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.
TitAnt detects online transaction fraud in milliseconds.
NetDP predicts loan defaults using network data, addressing cold-start issues.
Study of ants' movement rules on a 6D space, revealing distribution structures and singular trajectories.
Framework selects real estate redevelopment uses by integrating value, risk, complexity, and irreversibility.
Collaborative filtering, especially latent factor model, has been popularly used in personalized recommendation. Latent factor model aims to learn user and item latent factors from user-item historic behaviors. To apply it into real big data scenarios, efficiency becomes the first concern, including offline model train…
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
Ant colony optimization for clustering with improved K-means.
We propose the application of a high-speed maximum likelihood clustering algorithm to detect temporal financial market states, using correlation matrices estimated from intraday market microstructure features. We first determine the ex-ante intraday temporal cluster configurations to identify market states, and then st…
The paper identifies regions where investment strategies match expected performance.
Improved financial performance through better regime prediction.
A new test evaluates risk estimation accuracy using probability integral transform.
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the extensive amount of filings for which no a priori categorization of their content exists. For this purpose, we utilize an approach from data mini…
LAAT detects multiple low-density manifolds in noisy data.
Proving the existence of speculative financial bubbles even a posteriori has proven exceedingly difficult so anticipating a speculative bubble ex ante would at first seem an impossible task. Still as illustrated by the recent turmoil in financial markets initiated by the so called subprime crisis there is clearly an ur…
Italy and the Eurozone are heading in the year 2012 into a financial depression of unprecedented magnitude, with a forthcoming multitude of often contradictory public economic and financial stability emergency interventions whose ultimate endogenous and exogenous effects on public and private health spending and on the…
This paper investigates how realized and option implied volatilities are related to the future quantiles of commodity returns. Whereas realized volatility measures ex-post uncertainty, volatility implied by option prices reveals the market's expectation and is often used as an ex-ante measure of the investor sentiment.…
ANT improves TS diffusion models by automatically determining noise schedules.
We introduce a natural generalization of the forward-starting options, first discussed by M. Rubinstein. The main feature of the contract presented here is that the strike-determination time is not fixed ex-ante, but allowed to be random, usually related to the occurrence of some event, either of financial nature or no…
For distributed computing environment, we consider the empirical risk minimization problem and propose a distributed and communication-efficient Newton-type optimization method. At every iteration, each worker locally finds an Approximate NewTon (ANT) direction, which is sent to the main driver. The main driver, then, …
ANT learns sparse embeddings for large vocabularies efficiently.
New tests for conditional copulas based on decision trees.
Adaptive Nucleus Truncation Improves Long-Form Reasoning
The model of rational decision-making in most of economics and statistics is expected utility theory (EU) axiomatised by von Neumann and Morgenstern, Savage and others. This is less the case, however, in financial economics and mathematical finance, where investment decisions are commonly based on the methods of mean-v…
Model learns evolving network relationships over time.
Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of systemic risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. The current paper proposes a…
This study shows how social insects and machine learning methods share a common mathematical framework.
Study develops hybrid model to mitigate stablecoin liquidity risk.
New risk-sharing rules induced by capital allocation principles.
Math model helps bees decide between winter survival and raising young.
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neur…
New method separates model and non-model risks for more practical asset pricing.
In this paper we propose DeepSwarm, a novel neural architecture search (NAS) method based on Swarm Intelligence principles. At its core DeepSwarm uses Ant Colony Optimization (ACO) to generate ant population which uses the pheromone information to collectively search for the best neural architecture. Furthermore, by us…
The tick value is a crucial component of market design and is often considered the most suitable tool to mitigate the effects of high frequency trading. The goal of this paper is to demonstrate that the approach introduced in Dayri and Rosenbaum (2015) allows for an ex ante assessment of the consequences of a tick valu…
The paper predicts and explains the decay of stock anomaly performance over time.
A novel approach learns goal-conditioned policies for locomotion using batch RL.
This study improves audit sampling by using sequential procedures with statistical guarantees.
A fuzzy expert system selects stocks for BSE using AI techniques.
The purpose of this paper is to advance the understanding of the conditions that give rise to flash crash contagion, particularly with respect to overlapping asset portfolio crowding. To this end, we designed, implemented, and assessed a hybrid micro-macro agent-based model, where price impact arises endogenously throu…
Study uses chatbot to understand users' needs for ML model explanations.
AGFN improves causal discovery by integrating expert feedback and handling latent confounding.
This paper presents an analysis of the study variables such as gdp, employment levels, the level of R & D and technology that will serve as the basis for stochastic modeling of production possibilities frontier in the goodness of fractal dimensions Ex Ante and Ex Post a priori to determine the levels of causality immed…
Improved estimation of hedge fund tail risks using a novel model.
The paper examines how risk reduction and insurance choices interact under convex premium principles.
Paper tackles continual reinforcement learning challenges with diversity exploration and adversarial self-correction.
Paper compares solutions of Poisson equations on Riemannian manifolds with Robin boundary.
This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…
We theoretically and empirically study portfolio optimization under transaction costs and establish a link between turnover penalization and covariance shrinkage with the penalization governed by transaction costs. We show how the ex ante incorporation of transaction costs shifts optimal portfolios towards regularized …
Researchers visualize all surfaces from tesseract faces.