The paper discusses building ETF risk models using a multilevel classification taxonomy.
problem Building accurate risk models for ETFs.
method First, build a multilevel classification taxonomy for ETFs. Then, use this taxonomy to define risk factors and build risk models.
result The approach can accurately classify and model ETF risks.
Sensitivity analysis for individualized effects in OTRs with binary risk factors.
problem Addressing omitted confounding in individualized effects of OTRs.
method Simulation-based sensitivity analysis to simulate unmeasured confounders.
result Benchmarking the strength of omitted confounding for binary risk factors.
We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested "Russian-doll" embedding: the factor covariance matrix itself is modeled v…
In this article, we consider a 2 factors-model for pricing defaultable bond with discrete default intensity and barrier where the 2 factors are stochastic risk free short rate process and firm value process. We assume that the default event occurs in an expected manner when the firm value reaches a given default barrie…
Robo-advisors estimate clients' risk aversion using interactive questionnaires.
problem Estimating risk aversion of non-expert clients using adaptive questionnaires.
method Model risk aversion with cost functions and spectral risk measures. Use inverse reinforcement learning to design questions maximizing distinguishing power.
result Designing questions by maximizing distinguishing power achieves satisfactory accuracy in learning risk aversion with fewer than 50 questions.
New method turns optimization algorithms into uniformly stable learning algorithms for non-Euclidean norms.
problem Non-Euclidean norms in binary classification problems.
method Black-box reduction method using uniformly convex regularizers.
result Achieves optimal statistical risk bounds on excess risk for non-Euclidean norms.
New method improves LLM judge accuracy by accounting for dependencies in aggregated binary labels.
problem Classical label aggregation methods fail to account for dependencies among LLM judges, leading to miscalibrated predictions.
method Dependence-aware models based on Ising graphical models and latent factors.
result The proposed method outperforms classical methods on real-world datasets, reducing excess risk.
TRP uses tree-based approach for market-neutral portfolios.
problem Creating non-binary, market-neutral portfolios with signed signals.
method Tree-based portfolio construction with minimum-spanning-tree and sector-anchored variants.
result TRP outperforms HRP in preserving signal direction and managing exposures.
We have developed a strategy for the analysis of newly available binary data to improve outcome predictions based on existing data (binary or non-binary). Our strategy involves two modeling approaches for the newly available data, one combining binary covariate selection via LASSO with logistic regression and one based…
Most high-dimensional estimation and prediction methods propose to minimize a cost function (empirical risk) that is written as a sum of losses associated to each data point. In this paper we focus on the case of non-convex losses, which is practically important but still poorly understood. Classical empirical process …
Matrix factorization is a key tool in data analysis; its applications include recommender systems, correlation analysis, signal processing, among others. Binary matrices are a particular case which has received significant attention for over thirty years, especially within the field of data mining. Dictionary learning …
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.
In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by at…
Federated learning approach for binary matrix factorization.
problem Efficiently factorizing binary data distributed across stakeholders while maintaining privacy.
method Proximal optimization for federated learning of relaxed binary matrix factorization.
result Federated algorithm outperforms state-of-the-art methods in quality and efficacy.
This paper analyzes neural network classifiers' performance in binary classification.
problem Performance of neural network classifiers in binary classification problems.
method Plug-in classifiers based on neural networks, considering a more general function class and surrogate loss.
result Dimension-free, uniform rate of convergence for the excess risk of neural networks, showing minimax optimality.
Paper bounds convergence rate of adversarial surrogate risk.
problem Vulnerability of binary classification models to adversarial attacks.
method Characterizes conditions for adversarial consistency and provides surrogate risk bounds.
result Surrogate risk bounds quantify the rate of convergence of adversarial classification risk.
Paper proposes algorithms for BMF using integer programming.
problem Approximating binary input matrix as product of two smaller binary factors.
method Alternating optimization strategy using integer programming to solve subproblems and combine solutions.
result Proposed algorithms outperform state of the art on medium-scale problems.
We give an explicit algorithm and source code for computing optimal weights for combining a large number N of alphas. This algorithm does not cost O(N^3) or even O(N^2) operations but is much cheaper, in fact, the number of required operations scales linearly with N. We discuss how in the absence of binary or quasi-bin…
Reverse annealing boosts quantum matrix factorization performance.
problem Improving quantum matrix factorization performance.
method Combining forward and reverse annealing for nonnegative/binary matrix factorization.
result Combination of forward and reverse annealing significantly improves performance.
Neural networks approximate and estimate binary classifiers with polynomial input dependence.
problem Approximating and estimating binary classification functions with neural networks in high dimensions.
method ReLU neural networks, empirical risk minimization, Barron class.
result Approximation and estimation rates are independent of input dimension, overcoming curse of dimensionality.
Approach for modeling EHR data with rare features, improving prediction and interpretation.
problem Challenges in modeling rare binary features in EHR data.
method Tree-guided feature selection and logic aggregation for large-scale regression.
result Improved prediction and model interpretation of suicide risk in EHR data.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
Introduces factor risk measures to assess risk relative to multiple factors.
problem Measuring risk relative to multiple factors.
method Introduces a double-argument mapping as a risk measure to assess risk relative to a vector of factors.
result Characterizes various types of factor risk measures including distortion, quantile, linear, and coherent measures.
Aggregated hold-out (Agghoo) is a method which averages learning rules selected by hold-out (that is, cross-validation with a single split). We provide the first theoretical guarantees on Agghoo, ensuring that it can be used safely: Agghoo performs at worst like the hold-out when the risk is convex. The same holds true…
We present a scalable Bayesian model for low-rank factorization of massive tensors with binary observations. The proposed model has the following key properties: (1) in contrast to the models based on the logistic or probit likelihood, using a zero-truncated Poisson likelihood for binary data allows our model to scale …
A Bayesian Boolean Matrix Factorization for cancer genomics
problem Identifying coordinated feature changes in cancer
method Bayesian Boolean Matrix Factorization
result Captures widespread, near-simultaneous chromosome-number changes
Proposes a new tensor factorization model for better link prediction in knowledge graphs.
problem Lack of information in treating missing and non-existing relations equally in tensor factorization models.
method Introduces a binary tensor factorization model with probit link to address the issue.
result Shows improved prediction accuracy and interpretability compared to existing models.
New method separates model and non-model risks for more practical asset pricing.
problem Asset pricing under model-uncertainty.
method Binary model-risks and constraints over preferences; unique model-risk pricing formula.
result Unique model-risk pricing formula with dynamically conserved constant.
In this article, we study the problem of pricing defaultable bond with discrete default intensity and barrier under constant risk free short rate using higher order binary options and their integrals. In our credit risk model, the risk free short rate is a constant and the default event occurs in an expected manner whe…
Paper ranks stocks by compression risk, not volatility.
problem Investment risk not correlated with stock price volatility.
method Binary-ternary compressive coding of price change time series.
result Compression risk is a better indicator of stock investment risk.
Enhances LLM quantization with MDBF, improving perplexity and accuracy.
problem Limited performance of Double Binary Factorization in extreme quantization.
method Introduces Multi-envelope DBF, retaining sign matrices and replacing single envelope with rank-l envelope. result Improves perplexity and zero-shot accuracy over previous binary formats.
The paper analyzes recalibration methods for binary classifiers under distribution shift.
problem Recalibrating binary classifiers to match a target prior probability.
method Analysis of distribution shift assumptions and proposal of new recalibration methods.
result QMM methods provide conservative results for risk weights functions.
Develops a continuous compliance index for Islamic equity screening.
problem Binary rulebooks lead to inconsistent compliance assessment of firms.
method Integrates six leading financial and business activity standards into a single continuous index.
result Firms with the same pass/fail label can differ significantly in compliance strength.
The paper develops algorithms for Boolean matrix factorization using IP and heuristics.
problem Approximating binary input matrices as products of smaller binary factors.
method Alternating optimization with integer programming and greedy/local-search heuristics.
result Proposed methods improve scalability and performance compared to existing techniques.
New risk factors improve stress testing accuracy.
problem Improving stress testing accuracy with new risk factors.
method Adapted PCA and autoencoders for dimension reduction and interpretation.
result Aggregated risk factors enhance stress testing outcomes.
Study quantile reward identification with 1-bit feedback constraints.
problem Best arm identification with quantile reward and 1-bit communication.
method Proposes an algorithm using noisy binary search for quantile reward estimation.
result Derives upper and lower bounds on sample complexity for 1-bit feedback.
Develops a binary tree model for option pricing with skew dynamics.
problem Option pricing in incomplete markets with skew dynamics.
method Binary tree model with skew Brownian motion dynamics.
result Model preserves skewness under both discrete and continuous time limits.
Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this …
Unified framework for fair classification with group-blindness/awareness guarantees.
problem Challenges in enforcing fairness and group-blindness in binary classification.
method Unified framework based on post-processing procedure, applicable to various group fairness notions.
result Minimax rate-optimality of the proposed algorithm with controlled excess risk.
Paper analyzes impact of PRM on binary random variables and distribution shifts.
problem Impact of performative risk minimization on binary random variables and distribution shifts.
method Formulated two measures of impact, derived explicit formulas for full information, and provided estimators for partial information.
result PRM can have amplified side effects compared to methods that do not model data shift.
Study tests if equity factors explain Bitcoin's risk and returns.
problem Explaining Bitcoin's risk and return with equity factors.
method Applied statistical methods to test Fama-French factors on Bitcoin's excess returns.
result Fama-French factors have explanatory power on Bitcoin's risk and returns.
This paper presents a novel approach to speaker subspace modelling based on Gaussian-Binary Restricted Boltzmann Machines (GRBM). The proposed model is based on the idea of shared factors as in the Probabilistic Linear Discriminant Analysis (PLDA). GRBM hidden layer is divided into speaker and channel factors, herein t…
This study examines the evolving causal structure of equity risk factors.
problem Redundancy and risk contagion in multi-factor strategies during financial crises.
method Causal structure learning methods applied to US equity market data over 29 years.
result Statistically significant sparsifying trend of causal structure during normal times, but densification during financial stress.
We discuss when and why custom multi-factor risk models are warranted and give source code for computing some risk factors. Pension/mutual funds do not require customization but standardization. However, using standardized risk models in quant trading with much shorter holding horizons is suboptimal: 1) longer horizon …
Optimal cutoff interval for risk scores improves binary classification accuracy.
problem Improving binary classification accuracy with abstention.
method Determines optimal cutoff interval for risk scores, refraining from decisions outside this interval.
result Minimizes classification margin and maximizes accuracy within the interval.
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
problem The redundancy of extensive bond factor literature in explaining corporate bond risk premia.
method Bayesian Model Averaging Stochastic Discount Factor analysis of 18 quadrillion models.
result A Bayesian Model Averaging SDF explains risk premia better than low-dimensional models, with an out-of-sample Sharpe ratio of 1.5 to 1.8.
A new approach to risk allocation balances asset and factor risks.
problem Challenges in estimating expected returns for portfolio optimization.
method Risk Budgeting framework that allocates risk at the factor level.
result Effective portfolios can be constructed by balancing asset and factor risks.
We give a complete algorithm and source code for constructing general multifactor risk models (for equities) via any combination of style factors, principal components (betas) and/or industry factors. For short horizons we employ the Russian-doll risk model construction to obtain a nonsingular factor covariance matrix.…