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 theory for nonsmooth systems helps optimize and control complex functions.
The paper analyzes MACD using operator theory.
In modern portfolio theory, the balancing of expected returns on investments against uncertainties in those returns is aided by the use of utility functions. The Kelly criterion offers another approach, rooted in information theory, that always implies logarithmic utility. The two approaches seem incompatible, too loos…
Since the pioneering work of Ghys, Langevin and Walczak among others, it has been known that several methods of dynamical systems theory can be adopted to study of foliations. Our aim in this paper is to investigate complexity of foliations, by generalising existence problem of time averages in dynamical systems theory…
New bounds for average graph distance using curvature and centrality.
We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …
Paper connects 3D gravity averages to 2D CFT correlators.
Paper finds optimal shapes for minimizing average lengths of billiard trajectories in specific polygons.
The study analyzes when Bayesian averaging over decision trees is reliable.
As a natural application of the {\it theory of geometric averaging} in Finsler geometry and generalized Finsler geometry, a new approach to investigate {\it generalized Finsler geometry}, based on a convex invariance of the average structures, is introduced.
Predicting absolute magnitude of fluctuations of price, even if their sign remains unknown, is important for risk analysis and for option prices. In the present work, we display our predictions about absolute magnitude of daily fluctuations of the Dow Jones Industrials Average (DJIA), utilizing the original theory of c…
New RL algorithms improve average-reward performance.
This paper analyzes the convergence of Federated Average under relaxed assumptions.
New findings suggest no ensemble averaging for certain black hole observables.
We study U(N|M) character expectation value with the supermatrix Chern-Simons theory, known as the ABJM matrix model, with emphasis on its connection to the knot invariant. This average just gives the half BPS circular Wilson loop expectation value in ABJM theory, which shall correspond to the unknot invariant. We deri…
In this article we present new results for the pricing of arithmetic Asian options within a Black-Scholes context. To derive these results we make extensive use of the local scale invariance that exists in the theory of contingent claim pricing. This allows us to derive, in a natural way, a simple PDE for the price of …
A new method, based on the original theory of conservation of sum of kinetic and potential energy defined for prices is proposed and applied on Dow Jones Industrials Average (DJIA). The general trends averaged over months or years gave a roughly conserved total energy, with three different potential energies, i.e. posi…
Paper introduces stability in model averaging and proposes a L2-penalty method.
Although stochastic gradient descent (SGD) method and its variants (e.g., stochastic momentum methods, AdaGrad) are the choice of algorithms for solving non-convex problems (especially deep learning), there still remain big gaps between the theory and the practice with many questions unresolved. For example, there is s…
Many problems in machine learning and game theory can be formulated as saddle-point problems, for which various first-order methods have been developed and proven efficient in practice. Under the general convex-concave assumption, most first-order methods only guarantee an ergodic convergence rate, that is, the uniform…
The paper improves Monte Carlo methods for optimization problems.
Theory and methods to mitigate omitted variable bias in causal machine learning.
We introduce a covariance matrix estimator that both takes into account the heteroskedasticity of financial returns (by using an exponentially weighted moving average) and reduces the effective dimensionality of the estimation (and hence measurement noise) via techniques borrowed from random matrix theory. We calculate…
Study classifies stock price data into stationary and non-stationary periods for mechanical trading.
New invariant for square-free integers derived from kei theory.
Averaged SGD optimizes a smoothed objective, leading to better generalization.
The paper tackles learning to control systems with unknown parameters using Brownian noise.
We derive a dimensionally-reduced limit theory for an -dimensional nonlinear elastic body that is slender along dimensions. The starting point is to view an elastic body as an -dimensional Riemannian manifold together with a not necessarily isometric -immersion in -dimensional Euclidean space. The…
We investigate the average-case complexity of decision problems for finitely generated groups, in particular the word and membership problems. Using our recent results on ``generic-case complexity'' we show that if a finitely generated group has the word problem solvable in subexponential time and has a subgroup of…
This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average…
In this thesis, we consider the suitability of using the charged cold fluid model in the description of ultra-relativistic beams. The method that we have used is the following. Firstly, the necessary notions of kinetic theory and differential geometry of second order differential equations are explained. Then an averag…
Consider a family of portfolio strategies with the aim of achieving the asymptotic growth rate of the best one. The idea behind Cover's universal portfolio is to build a wealth-weighted average which can be viewed as a buy-and-hold portfolio of portfolios. When an optimal portfolio exists, the wealth-weighted average c…
Proposes methods to recover labels from shuffled networks using graph averages.
RF models implicitly regularize kernel methods as feature count increases.
The paper develops a new theory to understand deep learning optimization.
New framework analyzes SGD dynamics in large samples and dimensions.
Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communicate short messages. Communication is often the bottleneck. In this paper, we study one-step and iterative weighted parameter averaging in s…
In a recent Nature paper, Gabaix et al. \cite{Gabaix03} presented a theory to explain the power law tail of price fluctuations. The main points of their theory are that volume fluctuations, which have a power law tail with exponent roughly -1.5, are modulated by the average market impact function, which describes the r…
CV outperforms mean-variance for stock returns, minimizing risk and maximizing growth.
In this work, we introduce the {\em average top-} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the largest individual losses over a training dataset. We show that the \atk loss is a natural generalization of the two widely used aggregate losses, namely the average loss a…
The R-function theory of Thomas is used to model neutron inelastic scattering and the fine, intermediate, and gross structure observed in the Dow Jones Industrial Average on a typical trading day.
Bayesian stacking improves model performance with varying model weights.
Attempts to accurately measure the monetary velocity or related properties of bitcoin used in transactions have often attempted to either directly apply definitions from traditional macroeconomic theory or to use specialized metrics relative to the properties of the Blockchain like bitcoin days destroyed. In this paper…
This paper studies robust estimation methods in high dimensions, comparing model-averaged and composite quantile estimators.
This paper develops a new theory for ensemble learning beyond variance reduction.
Study finds Calabi-Yau models' operator spectra match random matrix theory.
Study the averaging principle for non-autonomous slow-fast systems and apply it to financial local stochastic volatility models.
The paper shows Gaussian fluctuations in eigenvalue statistics of random hyperbolic surfaces.