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.
Develops information geometry for Lévy processes in finance.
Researchers study the geometric properties of a specific type of stable processes.
The paper explores anticipative binary information in financial markets using Brownian motion and Poisson processes.
Zellner (1988) modeled statistical inference in terms of information processing and postulated the Information Conservation Principle (ICP) between the input and output of the information processing block, showing that this yielded Bayesian inference as the optimum information processing rule. Recently, Alemi (2019) re…
Meta learning with information theory and Gaussian processes.
In financial markets, the information that traders have about an asset is reflected in its price. The arrival of new information then leads to price changes. The `information-based framework' of Brody, Hughston and Macrina (BHM) isolates the emergence of information, and examines its role as a driver of price dynamics.…
The FSRM uses a multifractional process to capture price multifractality, revealing serial information for forecasting.
Levy processes, which have stationary independent increments, are ideal for modelling the various types of noise that can arise in communication channels. If a Levy process admits exponential moments, then there exists a parametric family of measure changes called Esscher transformations. If the parameter is replaced w…
New pricing model uses variance-gamma process for financial assets.
New measures generalize existing ones, linking information and risk.
Introduces relative information gain for improving Gaussian process regression rates.
Formalizes identifying information to answer key questions about machine learning from uncertain and novel observations.
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compa…
Augmented bridge matching preserves coupling information between distributions.
Kernel-based GPs model continuous processes with uncountable information.
Improved meta-learning for dynamics using additional structured knowledge.
We derive expressions for the predicitive information rate (PIR) for the class of autoregressive Gaussian processes AR(N), both in terms of the prediction coefficients and in terms of the power spectral density. The latter result suggests a duality between the PIR and the multi-information rate for processes with mutua…
Calculates local Granger causality for Gaussian and nonlinear systems.
Travel decisions tend to exhibit sensitivity to uncertainty and information processing constraints. These behavioural conditions can be characterized by a generative learning process. We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations. We outlin…
By employing the technique of enlargement of filtrations, we demonstrate how to incorporate information about the future trend of the stochastic interest rate process into a financial model. By modeling the interest rate as an affine diffusion process, we obtain explicit formulas for the additional expected logarithmic…
A new framework for asset pricing based on modelling the information available to market participants is presented. Each asset is characterised by the cash flows it generates. Each cash flow is expressed as a function of one or more independent random variables called market factors or "X-factors". Each X-factor is ass…
Batch Active Learning uses derivative information for Gaussian Process regression.
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artifici…
The paper analyzes how market prices respond to information processing and non-linear dynamics.
Study shows cognitive load impacts financial market efficiency, especially for less sophisticated investors.
A new hierarchy quantifies agency in systems based on information processing.
The information-based asset-pricing framework of Brody, Hughston and Macrina (BHM) is extended to include a wider class of models for market information. In the BHM framework, each asset is associated with a collection of random cash flows. The price of the asset is the sum of the discounted conditional expectations of…
Bayesian optimization sped up with scalable Gaussian processes.
Learning using privileged information is an attractive problem setting that helps many learning scenarios in the real world. A state-of-the-art method of Gaussian process classification (GPC) with privileged information is GPC+, which incorporates privileged information into a noise term of the likelihood. A drawback o…
Paper develops physics-informed, boundary-constrained Gaussian process for fluid flow field reconstruction.
The paper proves sampling methods using discrete-time processes and information theory.
Physics-informed GP regression solves eigenvalue problems by identifying non-trivial eigenspaces.
We propose a model for the credit markets in which the random default times of bonds are assumed to be given as functions of one or more independent "market factors". Market participants are assumed to have partial information about each of the market factors, represented by the values of a set of market factor informa…
Bayesian neural networks improve with summary information and Dirichlet process.
ChatGPT can summarize corporate disclosures more concisely and effectively, improving stock market reactions.
Deep learning, computational neuroscience, and cognitive science have overlapping goals related to understanding intelligence such that perception and behaviour can be simulated in computational systems. In neuroimaging, machine learning methods have been used to test computational models of sensory information process…
New model for disability insurance reserving handles delays in claim information.
A dynamical system can be regarded as an information processing apparatus that encodes input streams from the external environment to its state and processes them through state transitions. The information processing capacity (IPC) is an excellent tool that comprehensively evaluates these processed inputs, providing de…
We consider the mean-variance hedging problem under partial Information. The underlying asset price process follows a continuous semimartingale and strategies have to be constructed when only part of the information in the market is available. We show that the initial mean variance hedging problem is equivalent to a ne…
HR-calculus enables adaptive processing of quaternion signals.
In this paper we introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous- time information processes that describe the flow of information about market factors in a monetary economy. The nominal pricing kernel is at any given time assumed to be given by …
We present a multi-task learning formulation for Deep Gaussian processes (DGPs), through non-linear mixtures of latent processes. The latent space is composed of private processes that capture within-task information and shared processes that capture across-task dependencies. We propose two different methods for segmen…
A new method reduces energy consumption in machine learning by using multiple, less costly data sources.
A new model DKMPP integrates covariates and uses an integration-free method for spatio-temporal point processes.
The issue of giving an explicit description of the flow of information concerning the time of bankruptcy of a company (or a state) arriving on the market is tackled by defining a bridge process starting from zero and conditioned to be equal to zero when the default occurs. This enables to catch some empirical facts on …
A new framework for asset price dynamics is introduced in which the concept of noisy information about future cash flows is used to derive the price processes. In this framework an asset is defined by its cash-flow structure. Each cash flow is modelled by a random variable that can be expressed as a function of a colle…
Paper reinterprets majorizing measure theorem in terms of coding theory.
Active learning selects inputs for GPSSM to learn latent states.