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.
New composite indicators reveal hidden relationships between indicators.
For long time the measurement of innovation has been in the forefront of policy makers' and researchers' agenda worldwide. Therefore, there is an ongoing debate about which indicators should be used to measure innovation. Recent approaches have favoured the use of composite innovation indicators. However, there is no c…
Develops methods for causal inference in compositional data using instrumental variables.
Several formulas for computing coarse indices of twisted Dirac type operators are introduced. One type of such formulas is by composition product in -theory. The other type is by module multiplications in -theory, which also yields an index theoretic interpretation of the duality between Roe algebra and stable Hi…
The study visualizes Spanish fish and meat processing companies using financial, environmental, and social ratios.
In this paper the correlation between education, research and macroeconomic strength of countries at a global scale is analyzed on the basis of statistical data published by the UNIDO and OECD. It uses sets of composite indicators describing the economical performance and competitiveness as well as those relevant for h…
Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different ob…
We consider in this work a system of two stochastic differential equations named the perturbed compositional gradient flow. By introducing a separation of fast and slow scales of the two equations, we show that the limit of the slow motion is given by an averaged ordinary differential equation. We then demonstrate that…
Study uses Hawkes processes to analyze stock market contagion in China.
Sublinearly structured DNNs achieve feature learning consistency for compositional functions.
New methods for predicting compositional data using conformal prediction.
Generative model improves scene graph prediction for rare compositions.
The existence of forbidden patterns, i.e., certain missing sequences in a given time series, is a recently proposed instrument of potential application in the study of time series. Forbidden patterns are related to the permutation entropy, which has the basic properties of classic chaos indicators, thus allowing to sep…
Financial market created for wellbeing indices to mitigate socioeconomic risks.
Adding noise controls capacity of function compositions.
We give useful criteria for S_1 singularities in the Mond classification table, and cuspidal S_k singularities. As applications, we give a simple proof of a result given by Mond and a characterization of cuspidal S_k singularities for the composition of a cuspidal edge and a fold map indicated by Arnol'd for the case k…
Neural networks learn spectral representations for group composition.
Formula for spectrum linking braid and bridge indices.
By adopting Multifractal detrended fluctuation (MF-DFA) analysis methods, the multifractal nature is revealed in the high-frequency data of two typical indexes, the Shanghai Stock Exchange Composite 180 Index (SH180) and the Shenzhen Stock Exchange Composite Index (SZCI). The characteristics of the corresponding multif…
This paper proposes a new model for SPX and VIX derivatives markets.
The intrinsic entropy model accurately estimates stock market volatility.
BOIS optimizes complex systems by leveraging structural knowledge.
Estimates returns for dollar cost averaging using geometric Brownian motion.
The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However…
This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of th…
Maximizes probability of completing investment schedules with optimal portfolio weights.
We present a method of rank-optimal weighting which can be used to explore the best possible position of a subject in a ranking based on a composite indicator by means of a mathematical optimization problem. As an example, we explore the dataset of the OECD Better Life Index and compute for each country a weight vector…
We give an explicit handy (and cocycle-free) description of the groupoid of weak maps between two crossed-modules in terms of certain digrams of groups which we we call a {\em butterflies}. We define composition of butterflies and this way find a bicategory that is naturally biequivalent to the 2-category of pointed ho…
An upper bound of the superbridge index of the connected sum of two knots is given in terms of the braid index of the summands. Using this upper bound and minimal polygonal presentations, we give an upper bound in terms of the superbridge index and the bridge index of the summands when they are torus knots. In contrast…
Signals are geometric submanifolds with specific properties.
We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this impr…
We investigate the behavior of the Shanghai Stock Exchange Composite (SSEC) index for the period from 1990:12 to 2007:06 using an unconstrained two-regime threshold autoregressive (TAR) model with an unit root developed by Caner and Hansen. The method allows us to simultaneously consider non-stationarity and nonlineari…
New AI stock indices classify firms' AI engagement using 10-K filings.
Study reveals a universal formula for knotting in random equilateral polygons.
Machine learning detects regime shifts in online game-experiments with high accuracy.
Paper analyzes convergence of PAM method for low-rank factorization models.
A new privacy accountant for Gaussian differential privacy measures individual privacy losses.
We find a novel correlation structure in the residual noise of stock market returns that is remarkably linked to the composition and stability of the top few significant factors driving the returns, and moreover indicates that the noise band is composed of multiple subbands that do not fully mix. Our findings allow us …
Paper addresses uncertainty in model generalization under regime shifts.
This work is an analytical and numerical study of the composition of several fractals into one and of the relation between the composite dimension and the dimensions of the component fractals. In the case of composition of standard IFS with segments of equal size, the composite dimension can be expressed as a function …
SNNs can represent complex functions efficiently.
New geometric approach for analyzing compositional data like gut microbiomes.
The evolution of the probability distributions of Japan and US major market indices, NIKKEI 225 and NASDAQ composite index, and and currency exchange rates is described by means of the Fokker-Planck equation (FPE). In order to distinguish and quantify the deterministic and random influences on these…
Study on deep neural networks using branching processes and Mehler's formula.
Study excess capacity in neural networks using Rademacher complexity.
MatGAN uses GAN to efficiently generate new inorganic materials.
Low-rank inducing unitarily invariant norms have been introduced to convexify problems with low-rank/sparsity constraint. They are the convex envelope of a unitary invariant norm and the indicator function of an upper bounding rank constraint. The most well-known member of this family is the so-called nuclear norm. To …
We establish conditions for compositional generalization in machine learning.