This paper calculates risk-dependent centrality of Brazilian stocks, showing rankings vary with external risk and crisis events.
problem Understanding asset rankings in the Brazilian stock market under varying external risks.
method Computed risk-dependent centrality (RDC) for Brazilian stocks traded from 2008 to 2020, analyzing volatility and returns.
result Asset rankings based on RDC vary with external risk and crisis events, with higher volatility in crisis periods.
Two-stage risk control for ranked retrieval systems.
problem Assessing prediction uncertainty and risk control in sequential machine learning systems.
method Developed two-stage risk control methods based on LTT and CRC frameworks, leveraging sequential nature of retrieval and ranking phases.
result The proposed methods provide theoretical guarantees and reduce computational burden compared to prior work.
Paper extends ranking metrics theory for financial positions.
problem Developing a new class of functionals for evaluating financial positions.
method Axiomatic framework based on monotonicity and cash-quasiconcavity.
result Linking ranking metrics to families of acceptance sets and risk measures.
Paper extends ranking metrics theory for financial positions.
problem Developing a new class of performance evaluation methods.
method Axiomatic framework based on monotonicity and cash-quasiconcavity.
result Linking ranking metrics to families of acceptance sets and risk measures.
New insights into risk aversion for complex decision models.
problem Understanding risk aversion in non-monotone decision models.
method Characterization of probabilistic risk aversion for generalized rank-dependent functions.
result Probabilistic risk aversion is determined by the distortion function, which is convex or scaled quantile-spread mixtures.
CRS model improves ranking data modeling with theoretical guarantees.
problem Lack of rich, multimodal models for ranking data.
method Contextual Repeated Selection (CRS) model for multimodal ranking data.
result CRS model significantly outperforms existing methods in various ranking contexts.
Study shows risk-averse investors have consistent ranking of risky assets.
problem Ranking of risky assets in short-term investments.
method Analyzes various decision problems regarding risky assets with continuous returns.
result Risk-averse decision makers have the same ranking over risky assets.
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.
Paper discusses extending Gini score for tied rankings and case weights.
problem Extending Gini score for tied rankings and case weights.
method Discuss and adapt Gini score for ties and case weights.
result Gini score can be used for tied rankings and case weights.
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…
Study optimal reward schemes for inducing desired player performance in risky contests.
problem Designing optimal rewards to encourage desired performance levels in risky contests.
method Analyzed the optimal reward schemes for inducing average and specific rank performance.
result Optimal reward schemes can have surprising shapes, not just related to inequality.
New RL algorithm maximizes CVaR in low-rank MDPs with provable efficiency.
problem Maximizing CVaR in large state spaces with function approximation.
method Upper Confidence Bound (UCB) bonus-driven algorithm for low-rank MDPs.
result Achieves sample complexity of O(H^7 A^2 d^4 / τ^2 ε^2) for ε-optimal CVaR.
Under expected utility the local index of absolute risk aversion has played a central role in many applications. Besides, its link with the "global" concepts of the risk and probability premia has reinforced its attractiveness. This paper shows that, with an appropriate approach, similar developments can be achieved in…
Randomized algorithm solves vector-valued regression problems with low-rank operators.
problem Vector-valued regression problems involving infinite-dimensional spaces.
method Randomized Reduced Rank Regression (R4) using Gaussian sketching for optimization.
result R4 estimators are efficient and accurate, with empirical risk close to optimal.
We implement momentum strategies using reward-risk measures as ranking criteria based on classical tempered stable distribution. Performances and risk characteristics for the alternative portfolios are obtained in various asset classes and markets. The reward-risk momentum strategies with lower volatility levels outper…
We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions. The basic model is built based on satisficing measure (SM) which yields a single index for risk comparison. Since SM is a dual representation for a family of r…
Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-k items from the estimated preference order is recommended to the end user in the decreasing …
Study asymptotic properties of generalized shortfall risk measures for heavy-tailed risks.
problem Understanding risk measures for heavy-tailed risks.
method Derive asymptotic expansions for generalized shortfall risk measures.
result Unified theory for risk measures including distortion and utility-based measures.
New theory extends rank-dependent utility for risk and ambiguity.
problem Modeling decision-making under risk and ambiguity.
method Axiomatizes a new preference relation with ambiguity index, probability weighting, and utility function.
result Extends rank-dependent utility to risk and ambiguity, reducing to existing models under specific conditions.
Counterparty risk denotes the risk that a party defaults in a bilateral contract. This risk not only depends on the two parties involved, but also on the risk from various other contracts each of these parties holds. In rather informal markets, such as the OTC (over-the-counter) derivative market, institutions only rep…
Mirror descent algorithm recovers low-rank matrices in matrix sensing.
problem Matrix sensing with low-rank matrices under certain conditions.
method Discrete-time mirror descent applied to empirical risk with Bregman divergence analysis.
result Mirror descent converges to a matrix minimizing a specific nuclear norm-related quantity.
New method improves compatibility of risk stratification models without sacrificing accuracy.
problem Compatibility issues arise when updating clinical machine learning models.
method Proposes rank-based compatibility measure and new loss function.
result Increased compatibility of models by 0.019 with no loss in discriminative performance.
Proposes a new method to rank risky investments based on Omega measure.
problem Evaluating and ranking risky investment projects.
method Introduces an investment certainty equivalence approach and uses the Omega measure.
result Proposed method ranks projects differently from conventional risk-adjusted discount rate (RADR) approach.
Evaluation of systemic risk in networks of financial institutions in general requires information of inter-institution financial exposures. In the framework of Debt Rank algorithm, we introduce an approximate method of systemic risk evaluation which requires only node properties, such as total assets and liabilities, a…
DP-GD achieves dimension-independent convergence for unconstrained private GLMs.
problem Differentially private empirical risk minimization for unconstrained GLMs.
method Differentially private gradient descent (DP-GD).
result DP-GD achieves an excess empirical risk of $ ilde O\left(\sqrt{ exttt{rank}}/εn
ight)$ for unconstrained GLMs.
In medical risk modeling, typical data are "scarce": they have relatively small number of training instances (N), censoring, and high dimensionality (M). We show that the problem may be effectively simplified by reducing it to bipartite ranking, and introduce new bipartite ranking algorithm, Smooth Rank, for robust lea…
Study variance-optimal hedging of forward curve derivatives under stochastic volatility.
problem Variance-optimal hedging of forward curve derivatives with stochastic volatility.
method Assumes HJM-Musiela dynamics modulated by stochastic covariance, uses Galtchouk-Kunita-Watanabe projection.
result Density of finite-maturity strategies, convergence of finite-rank projections, decomposition of hedging error.
Study examines risk premium convergence rates in risk sharing contracts.
problem Analyzing risk premium convergence rates in risk sharing contracts.
method Examines the limiting behavior of risk premium associated with Pareto optimal risk sharing contracts under general law-invariant risk measures.
result Risk premium convergence rate is typically n1/2, not n. Node centrality is one of the most important and widely used concepts in the study of complex networks. Here, we extend the paradigm of node centrality in financial and economic networks to consider the changes of node "importance" produced not only by the variation of the topology of the system but also as a consequen…
LLMs can help explain credit risk models but not autonomously.
problem Leveraging LLMs for post-hoc explainability in credit risk models.
method Comparison of LLM outputs with SHAP and coefficient-based attributions on three LMs.
result LLMs reliably preserve feature-importance rankings but poorly align with autonomous explanations.
The study proposes a method for risk reduction without relying on risk measurement.
problem Theoretical utopia of risk minimization vs. practical risk reduction.
method Generalization of matrix rank and condition number for identifying riskiest scenarios.
result Risk reduction achieved without risk measurement, validated by real data.
A new method predicts stock ranking uncertainty to improve trading performance during regime shifts.
problem Ranking models fail during regime shifts, leading to suboptimal performance.
method Adapting DEUP to rankers, predicting rank displacement and uncertainty, and proposing a two-level deployment policy.
result The two-level deployment policy improves risk-adjusted performance and indicates DEUP adds value mainly as a tail-risk guard.
We simplify SSL by approximating redundant structural components with low-rank factorization.
problem Improving self-supervised learning performance with limited labeled data.
method Low-rank approximation of structural redundancy, introducing ε_s to measure approximation quality.
result The proposed method enhances SSL performance, as shown by theoretical and experimental validations.
We consider the teacher-student setting of learning shallow neural networks with quadratic activations and planted weight matrix W∗∈Rm×d, where m is the width of the hidden layer and d≤m is the data dimension. We study the optimization landscape associated with the empirical and the popula…
A risk-aware RL approach using RDEU and Wasserstein ball for robust performance.
problem Optimizing risk-aware performance criteria in uncertain environments.
method Rank dependent expected utility (RDEU) for risk assessment, Wasserstein ball for robustness, actor/agent framework.
result Explicit policy gradient formulae for robust optimization.
New concept of attitude towards probability introduced in risk sharing problems.
problem Risk sharing problems and attitudes towards probability.
method Generalized definition of probability premium, local approximation, rank-dependent utility model, dual theory.
result Attitude towards probability can be first-order or second-order, depending on the model.
Enhances currency strategy Sharpe ratio by 30% using context-aware Learning to Rank.
problem Sub-optimal ranking of assets during critical market periods.
method Context-aware Learning to Rank model based on Transformer architecture.
result Significantly improves Sharpe ratio and various performance metrics.
New framework for conditional risk minimization using optimal transport.
problem High-stakes decisions with side information, especially economic conditions.
method Universal framework based on union-ball formulation in optimal transport.
result Offers interpretability, tractability, and scalability for various risk functionals.
New method quantifies systemic risk of firms in supply networks.
problem Quantifying economic systemic risk of firms from supply networks.
method Unique value-added tax dataset; novel approach for computing ESR.
result A tiny fraction of companies have high systemic risk impacting 23% of national production.
Diffsurv extends differentiable sorting to handle censored time-to-event data.
problem Handling censored time-to-event data in survival analysis.
method Extending differentiable sorting methods to account for censored samples.
result Diffsurv outperforms established baselines in various risk prediction scenarios.
This paper evaluates investment risks in LATAM AI startups using DCF method.
problem Unique challenges and risks faced by LATAM tech startups.
method Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) metrics; Discounted Cash Flow (DCF) method.
result Developed a ranking of emerging powers in Latin America for tech startup investment.
Global balance index measures systemic risk in financial networks.
problem Measuring systemic risk in financial networks.
method Defined global balance index based on a diffusive process and linear system.
result Global balance index correlates with systemic risk measures.
Ranking problems, also known as preference learning problems, define a widely spread class of statistical learning problems with many applications, including fraud detection, document ranking, medicine, credit risk screening, image ranking or media memorability. In this article, we systematically review different types…
A new framework assesses financial and ESG risks for sustainable investing.
problem Measuring risk and reward in sustainable investing considering environmental, social, and governance factors.
method Proposes axiomatic definitions for ESG-coherent risk measures and reward-risk ratios based on bivariate random variables.
result Empirical analysis ranks stocks using the proposed measures.
This paper studies the estimation of low-rank Markov chains from empirical trajectories. We propose a non-convex estimator based on rank-constrained likelihood maximization. Statistical upper bounds are provided for the Kullback-Leiber divergence and the ℓ2 risk between the estimator and the true transition matri…
Locus scores predictions for risk, reducing large-loss events.
problem Deployment cost from inaccurate predictions, especially large losses.
method Distribution-free loss-scale reliability score using any predictive distribution.
result Reduces large-loss frequency compared to standard heuristics.
Bipartite ranking is an important supervised learning problem; however, unlike regression or classification, it has a quadratic dependence on the number of samples. To circumvent the prohibitive sample cost, many recent work focus on stochastic gradient-based methods. In this paper we consider an alternative approach, …
TripleSurv improves survival analysis by ranking samples with time-adaptive adjustments.
problem Modeling censored time-to-event data with high accuracy and robustness.
method Introduces a time-adaptive coordinate loss function to rank samples and calibrate robustness.
result TripleSurv outperforms state-of-the-art methods on various survival datasets.