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.
TIMME detects Twitter users' ideology from sparse, heterogeneous data.
A common assumption of political economy is that profit rates across firms or sectors tend to uniformity, and often models are formulated in which this tendency is assumed to have been realised. But in reality this tendency is never realised and the distribution of firm profits is not degenerate but skewed to the right…
Paper finds political networks reduce bond issuance costs in China.
Scores political leanings in Web3 betting markets.
New method predicts political ideology from online activity.
TBIP uses texts to quantify lawmakers' political positions.
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…
There are few papers about the international trade of flowers, so it is believed that this paper, with this topic, could be an important contribution to the international scientific community. It is intended to analyze if the international trade flowers tendencies and policies are adapted to the actual world global con…
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.
Study optimal futures trading strategies for assets with multiscale central tendency price model.
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…
Study of negative ads on social media during U.S. midterm elections.
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…
Marxism's rate of profit thesis rejected as illogical.
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.
The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of s…
Model predicts political ideology using context vectors to mitigate bias and scarcity.
Study examines Trump's crypto influence on markets, revealing conflicts and vulnerabilities.
Startups is a popular phenomenon that has a significant impact on global economy growth, innovation and society development. However, there is still insufficient understanding about startups, particularly, how to start a new business in the relation to consequent performance. Toward this knowledge, we have performed an…
New method combines machine learning with data assimilation for model error correction.
News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.
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.
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.
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…
Venice used 'helicopter money' to subsidize during famine and plague, but it caused instability.
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.
We develop a model of how information flows into a market, and derive algorithms for automatically detecting and explaining relevant events. We analyze data from twenty-two "political stock markets" (i.e., betting markets on political outcomes) on the Iowa Electronic Market (IEM). We prove that, under certain efficienc…
In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In partic…
QLSTM outperforms LSTM in predicting KSE 100 index movements.