standard call auction problem where orders are submitted according to Poisson processes, with random prices distributed according to a general distribution, and may be c…
In this paper we consider a class of weighted-volume preserving curvature flows acting on hypersurfaces that are trapped within two parallel hyperplanes and satisfy an orthogonal boundary condition. In the author's thesis the stability of cylinders under the flows was considered; it was found that they are stable provi…
We propose a model for the credit and liquidity risks faced by clearing members of Central Counterparty Clearing houses (CCPs). This model aims to capture the features of: gap risk; feedback between clearing member default, market volatility and margining requirements; the different risks faced by various types of mark…
Unified model for network risks, including bilateral and central clearing, with practical applications.
We propose a simple model of inter-bank borrowing and lending where the evolution of the log-monetary reserves of banks is described by a system of diffusion processes coupled through their drifts in such a way that stability of the system depends on the rate of inter-bank borrowing and lending. Systemic risk is ch…
The paper analyzes optimal timing for converting wealth into annuities in the presence of a mortality shock.
We introduce a dynamic model of the default waterfall of derivatives CCPs and propose a risk sensitive method for sizing the initial margin (IM), and the default fund (DF) and its allocation among clearing members. Using a Markovian structure model of joint credit migrations, our evaluation of DF takes into account the…
We consider a dynamic market model where buyers and sellers submit limit orders. If at a given moment in time, the buyer is unable to complete his entire order due to the shortage of sell orders at the required limit price, the unmatched part of the order is recorded in the order book. Subsequently these buy unmatched …
Paper introduces Cycles Protocol to integrate trade credit into market clearing.
AI-Trader benchmarks LLMs in live financial markets, revealing poor trading performance.
FCPCA fuzzy clusters high-dimensional time series data efficiently.
A new method for clearing liability networks using sheaves on directed hypergraphs.
We consider an auction market in which market makers fill the order book during a given time period while some other investors send market orders. We define the clearing price of the auction as the price maximizing the exchanged volume at the clearing time according to the supply and demand of each market participants.…
We quantify the sensitivity of the Eisenberg-Noe clearing vector to estimation errors in the bilateral liabilities of a financial system in a stylized setting. The interbank liabilities matrix is a crucial input to the computation of the clearing vector. However, in practice central bankers and regulators must often es…
Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic way of quantifying creative artifacts. In this work, we propose a regression-based learning framework which takes into account quantitative…
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to perform revenue optimization in auctions and markets with contextual information. T…
The paper examines clearing payments in financial networks to prevent cascaded defaults.
Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian m…
Study prenatal PM2.5 exposure and 4th grade reading scores, identifying critical windows of susceptibility.
Double descent found in DRL, improving generalization with model capacity.
GRASPEL learns large graphs from data efficiently.
Paper proposes a decentralized payment clearing system using blockchain and optimal bidding strategies.
Pari-mutuel markets are trading platforms through which the common market maker simultaneously clears multiple contingent claims markets. This market has several distinctive properties that began attracting the attention of the financial industry in the 2000s. For example, the platform aggregates liquidity from the ind…
In this study, we focus on the market clearing problem of Turkish day-ahead electricity market. We propose a mathematical model by extending the variety of bid types for different price regions. The commercial solvers may not find any feasible solution for the proposed problem in some instances within the given time li…
Novel method improves load estimation in power grids using anomaly and change point detection.
Study confirms financial bubbles' common patterns in isolated markets.
Develops ML tool for macroeconomic forecasting with clear interpretations.
This paper develops an XVA (costs) analysis of centrally cleared trading, parallel to the one that has been developed in the last years for bilateral transactions. We introduce a dynamic framework that incorporates the sequence of cash-flows involved in the waterfall of resources of a clearing house. The total cost of …