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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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199397596794 · Jun 202019922001200920172026
48 results for agenda setting

One important effect of price shocks in the United States has been increased political attention paid to the structure and performance of oil and natural gas markets, along with some governmental support for energy conservation. This paper describes how price changes helped lead the emergence of a political agenda acco…

2015-02-25abs ↗pdf ↗

Two computational models analyze political topics in social media tweets.

problem Measuring political attention in social media is labor-intensive and restrictive.
method Two computational models: supervised classifier and unsupervised topic model.
result Models provide different benefits: supervised classifier reduces labor, unsupervised model uncovers political and non-political uses.

Tackling climate change is at the top of many agendas. In this context, emission trading schemes are considered as promising tools. The regulatory framework for an emission trading scheme introduces a market for emission allowances and creates a need for risk management by appropriate financial contracts. In this work,…

2010-11-26abs ↗pdf ↗

New eco-systemic prudential policies aim to finance green companies, reducing systemic financial risk.

problem Insufficient financing for green companies despite available savings and monetary management.
method Reorient corporate accounting towards socio-environmental solvency, facilitating access with public guarantees.
result Green financing increases, reducing systemic financial risk and promoting less leveraged investments.

In this article I describe a research agenda for securing machine learning models against adversarial inputs at test time. This article does not present results but instead shares some of my thoughts about where I think that the field needs to go. Modern machine learning works very well on I.I.D. data: data for which e…

2019-03-14abs ↗pdf ↗

In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and internationa…

2018-12-13abs ↗pdf ↗

Study maps research streams in biodiversity finance, identifies key areas.

problem Biodiversity loss and need for finance to reverse trends.
method Quantitative bibliometric analysis of 189,456 references.
result Identifies eight primary research streams in biodiversity finance.

Survey connects XAI and surrogate modeling for better understanding of complex systems.

problem Opaque surrogate models hide input-output relationships, hindering decision-making.
method Synthesizes XAI techniques with surrogate modeling workflows.
result Surrogate models can be made more interpretable using XAI methods.

Paper proposes using word embeddings to detect trolls in social media debates.

problem Preventing online harassment through rapid detection of offensive posts.
method Word embedding models for identifying fast-changing topics and negative content.
result GloVe model helps in discovering new keywords for trolling detection.

New framework uses OR to ensure AI systems make safe decisions.

problem Ensuring generative AI systems make safe decisions as they gain autonomy.
method Developed a conceptual framework combining flow-based models and adversarial robustness.
result Increased autonomy requires new OR approaches for feasibility, robustness, and stress testing.

Policy shifts between Trump and Biden impact ESG investments, creating volatility.

problem Dramatic policy shifts between Trump and Biden administrations affect ESG investments.
method Analyzes contrasting policies of Trump and Biden administrations and their impacts on ESG investments.
result Policy changes significantly influence ESG investments, leading to volatility and portfolio reassessment.

Triangulation filters spurious circuits in multilingual models.

problem Unreliable explanations of multilingual models across languages.
method Formalizes reference families and introduces triangulation as a causal acceptance rule.
result Triangulation provides a falsifiable standard for mechanistic claims.

Understanding the causes of crime is a longstanding issue in researcher's agenda. While it is a hard task to extract causality from data, several linear models have been proposed to predict crime through the existing correlations between crime and urban metrics. However, because of non-Gaussian distributions and multic…

2017-12-08abs ↗pdf ↗

In the eyes of a rationalist like Descartes or Spinoza, human reasoning is flawless, marching toward uncovering ultimate truth. A few centuries later, however, culminating in the work of Kahneman and Tversky, human reasoning was portrayed as anything but flawless, filled with numerous misjudgments, biases, and cognitiv…

2018-10-15abs ↗pdf ↗

This paper quantifies the impact of Dow Jones Sustainability Index listing on stock returns.

problem Measuring the impact of listing on the Dow Jones Sustainability Index.
method A matching portfolio approach to analyze pre- and post-listing returns.
result Cumulative abnormal returns are significantly positive in the weeks leading to the official announcement.

This study shows how trade policy uncertainty affects stock-T bill correlations.

problem The impact of trade policy uncertainty on stock-T bill relationships.
method Extended Dynamic Conditional Correlation (DCC) framework incorporating exogenous variables.
result Trade policy uncertainty significantly alters stock-T bill correlations, especially under specific political conditions.

Paper develops a consistent model selection framework for learning Hypotheses Space from data.

problem Avoiding overfitting in complex spaces with limited data.
method Develops a model selection framework based on Learning Spaces, selecting a Hypotheses Space from data.
result The method converges with probability one to a target Hypotheses Space, providing a consistent framework for model selection.

We educe a perspective on how best to regulate the bank of tomorrow in frames of debate launched by the International Centre for Financial Regulation and Financial Times. Our goal is to create a conceptual framework for policymakers and regulators to shape the international financial system in century of globalization …

2012-06-10abs ↗pdf ↗

New deep learning model for matching sets of items, preserving exchangeability.

problem Matching two different sets of items while preserving exchangeability.
method Exchangeable deep neural networks architecture and efficient training framework.
result Significant improvements in fashion set recommendation and group re-identification.

The paper analyzes set-to-set matching with neural networks, focusing on theoretical generalization.

problem Theoretical analysis of set-to-set matching with neural networks.
method Generalization error analysis of set-to-set matching with neural networks.
result Theoretical insights into the behavior of set-to-set matching models.

Generative model learns to autoencode and generate sets of images.

problem Learning to represent and generate sets of images with unknown number of sets.
method Set Distribution Networks (SDNs) learn set encoder, discriminator, generator, and prior.
result SDNs can reconstruct and generate sets of images with preserved attributes.

Maps sets to probability distributions to minimize information loss.

problem Learning to map sets to probability distributions to preserve information.
method Relates set operations to probability distribution interpolations and demonstrates a preliminary solution.
result Experimental results show the effectiveness of the set embedding approach.

New set-valued star-shaped risk measures introduced for better risk assessment.

problem Improving risk assessment in financial contexts.
method Developed new set-valued star-shaped risk measures and proved their representation theorems.
result Set-valued star-shaped risk measures can be represented as unions of set-valued convex risk measures.

We introduce the concept of hereditarily non uniformly perfect sets, compact sets for which no compact subset is uniformly perfect, and compare them with the following: Hausdorff dimension zero sets, logarithmic capacity zero sets, Lebesgue 2-dimensional measure zero sets, and porous sets. In particular, we give an exa…

2016-09-23abs ↗pdf ↗

Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our m…

2019-06-15abs ↗pdf ↗