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arXiv research

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.

168,742 papers · 148 categories

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48 results for discretionary benefits

Study shows human advisors use context to improve student outcomes in algorithm-assisted advising.

problem How human advisors use context to guide interventions in algorithm-assisted advising.
method Mixed-methods approach combining quantitative and qualitative data from a randomized controlled trial.
result 2 out of 3 interventions by advisors were plausibly 'expertly targeted' to students using non-algorithmic context.

We analyze an optimal stopping problem with random maturity under a nonlinear expectation with respect to a weakly compact set of mutually singular probabilities P\mathcal{P}. The maturity is specified as the hitting time to level 00 of some continuous index process at which the payoff process is even allowed to have…

2015-05-28abs ↗pdf ↗

Evidence acquisition costs influence disclosure behavior and preference.

problem How evidence acquisition costs affect disclosure behavior and preference.
method Analyzes sender-receiver interactions with covert and overt evidence acquisition, varying certification costs.
result Equilibria converge to the Pareto-worst free-learning equilibrium as costs vanish, and receivers prefer covert to overt acquisition.

We analyze expenditure patterns of discretionary funds by Brazilian congress members. This analysis is based on a large dataset containing over 77 million expenses made publicly available by the Brazilian government. This dataset has, up to now, remained widely untouched by machine learning methods. Our main contribut…

2018-11-29abs ↗pdf ↗

Robinhood users react strongly to overnight price changes and big losers, trading quickly after extreme losses.

problem Understanding trading behavior of Robinhood users, especially in high-frequency trading scenarios.
method Analyzed intraday and overnight price changes, focusing on big losers and gainers.
result Robinhood users react more to overnight price changes and big losers, trading quickly after extreme losses.

The paper examines how macroeconomic control tools lost effectiveness, leading to a 'dark ages' period.

problem Loss of effectiveness of control tools in macroeconomic stabilization policy.
method Historical analysis of macroeconomic stabilization policy from 1948 to 1993.
result The overstatement of the Lucas critique and Kydland and Prescott's time-inconsistency led to a period of ineffective stabilization policy.

The study identifies extremal dependence in financial markets using a bootstrap-based testing procedure.

problem Accurately identifying extremal dependence in multivariate heavy-tailed financial data.
method Bootstrap-based testing procedure applied to U.S. and Chinese stock returns.
result The U.S. exhibits more isolated clustering of dependent assets compared to China.

As part of Basel II's incremental risk charge (IRC) methodology, this paper summarizes our extensive investigations of constructing transition probability matrices (TPMs) for unsecuritized credit products in the trading book. The objective is to create monthly or quarterly TPMs with predefined sectors and ratings that …

2011-02-18abs ↗pdf ↗

Predicts stock volatility using Twitter data and random forests.

problem Predicting stock implied volatility using Twitter data.
method Random forests with ablation study on different predictors, including Twitter attention and sentiment features.
result Certain sectors like Consumer Discretionary, Technology, Real Estate, and Utilities are easier to predict.

Develops a new framework for integrating satellite allocations in small portfolios.

problem Feasibility constraints in small portfolios, not return predictability, are the primary concerns.
method A four-layer feasibility framework: physical, economic, structural, and epistemic.
result Closed-form feasibility bounds on satellite size, turnover, and breadth without return forecasts.

Cost-benefit analysis often assumes accurate estimates, but this study finds significant inaccuracies.

problem Inaccurate cost and benefit estimates in public investments.
method Largest dataset of public investments, statistical analysis of cost overruns and benefit shortfalls.
result Significant inaccuracies in cost and benefit estimates of public investments.

Study examines the impact of employment benefit costs on firm profitability.

problem Impact of employment benefit costs on firm profitability.
method Panel data regression analysis using E-Views.
result There is a significant positive relationship between employment benefit costs and firm profitability.

Paper explores how to design federated learning protocols that benefit all participants while maintaining privacy.

problem Privacy concerns undermine the accuracy benefits of federated learning in privacy-sensitive domains.
method The paper provides conditions for mutually beneficial federated learning protocols and designs protocols that maximize total utility and accuracy.
result The paper demonstrates that federated learning can be designed to be mutually beneficial, striking a balance between privacy and model accuracy.

We present new minimax results that concisely capture the relative benefits of source and target labeled data, under covariate-shift. Namely, we show that the benefits of target labels are controlled by a transfer-exponent γγ that encodes how singular Q is locally w.r.t. P, and interestingly allows situations where tr…

2018-03-05abs ↗pdf ↗

Finance has benefited from the Wolfram's NKS approach but it can and will benefit even more in the future, and the gains from the influence may actually be concentrated among practitioners who unintentionally employ those principles as a group.

2012-10-04abs ↗pdf ↗

The quality of an induced model by a learning algorithm is dependent on the quality of the training data and the hyper-parameters supplied to the learning algorithm. Prior work has shown that improving the quality of the training data (i.e., by removing low quality instances) or tuning the learning algorithm hyper-para…

2014-03-13abs ↗pdf ↗

We identify action representations from video data, proving their statistical benefits.

problem Identifying latent action policies from video data.
method Entropy-regularized LAPO objective, formalizing desiderata for action representations.
result Entropy-regularized LAPO identifies action representations satisfying desiderata under suitable conditions.

Study adaptive clinical trial methods for identifying patient subpopulations with treatment benefit.

problem Adaptive identification of patient subpopulations with treatment benefit in clinical trials.
method Proposes AdaGGI and AdaGCPI meta-algorithms for subpopulation construction.
result Empirical investigation of AdaGGI and AdaGCPI performance across various simulation scenarios.

The paper introduces a method to measure the benefits of incidental supervision signals.

problem Lack of a principled way to measure the benefits of various types of incidental supervision signals.
method Unified PAC-Bayesian motivated informativeness measure, PABI.
result Demonstrates PABI's effectiveness in quantifying the value added by various types of incidental signals.

Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy by appealing to a number of intuitive benefits, including learning over temporally extended transitions, exploring over temporally extended …

2019-09-23abs ↗pdf ↗

The paper studies the benefits of curriculum learning in linear regression tasks.

problem Theoretical understanding of curriculum learning's benefits in machine learning.
method Theoretical analysis of curriculum learning in structured and unstructured multitask linear regression problems.
result Adaptive learning in the unstructured setting is fundamentally harder than oracle learning, but not in the structured setting.

We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes. A Graph Neural Process is defined as a Conditional Neural Process that operates on arbitrary graph data. It takes features of sparsely observed context points as input, and outputs a distribution over targ…

2019-02-26abs ↗pdf ↗

A new method sorts projects using Quicksort and Bradley-Terry model for uncertain long-term benefits.

problem Selecting projects with uncertain long-term benefits.
method Combining Quicksort and Bradley-Terry model for ranking projects based on uncertain long-term benefits.
result Proposed methods outperform existing aggregation methods and can be combined with sampling techniques.

The paper prices a new life insurance policy for couples, considering various contingent benefits.

problem Valuation of life insurance policies for dependent coupled lives.
method Employed the QPQP-rule combining real-world and risk-neutral measures for pricing.
result Explicit pricing expressions for the new insurance policy were derived and analyzed.

Actuaries tackle loss of earning capacity in Denmark, balancing public benefits and private insurance.

problem Balancing public benefits and private insurance for loss of earning capacity in Denmark.
method Innovative approaches from researchers and practitioners, leveraging actuarial expertise.
result Development of equitable, data-driven solutions to mitigate risk and enhance societal well-being.

The paper analyzes the benefit-cost ratio for feature selection in machine learning.

problem Tackling the challenge of distinguishing relevant features from noise in feature selection.
method Simulation study with different cost and data settings to analyze the benefit-cost ratio.
result The benefit-cost ratio can overemphasize cheap noise features in scenarios with large cost differences and small effect sizes.

We discuss the binary nature of funding impact in derivative valuation. Under some conditions, funding is either a cost or a benefit, i.e., one of the lending/borrowing rates does not play a role in pricing derivatives. When derivatives are priced, considering different lending/borrowing rates leads to semi-linear BSDE…

2017-03-01abs ↗pdf ↗

Study evaluates when splitting classifiers can improve performance despite disparate treatment.

problem Impact of disparate treatment in classification models.
method Comparison of split classifiers and group-blind classifiers, quantifying performance improvement.
result Proves an equivalent expression for the benefit-of-splitting which can be efficiently computed.

Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive lis…

2016-02-10abs ↗pdf ↗

Curriculum learning shows marginal benefits over random ordering on standard datasets.

problem Investigating the benefits of curriculum learning over random ordering in training neural networks.
method Experiments with curriculum, anti-curriculum, and random-curriculum orderings on various datasets and conditions.
result Curriculum learning only marginally improves performance on standard datasets and is outperformed by random ordering.