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.
The paper models stock returns using -Gaussians and negative binomials.
Study finds inventory inaccuracies are linked to store activity and product perishability.
What return should you expect when you take on a given amount of risk? How should that return depend upon other people's behavior? What principles can you use to answer these questions? In this paper, we approach these topics by exploring the consequences of two simple hypotheses about risk. The first is a common-sense…
Study reveals how illiquidity network signals Chinese stock market crashes.
Detailed study of multifractal characteristics of the financial time series of asset values and of its returns is performed using a collection of the high frequency Deutsche Aktienindex data. The tail index (), the Renyi exponents based on the box counting algorithm for the graph () and the generalized Hurst ex…
Study examines how BRICS nations' economies respond to COVID-19.
The Hype Index measures media attention to equities using NLP.
Paper tests for time-varying entropy in stock prices, finding periods of inefficiency.
We propose an indicator to measure the degree to which a particular news article is novel, as well as an indicator to measure the degree to which a particular news item attracts attention from investors. The novelty measure is obtained by comparing the extent to which a particular news article is similar to earlier new…
The statistical properties of the bid-ask spread of a frequently traded Chinese stock listed on the Shenzhen Stock Exchange are investigated using the limit-order book data. Three different definitions of spread are considered based on the time right before transactions, the time whenever the highest buying price or th…
Counting tripods on a flat torus using lattice point counting.
Flow Matching for count data improves sample quality and efficiency.
There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined …
New algorithms handle unpredictable actions in sequential learning.
New theorem counts curves on orbifolds.
A new method, Count-MORL, improves offline reinforcement learning by using state-action frequency.
Proposes a method to reconcile count time series forecasts.
Study geodesic paths on flat surfaces, comparing length and singularity counts.
Graph neural networks struggle with counting certain substructures in graphs.
The conventional formal tool to detect effects of the financial persistence is in terms of the Hurst exponent. A typical corresponding result is that its value comes out close to 0.5, as characteristic for geometric Brownian motion, with at most small departures from this value in either direction depending on the mark…
Counts arcs in surfaces, proving convergence of geodesic currents.
Deviance-style normalization for sparse, jointly overdispersed count matrices
The paper proposes count echo state networks for forecasting graduate student enrollments.
Counted essential surfaces in a knot's exterior, finding a unique pattern.
Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the…
Proposes a robust EM algorithm for analyzing incomplete panel count data.
Calegari, Marques, and Neves count minimal surfaces in hyperbolic manifolds.
Better neural arithmetic logic units improve cell counting model generalization.
Counts minimal tori in Riemannian manifolds with 6 or more dimensions.
In recent scene recognition research images or large image regions are often represented as disorganized "bags" of features which can then be analyzed using models originally developed to capture co-variation of word counts in text. However, image feature counts are likely to be constrained in different ways than word …
Estimates point counts in Teichmüller space for mapping class groups.
Proves quaternionic analog of Cartan's theorem and counts arithmetic chains.
Counting is a fundamental task in biomedical imaging and count is an important biomarker in a number of conditions. Estimating the uncertainty in the measurement is thus vital to making definite, informed conclusions. In this paper, we first compare a range of existing methods to perform counting in medical imaging and…
Count data take on non-negative integer values and are challenging to properly analyze using standard linear-Gaussian methods such as linear regression and principal components analysis. Generalized linear models enable direct modeling of counts in a regression context using distributions such as the Poisson and negati…
Study shows how to count and equidistribute cusped Hitchin representations with entropy gaps.
Enhances psyquandle counting invariants using cocycles.
We prove formulae for the countings by orbit of square-tiled surfaces of genus two with one singularity. These formulae were conjectured by Hubert & Lelièvre. We show that these countings admit quasimodular forms as generating functions.
Graphlets are defined as k-node connected induced subgraph patterns. For an undirected graph, 3-node graphlets include close triangle and open triangle. When k = 4, there are six types of graphlets, e.g., tailed-triangle and clique are two possible 4-node graphlets. The number of each graphlet, called graphlet count, i…
Enhances knot counting using mosaic diagrams.
The involutory birack counting invariant is an integer-valued invariant of unoriented tangles defined by counting homomorphisms from the fundamental involutory birack of the tangle to a finite involutory birack over a set of framings modulo the birack rank of the labeling birack. In this first of an anticipated series …
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvantages: collapsed Gibbs sampling is unbiased but is also inefficient for large count values and requires averaging over many samples to reduce …
We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their context such that the counts can be aggregated as a stream. We demonstrate performance and scalability of the resulting approach on random quer…
A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic count matrix, whose columns are sequentially observed count vectors. The model builds a novel Markov chain that sends the latent gamma random variables at time as the shape parameters of those at time , which are linked …
We consider involutory virtual biracks with good involutions, also known as symmetric involutory virtual biracks. Any good involution on an involutory virtual birack defines an enhancement of the counting invariant. We provide examples demonstrating that the enhancement is stronger than the unenhanced counting invarian…
We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncert…
The paper counts mapping classes by Nielsen-Thurston type, finding growth rates for different subsets.
Novel Bayesian method for high-dimensional count data prediction.
New tribrackets defined to count link homotopy invariants.