NeuralBeta uses deep learning to estimate beta, outperforming traditional methods.
problem Limitations of traditional beta estimation methods in capturing dynamic beta behavior.
method Neural networks with a new output layer for interpretability.
result NeuralBeta outperforms benchmark methods in dynamic beta estimation.
Study examines time-varying betas and their volatility in bank interest income and expense margins.
problem Understanding the variability of bank betas and their impact on net interest margins.
method Used state-space methods to estimate time-varying betas and conditional volatility.
result Substantial variation in interest income and expense betas, leading to varying net interest margin coefficients.
New estimator reveals intraday betas mainly driven by correlations.
problem Intraday fluctuations in market betas due to time-varying volatility.
method Proposes a novel subsampled quadrant estimator for high-frequency financial data.
result Intraday variation in betas primarily driven by intraday variation in correlations.
Beta is a widely used quantity in investment analysis. We review the common interpretations that are applied to beta in finance and show that the standard method of estimation - least squares regression - is inconsistent with these interpretations. We present the case for an alternative beta estimator which is more app…
We describe the underlying probabilistic interpretation of alpha and beta divergences. We first show that beta divergences are inherently tied to Tweedie distributions, a particular type of exponential family, known as exponential dispersion models. Starting from the variance function of a Tweedie model, we outline how…
Proposes logistic-beta process for modeling dependent probabilities with beta marginals.
problem Limited work on flexible and computationally convenient stochastic process extensions for dependent random probabilities.
method Introduces logistic-beta process with logistic transformation and beta marginals, capable of modeling dependence in discrete and continuous domains.
result Logistic-beta processes enable effective posterior inference and design of computationally tractable dependent Bayesian nonparametric models.
We present a reactive beta model that includes the leverage effect to allow hedge fund managers to target a near-zero beta for market neutral strategies. For this purpose, we derive a metric of correlation with leverage effect to identify the relation between the market beta and volatility changes. An empirical test ba…
Efficiently models categorical data with low to medium class overlap, improving accuracy over standard distributions.
problem Poor parameter estimates and accuracy in multinomial and Dirichlet multinomial distributions when assumptions are violated.
method Introduces Beta-Liouville multinomial distribution and efficient estimation methods.
result Beta-Liouville multinomial outperforms standard distributions on two out of four datasets.
Bayesian Beta regression for proportions in high dimensions with theoretical guarantees.
problem Modeling bounded continuous responses in high-dimensional settings with theoretical guarantees.
method Proposes a Bayesian approach using a tempered posterior with Horseshoe prior for shrinkage and variable selection.
result Demonstrates improved estimation accuracy and model interpretability in high-dimensional scenarios.
New estimates show spectral gap stability in RCD spaces, close to Beta distribution.
problem Stability of spectral gap bounds in metric-measure spaces.
method Combines L1-functional inequality and Stein's method. result Sharp quantitative estimate for spectral gap stability.
Paper develops new spot regression estimators using candlesticks for asset pricing.
problem Estimation of spot betas in asset pricing and risk management.
method Develops a new estimation and inference framework for spot regressions using high-frequency candlesticks.
result The proposed candlestick-based estimators reduce estimation risk and achieve higher power in hypothesis testing.
Unified and noise-reduced data valuation framework for machine learning.
problem Quantifying the contribution of individual data points in machine learning.
method Beta Shapley, a generalization of Data Shapley, relaxes the efficiency axiom.
result Beta Shapley outperforms state-of-the-art data valuation methods on various ML tasks.
Paper tests if beta coefficients in AMF model are consistent over time.
problem Testing time-invariance of beta coefficients in AMF model.
method Used AMF model with GIBS algorithm to identify relevant factors, compared to FF5 model.
result AMF model shows time-invariant beta coefficients for most periods, FF5 does not.
Proposes a new factor to improve BAB strategies by recognizing bad-beta assets.
problem Investors often misprice assets based on beta, ignoring bad-beta.
method Double-sorting on beta and bad-beta to create a new factor.
result The Betting Against Bad Beta factor improves BAB strategies.
Extended study improves covariance matrix estimation for portfolio managers.
problem Limited sample sizes and poor performance of PCA estimator in high-dimensional returns.
method Developed a more general shrinkage framework targeting further information.
result Improves the PCA estimator of beta by shrinking it toward a target.
New f-Betas for portfolio optimization using f-divergence risk measures.
problem Optimizing portfolio performance under varying market conditions.
method Derive f-Betas and Hellinger-Betas, using f-divergence risk measures.
result Demonstrated new Beta metrics provide better performance under stress.
This paper examines the validity of the Capital Asset Pricing Model (CAPM) on the Ugandan stock market using monthly stock returns from 10 of the 11 companies listed on the Uganda Stock Exchange (USE), for the period 1st March 2007 to 10th November 2009. Due to the absence of readily available Uganda Stock Exchange(USE…
Paper proposes an efficient algorithm for nonnegative binary matrix factorization.
problem Decomposing binary data using matrix factorization.
method Majorization-minimization algorithm with Beta prior for improved performance.
result Proposed algorithm offers excellent trade-off between performance, complexity, and interpretability.
An investigation is presented of how a comprehensive choice of four most important measures of concordance (namely Spearman's rho, Kendall's tau, Spearman's footrule, and Gini's gamma) relate to the fifth one, i.e., the Blomqvist's beta. In order to work out these results we present a novel method of estimating the val…
This article analyzes the problem of estimating the time until an event occurs, also known as survival modeling. We observe through substantial experiments on large real-world datasets and use-cases that populations are largely heterogeneous. Sub-populations have different mean and variance in their survival rates requ…
Machine learning improves beta forecasts, enhancing equity valuation and portfolio performance.
problem Improving beta forecasts for better equity valuation and portfolio performance.
method Using machine learning on a large cross-section of US stocks with various firm characteristics.
result Machine learning improves out-of-sample performance of asymmetric beta measures.
New invariants help solve existence of weighted cscK metrics.
problem Existence of weighted cscK metrics in K-stability.
method Introduced weighted analytic delta invariant and beta invariant.
result Sufficient condition for existence of weighted cscK metrics.
The study assesses carbon risk in investment portfolios and proposes new management strategies.
problem The impact of carbon risk on stock pricing and portfolio construction.
method Developed a BMG risk factor and estimated time-varying carbon beta using a multi-factor model.
result Carbon risk can be incorporated into portfolio construction to reduce unrewarded financial risks.
The article explains the probabilistic method of default probability estimation by Pluto and Tasche.
problem Estimating default probabilities for portfolios with low default rates.
method Detailed derivation and explanation of the Pluto-Tasche method, including assumptions and inequalities.
result Clarification of borrower independence, conditional independence, and interaction between probability distributions.
A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multi-scoop" generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infi…
The beta-Bernoulli process provides a Bayesian nonparametric prior for models involving collections of binary-valued features. A draw from the beta process yields an infinite collection of probabilities in the unit interval, and a draw from the Bernoulli process turns these into binary-valued features. Recent work has …
Beta diffusion generates bounded data using multiplicative transitions.
problem Generating data within specific ranges.
method Integrates demasking and denoising with scaled and shifted beta distributions.
result KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs.
We propose a Monte Carlo simulation method to generate stress tests by VaR scenarios under Solvency II for dependent risks on the basis of observed data. This is of particular interest for the construction of Internal Models and requirements on evaluation processes formulated in the Commission Delegated Regulation. The…
We show that the stick-breaking construction of the beta process due to Paisley, et al. (2010) can be obtained from the characterization of the beta process as a Poisson process. Specifically, we show that the mean measure of the underlying Poisson process is equal to that of the beta process. We use this underlying re…
In this paper, the geometric meaning of (alpha,beta)-norms is made clear. On this basis, we introduce a new class of Finsler metrics called general (alpha,beta)-metrics, which are defined by a Riemannian metric and an 1-form. These metrics not only generalize original (alpha,beta)-metrics naturally, but also include so…
Study beta function for convex billiard maps, linking spectral invariants.
problem Understanding spectral invariants of convex billiard maps.
method Birkhoff normal form via constructive generating functions, explicit beta function formula.
result Linked spectral invariants to beta function for convex billiard maps.
The paper analyzes LASSO penalization for high-dimensional Beta regression models.
problem Theoretical analysis of LASSO in high-dimensional Beta regression.
method Non-convexity handling through a neighborhood framework, debiasing for confidence intervals, proximal gradient algorithm.
result Non-asymptotic bound on ℓ1-error of stationary points. Beta-SOD detects and corrects noisy object re-identification using cosine similarity and Beta mixtures.
problem Noisy object re-identification in image datasets.
method Reframed Re-ID as a similarity task, using Siamese networks and Beta mixture models.
result Superior performance in noisy conditions compared to state-of-the-art methods.
This is a short description of graphic lambda calculus, with special emphasis on a duality suggested by the two different appearances of knot diagrams, in lambda calculus and emergent algebra sectors of the graphic lambda calculus respectively. This duality leads to the introduction of the dual of the graphic beta move…
Study explores geometric structure and prior for beta-logistic distribution.
problem Understanding the geometric structure and prior distributions of the beta-logistic distribution.
method Exploring dual geometric structure and uncovering α-parallel prior. result The beta-logistic distribution admits an α-parallel prior for any real number α. Study uses CSIE to estimate portfolio volatility relative to market.
problem Estimating relative volatility risk of stock portfolios.
method Cross-sectional intrinsic entropy (CSIE) model to estimate cross-sectional volatility.
result Discover sets of symbols that outperform market indices in terms of return with similar or lower risk.
Study efficient algorithms for one-shot federated conformal prediction.
problem Valid prediction sets in one-shot federated learning.
method Quantile-of-quantiles family of estimators and split conformal prediction.
result No significant loss in coverage and length compared to centralized setting.
The paper proves optimizability implies inequalities for sampling.
problem Optimizing functions via Gradient Flow and sampling from Gibbs measures.
method Gradient Flow and Lyapunov potentials to establish inequalities.
result Optimizability via Gradient Flow implies Poincaré and Log-Sobolev Inequalities.
Unified framework for scale-invariant representation learning using MAPCA.
problem Learning invariant representations in data.
method Metric-Aware Principal Component Analysis (MAPCA) based on generalized eigenproblem.
result MAPCA provides a unified geometric language for various self-supervised learning objectives.
This paper generalizes beta divergence beyond its classical form associated with power variance functions of Tweedie models. Generalized form is represented by a compact definite integral as a function of variance function of the exponential dispersion model. This compact integral form simplifies derivations of many pr…
The Brylinski beta function is extended for coaxial layers on submanifolds.
problem Extending the Brylinski beta function to coaxial layers on submanifolds.
method Analytic continuation and computation of residues for the function.
result The Brylinski beta function has an analytic continuation with simple poles.
Smart beta, also known as strategic beta or factor investing, is the idea of selecting an investment portfolio in a simple rule-based manner that systematically captures market inefficiencies, thereby enhancing risk-adjusted returns above capitalization-weighted benchmarks. We explore the idea of applying a smart strat…
A beta function for double layers is defined and analyzed.
problem Defining and analyzing a beta function for double layers.
method Holomorphic function definition and analytic continuation.
result Residues of the beta function are integrals of invariants.
We derive and approximate the conjugate prior of Dirichlet and beta distributions.
problem Intractability of conjugate prior for Dirichlet and beta distributions.
method Derive conjugate prior, define closed-form approximation, and provide algorithm.
result Closed-form approximation enables fully tractable Bayesian treatment.
This work improves distribution recovery from sparse data using Random Forest implicit regularization.
problem Distribution recovery from limited statistics.
method Closed-form estimator for scaled beta distributions, using composite quantile and moment matching.
result Improved classification accuracy through closed-form distribution recovery and implicit regularization.
We consider a shrinking flow of smooth, closed, uniformly convex hypersurfaces in (n+1)-dimensional Euclidean space with speed fu^{alpha}{sigma}_n^{beta}, where u is the support function of the hypersurface, alpha, beta are two constants, and beta>0, sigma_n is the n-th symmetric polynomial of the principle curvature r…
A new beta-VAE based regression model accelerates oilfield optimization studies.
problem Computational expense of full-physics reservoir simulations.
method beta-VAE for interpretable latent space representation, probabilistic dense layers for uncertainty quantification.
result Interpretable latent representation and quantified uncertainty for optimization decisions.
This paper studies the Fisher-Rao geometry on the parameter space of beta distributions. We derive the geodesic equations and the sectional curvature, and prove that it is negative. This leads to uniqueness for the Riemannian centroid in that space. We use this Riemannian structure to study canonical moments, an intrin…