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.
Proposes a neural network model to improve predictions in biased datasets.
Study uses Viber and street polls to estimate Belarus election ratings and turnout.
Complex dynamical systems driven by the unravelling of information can be modelled effectively by treating the underlying flow of information as the model input. Complicated dynamical behaviour of the system is then derived as an output. Such an information-based approach is in sharp contrast to the conventional mathem…
This paper examines how voter concentration affects election outcomes in district-based systems.
Donald Trump was lagging behind in nearly all opinion polls leading up to the 2016 US presidential election, but he surprisingly won the election. This raises the following important questions: 1) why most opinion polls were not accurate in 2016? and 2) how to improve the accuracies of opinion polls? In this paper, we …
Blockchain fan tokens boost sports fan engagement by 50%.
Calculates winning probability for three candidates based on support rates and information timing.
Study on continual learning with Twitter data, developing ConGraD algorithm.
ELCO enhances graph generation by adding new nodes and edges.
This paper presents a data set describing the evolution of results in the Portuguese Parliamentary Elections of October 6 2019. The data spans a time interval of 4 hours and 25 minutes, in intervals of 5 minutes, concerning the results of the 27 parties involved in the electoral event. The data set is tailored f…
The prevalence of online media has attracted researchers from various domains to explore human behavior and make interesting predictions. In this research, we leverage heterogeneous social media data collected from various online platforms to predict Taiwan's 2016 presidential election. In contrast to most existing res…
The problem of population recovery refers to estimating a distribution based on incomplete or corrupted samples. Consider a random poll of sample size conducted on a population of individuals, where each pollee is asked to answer binary questions. We consider one of the two polling impediments: (a) in lossy pop…
We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / …
GPI uses GenAI models to infer causal and predictive effects from unstructured data.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding …
The paper uses Black-Scholes model to analyze political support and coalition agreements.
Modern applications of machine learning (ML) deal with increasingly heterogeneous datasets comprised of data collected from overlapping latent subpopulations. As a result, traditional models trained over large datasets may fail to recognize highly predictive localized effects in favour of weakly predictive global patte…
Enhances topic-metadata relationship modeling using Bayesian methods.
The paper proposes using experts' insights in machine learning tasks.
MakerDAO's governance is centralized despite its decentralized claim.
The paper explores learning from label proportions, showing differences in efficiency between LLP and PAC learning.
Among the central tenets of globalization is free migration of labor. Although much has been written about its benefits, little is known about the limitations of globalization, including how immigration affects the anti-globalist sentiment. Analyzing polls data, we find that over the last three years in a group of EU c…
QuEst combines model predictions with observed data to estimate quantile-based measures.
Wasserstein t-SNE embeds hierarchical datasets considering within-unit distributions.
Classification Ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. This study aims to improve the results of identifying the Persian handwritten letters using Error Correcting Output Coding (ECOC) ensemb…
Majority Vote is optimal for reliable data labeling under certain conditions.
Study improves pension scheme efficiency in Kenya through governance and risk management.
We consider the estimation of binary election outcomes as martingales and propose an arbitrage pricing when one continuously updates estimates. We argue that the estimator needs to be priced as a binary option as the arbitrage valuation minimizes the conventionally used Brier score for tracking the accuracy of probabil…
There certainly is little or no doubt that politicians, sometimes consciously and sometimes not, exert a significant impact on stock markets. The evolving volatility over the Republican Donald Trump's surprise victory in the US presidential election is a perfect example when politicians, through announced policies, sen…
Hybrid engine analyzes news sentiment for markets in real-time.
This paper examines how institutional liquidity affects prediction markets.
Imitative and contrarian behaviors are the two typical opposite attitudes of investors in stock markets. We introduce a simple model to investigate their interplay in a stock market where agents can take only two states, bullish or bearish. Each bullish (bearish) agent polls m "friends'' and changes her opinion to bear…
Study optimizes data collection from biased, costly sources to minimize risk.