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.
GARIM theory explains how conscious manipulation of internal representations enhances goal-directed behavior.
Algorithm improves transfer learning by inferring successor maps.
Develops a unified framework for valuing insurance products with guarantees.
Market-maker optimizes quotes based on strategic market-takers' behavior.
Agents need world models to generalize multi-step tasks.
Introduction: Machine learning provides fundamental tools both for scientific research and for the development of technologies with significant impact on society. It provides methods that facilitate the discovery of regularities in data and that give predictions without explicit knowledge of the rules governing a syste…
GPRNs accurately model stellar activity affecting RV measurements of exoplanets.
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate…
BFPM improves machine learning accuracy by considering object types and memberships flexibly.
Enhances flexibility in data reweighting with optimal transport and maximum entropy principles.
A Python package for GLHMM, a flexible HMM framework.
Infinite neural networks lack key flexibility, finite ones learn better.
Deep learning's anomalous generalization explained by standard frameworks.
Study reveals model misspecification significantly impacts neural SBI algorithms.
Enhances reward specification in RL with a novel language-based approach.
Deep RL solves complex economic models with heterogeneous agents.
Proposes causal modeling for intersectional fairness in rankings.
Study examines extreme and erratic cryptocurrency behaviour during COVID-19.
A new tail-shape index based on Value at Risk and Expected Shortfall.
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour policy models: how precisely …
The main aim of this work is to incorporate selected findings from behavioural finance into a Heterogeneous Agent Model using the Brock and Hommes (1998) framework. Behavioural patterns are injected into an asset pricing framework through the so-called `Break Point Date', which allows us to examine their direct impact.…
New simulation model predicts financial market dynamics with high accuracy.
The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. …
Automated model tracks mouse behavior in home cages.
Impact of chosen behavioural factors on imprecision of present value is discussed here. The formal model of behavioural present value is offered as a result of this discussion. Behavioural present value is described here by fuzzy set. These considerations were illustrated by means of extensive numerical case study. Fin…
FAB combines flows with AIS to approximate complex distributions.
New bounds on predicting agent behavior from behavior alone.
We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and population levels. Our model class is a generalisation of nonlinear mixed-effects (NLME) dynamical systems, the statistical workhorse for ma…
We develop an algebraic framework for the description and analysis of financial behaviours, that is, behaviours that consist of transferring certain amounts of money at planned times. To a large extent, analysis of financial products amounts to analysis of such behaviours. We formalize the cumulative interest compliant…
LLMs can simulate human investment attitudes based on personality traits.
Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This paper uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behav…
The financial market entropy is modeled using open quantum systems.
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao 2010 and Shao & Lii 2011, which is a sum of sinusoids where and the frequency, amplitud…
FTIP uses normalizing flows to improve posterior inference in function space.
Financial advisors use KYC info but not client behaviours to guide investments.
Generative models explain machine learning predictions with counterfactual instances.
Studies report that firms do not invest in cost-effective green technologies. While economic barriers can explain parts of the gap, behavioural aspects cause further under-valuation. This could be partly due to systematic deviations of decision-making agents' perceptions from normative benchmarks, and partly due to the…
We propose a probabilistic framework to directly insert prior knowledge in reinforcement learning (RL) algorithms by defining the behaviour policy as a Bayesian posterior distribution. Such a posterior combines task specific information with prior knowledge, thus allowing to achieve transfer learning across tasks. The …
Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically relies on manually generated demonstrations or specially deployed sensors and has not generally been able to leverage the copious demonstrat…
Accurate statistical models of neural spike responses can characterize the information carried by neural populations. But the limited samples of spike counts during recording usually result in model overfitting. Besides, current models assume spike counts to be Poisson-distributed, which ignores the fact that many neur…
This paper analyses the behaviour of volatility for several international stock market indexes, namely the SP 500 (USA), the Nikkei (Japan), the PSI 20 (Portugal), the CAC 40 (France), the DAX 30 (Germany), the FTSE 100 (UK), the IBEX 35 (Spain) and the MIB 30 (Italy), in the context of non-stationarity. Our empirical …
NDPs learn to sample from complex function distributions using neural networks and diffusion models.
Mixtures of multivariate contaminated shifted asymmetric Laplace distributions are developed for handling asymmetric clusters in the presence of outliers (also referred to as bad points herein). In addition to the parameters of the related non-contaminated mixture, for each (asymmetric) cluster, our model has one param…
Neural Bayes methods simplify fitting complex bivariate extremal models.
Neural network predicts nonlinear safety behavior based on personality traits.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
The financial market is nonpredictable, as according to the Bachelier, the mathematical expectation of the speculator is zero. Nevertheless, we observe in the price fluctuations the two distinct scales, short and long time. Behaviour of a market in long terms, such as year intervals, is different from that in short ter…
Diffusion models mimic human actions in sequential tasks.