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.
Paper finds political networks reduce bond issuance costs in China.
Study examines Trump's crypto influence on markets, revealing conflicts and vulnerabilities.
Venice used 'helicopter money' to subsidize during famine and plague, but it caused instability.
Study maps research streams in biodiversity finance, identifies key areas.
Scores political leanings in Web3 betting markets.
New method predicts political ideology from online activity.
TBIP uses texts to quantify lawmakers' political positions.
Unfulfilled expectations from macro-economic initiatives during the Great Recession and the massive shift into globalization echo today with political upheaval, anti-establishment propaganda, and looming trade/currency wars that threaten domestic and international value chains. Once stable entities like the EU now look…
System detects financial news temporality combining NLP and ML.
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…
Understanding how political attention is divided and over what subjects is crucial for research on areas such as agenda setting, framing, and political rhetoric. Existing methods for measuring attention, such as manual labeling according to established codebooks, are expensive and can be restrictive. We describe two co…
Based mainly on examples of interest in mechanics, we define the notion of a polite group action. One may view this as not only trying to give a more general notion than properness of a group action, but also to more fully understand the role of invariant functions in describing just about everything of interest in red…
Human stablecoin transactions predict political risk in cryptocurrency markets.
Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate th…
Model forecasts hourly electricity demand influenced by weather, socio-economic, and political factors.
Italy's vaccine coverage fell, leading to political debates and online social media discussions.
A growing number of empirical studies suggest that negative advertising is effective in campaigning, while the mechanisms are rarely mentioned. With the scandal of Cambridge Analytica and Russian intervention behind the Brexit and the 2016 presidential election, people have become aware of the political ads on social m…
Study max- and min-stability under first-order stochastic dominance, finding new functional characterizations.
In this paper we present a kinetic model with stochastic game-type interactions, analyzing the relationship between the level of political competition in a society and the degree of economic liberalization. The above issue regards the complex interactions between economy and institutional policies intended to introduce…
Every year at the United Nations, member states deliver statements during the General Debate discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international pol…
Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads o…
Managing large-scale transportation infrastructure projects is difficult due to frequent misinformation about the costs which results in large cost overruns that often threaten the overall project viability. This paper investigates the explanations for cost overruns that are given in the literature. Overall, four categ…
The paper uses Black-Scholes model to analyze political support and coalition agreements.
Model predicts political ideology using context vectors to mitigate bias and scarcity.
This study maps systemic risks in TradFi and DeFi, highlighting their interdependence.
In Natural Language Processing (NLP) tasks, data often has the following two properties: First, data can be chopped into multi-views which has been successfully used for dimension reduction purposes. For example, in topic classification, every paper can be chopped into the title, the main text and the references. Howev…
News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.
The study analyzes how wartime controls influenced zaibatsu stock prices in Japan.
In the era of big data, analysts usually explore various statistical models or machine learning methods for observed data in order to facilitate scientific discoveries or gain predictive power. Whatever data and fitting procedures are employed, a crucial step is to select the most appropriate model or method from a set…
This paper considers an often forgotten relationship, the time delay between a cause and its effect in economies and finance. We treat the case of Foreign Direct Investment (FDI) and economic growth, - measured through a country Gross Domestic Product (GDP). The pertinent data refers to 43 countries, over 1970-2015, - …
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
Anti-ELAB protests affected Hong Kong firms' stock prices, especially those linked to pan-democrats.
IndexGAN predicts stock trends using GAN with expert knowledge and news context.
The study shows how trade uncertainty affects stock-bond correlations over time.
cMCA uses contrastive learning to identify latent subgroups in political party data.
Policy shifts between Trump and Biden impact ESG investments, creating volatility.
HSBC grew from Hong Kong to global dominance despite political upheavals.
There is bountiful evidence that political uncertainty stemming from presidential elections or doubt about the direction of future policy make financial markets significantly volatile, especially in proximity to close elections or elections that may prompt radical policy changes. Although several studies have examined …
Study examines how governance, corruption, and R&D affect economic development.
Paper connects fair machine learning to political philosophy, highlighting flaws in ideal approaches.
Locational Marginal Pricing aims to free UK power markets.
Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information space to confuse and distract voters. Past works to identify disruptive patterns are mostly focused on analyzing the content of tweets. In this study, we jointly embed the informati…
This article presents a preliminary approach towards characterizing political fake news on Twitter through the analysis of their meta-data. In particular, we focus on more than 1.5M tweets collected on the day of the election of Donald Trump as 45th president of the United States of America. We use the meta-data embedd…
MGM improves media profiling by integrating textual and structural features.
Computer Vision and machine learning methods were previously used to reveal screen presence of genders in TV and movies. In this work, using head pose, gender detection, and skin color estimation techniques, we demonstrate that the gender disparity in TV in a South Asian country such as Bangladesh exhibits unique chara…
This study shows how trade policy uncertainty affects stock-T bill correlations.
BERT models can classify multilingual party manifestos across different dimensions.
Derives a Matern Gaussian process on hypergraphs for regression and embedding.