Predicts Taiwan's 2016 election using online media data.
problem Predicting Taiwan's 2016 presidential election using online data.
method Leveraged heterogeneous social media data and Kalman filter to fuse signals.
result Public opinions in online media are more predictive than traditional polls.
This paper analyzes ETFs with Taiwan exposure, finding heavy tails and asymmetric volatility.
problem Heavy tails and asymmetric volatility in Taiwan-related ETFs.
method Tail-risk diagnostics, asymmetric volatility modeling, and portfolio optimization under mean--variance and CVaR criteria.
result CVaR optimization produces more concentrated allocations, favoring SMH during the post-COVID AI-driven expansion.
We refute Taleb's claim that election forecasts are arbitrage-violating.
problem The validity of probabilistic election forecasts using no-arbitrage pricing techniques.
method We use mild assumptions to show all forecasts are arbitrage-free.
result Taleb's heuristic for evaluating forecasts is incorrect.
Paper uses Twitter data to analyze public opinion on economic issues during elections.
problem Limited, expensive, and time-consuming surveys for economic issues.
method Combines sentiment analysis and topic modeling for Twitter data.
result Effective analysis of economic concerns during the 2012 US presidential election.
We investigated the critical dynamics on the daily Taiwan stock exchange index (TSE) from 1971 to 2005, and the 5-min intraday data from 1996 to 2005. A global persistence exponent θp was defined for non-equilibrium critical phenomena \cite{Janssen,Majumdar}, and describing dynamic behavior in an economic index \c…
Volatility, fitting with first order Landau expansion, stationarity, and causality of the Taiwan stock market (TAIEX) are investigated based on daily records. Instead of consensuses that consider stock market index change as a random time series we propose the market change as a dual time series consists of the index a…
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 …
Data set tracks real-time election results for 4 hours post-October 2019 Portuguese elections.
problem Real-time tracking of election results for predictive modeling.
method Real-time data collection and interval-based analysis.
result Data set supports various predictive modeling tasks including numerical forecasting.
Model election dynamics and disinformation impact using information flow.
problem Understanding the unpredictable evolution of election polls and disinformation effects.
method Model election dynamics by specifying the flow of relevant information, deriving poll statistics as outputs.
result Derived outputs (poll statistics) reveal insights into prediction, disinformation impact, and optimal information management strategies.
Study on how elections affect democracy and development in Sub-Saharan Africa.
problem Understanding the impact of elections on democracy and development in Sub-Saharan Africa.
method Examined 33 Sub-Saharan African countries post-election, analyzing development indicators over a decade.
result Elections can lead to positive, negative, or no change in development, depending on the pre-election regime and election outcome.
A framework traces ideology changes on social media during the 2016 U.S. election.
problem Understanding how ideology changed on social media during the 2016 U.S. election.
method Jointly estimating ideology of users and news sites, tracing changes over time.
result Both liberal and conservative users became more polarized over time.
Human stablecoin transactions predict political risk in cryptocurrency markets.
problem Predicting political risk in cryptocurrency markets.
method Structural break analysis and surrogate-based robustness tests.
result Human-driven stablecoin transactions shift significantly before major political events.
Improves stock market predictions on Election Day.
problem Predicting stock market volatility on Election Day.
method Combining large language models with specialized agents.
result EDSMF model improves S&P 500 prediction accuracy.
This paper examines how voter concentration affects election outcomes in district-based systems.
problem How does the spatial concentration of electors impact election results?
method The authors frame the spatial distribution of electors in a probabilistic setting and explore models to capture intra-district polarization. They use Likelihood-free Inference under the Approximate Bayesian Computation framework and supervised regression methods to estimate parameters.
result The models can capture statistical properties of real elections and show how voter distributions can change election results.
A thermodynamic theory explains EU election vote distributions.
problem Describing EU election vote distributions.
method Tracing parallels between system energies of coupled nonlinear oscillators and party vote fractions.
result The Rayleigh-Jeans (RJ) theory well depicts EU vote results and candidate vote dispersion.
Fuzzy Forests reduces feature space in high-dimensional survey data.
problem High-dimensional and highly correlated datasets in social science.
method Fuzzy Forests algorithm, a variant of Random Forests.
result Partisan polarization was the strongest factor in the 2020 presidential election.
GAT-AGNN learns stock trends using graph and attention mechanisms.
problem Predicting dynamic stock trends in a complex market.
method Sequential graph structure with attention mechanisms.
result GAT-AGNN outperforms state-of-the-art methods in stock trend prediction.
Calculates winning probability for three candidates based on support rates and information timing.
problem Determining optimal strategy for three candidates in an election.
method Closed-form solution using support rates, political spectrum positioning, time left, and information revelation rate.
result Optimal strategy can be complex, especially for candidates in the center of a polarized electorate.
ELECTS model predicts crop type early from satellite data.
problem Early decision-making in crop type mapping.
method End-to-end deep learning model with modular design.
result ELECTS reduces data requirements for accurate early predictions.
Proposes a transfer learning framework to improve U.S. election prediction models.
problem Limited spatial data and spatial dependence challenges in presidential election prediction.
method Proposes a novel transfer learning framework within the SAR model, using a two-stage algorithm with transferring and debiasing stages.
result Substantially improves prediction accuracy and outperforms traditional methods in U.S. presidential swing states.
The study uncovers stock synergy networks using mutual information in Indian stock market data.
problem Detecting clusters of stocks in synergy in the Indian Stock Market.
method Analysis of high frequency data, mutual information for non-linearity, comparison with correlation method.
result Mutual information method successfully identifies effective networks compared to correlation method.
Study uses Viber and street polls to estimate Belarus election ratings and turnout.
problem Obtaining accurate ratings of candidates in Belarus's banned polls.
method Bayesian multilevel regression with poststratification.
result Estimated ratings and turnout contradict official election results.
Quantum model outperforms classical in training but underperforms in real-world metrics.
problem Mismatch between proxy reward signals and true investment objectives in financial domains.
method Hybrid quantum-classical reinforcement learning framework with automated feature engineering.
result Quantum models achieve higher training rewards but underperform in real-world metrics.
The paper uses Black-Scholes model to analyze political support and coalition agreements.
problem Determining the minimum support level for a minor party in a pre-electoral coalition.
method Modeling political support as a stochastic process with a deterministic growth rate and applying Black-Scholes option pricing theory.
result The minimum support level for a minor party to gain a representative in a pre-electoral coalition.
Paper proposes a faster neural machine translation model using election methods and Q-learning.
problem Slower inference time in complex attention models.
method Modelled attention network using election methods and Q-learning.
result Inference time is less than a standard Bahdanau translator, results comparable.
A new method for efficient option pricing using AR and MCS.
problem Infeasibility of pricing financial derivatives due to computational limitations.
method Multi-path option pricing approach via autoregression and Monte Carlo Simulations.
result Our approach is comparable to prior models in pricing weekly TAIEX options.
Study reveals why polls were inaccurate in 2016 US election.
problem Why opinion polls were inaccurate in 2016 US election.
method Proposes clean sensing framework for optimal parameter estimation from heterogeneous data sources.
result Optimal polling strategy allocates resources based on data quality and quantity.
Study of negative ads on social media during U.S. midterm elections.
problem Understanding the effectiveness and mechanisms of negative advertising on social media.
method Machine learning for sentiment analysis, AI image recognition, ordinal regressions.
result Negative ads are less effective than previously thought, anger is a key mechanism.
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…
The goal of this research was to find a way to extend the capabilities of computers through the processing of language in a more human way, and present applications which demonstrate the power of this method. This research presents a novel approach, Rhetorical Analysis, to solving problems in Natural Language Processin…
New model detects postoperative complications early after surgery.
problem Early detection of postoperative complications in patients.
method Hidden Markov Model sequence classifier analyzing postoperative temperature sequences.
result Improved classification performance compared to other machine learning classifiers.
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 / …
Detects anomalous behavior in social media users by analyzing content and connections.
problem Identifying disruptive patterns in user behavior on social media platforms.
method Joint representation learning of content and connection to detect anomalous behavior.
result Observed densely connected users engaging in local politics and exhibiting troll-like behavior.
The study analyzes meta-data of viral tweets to distinguish fake news from real ones.
problem Identifying political fake news on Twitter using metadata.
method Analysis of 1.5M tweets from the 2016 US election, focusing on viral tweets.
result Significant differences found in followers, URLs, and user verification.
Pension benefits valuation with Florida second election and DB Underpin options.
problem Valuation of hybrid pension benefits with embedded options.
method Arbitrage-free pricing methodology to value Bermudan options.
result Illustrates the difference between FSE, DB Underpin, and Bermudan DB Underpin options.
The portfolio optimization problem is a basic problem of financial analysis. In the study, an optimization model for constructing an options portfolio with a certain payoff function has been proposed. The model is formulated as an integer linear programming problem and includes an objective payoff function and a system…
New model reveals voter preferences from aggregate election data.
problem Infer individual-level voter preferences from aggregate election data.
method Modeling aggregate count data as Poisson binomial, relating probabilities to covariates using logistic and neural networks.
result Model predicts voter preferences at precinct and individual levels.
New results for modeling voter probabilities in elections.
problem Modeling voter preferences with aggregate data and individual covariates.
method Maximum likelihood estimation for Poisson binomial distribution, approximated with heteroscedastic Gaussian.
result Existence and curvature results for the MLE of the Poisson binomial likelihood.
Bayesian estimators for causal inference using hierarchical Gaussian Processes.
problem Estimating causal effects in sharp and fuzzy RD/RK designs.
method Hierarchical Gaussian Process models for regression and classification.
result Hierarchical GP models improve precision and coverage of RD/RK estimations.
New voting strategies show committee-based consensus can scale efficiently.
problem Ensuring honest committees in committee-based consensus protocols.
method Empirical analysis of simpler voting strategies and their convergence to optimality.
result Simpler voting strategies converge to optimality exponentially quickly, ensuring robustness and efficiency.
Estimates mode of discrete distributions with fewer samples.
problem Identifying the mode of a discrete distribution with high probability.
method Generalizes PPR martingale confidence sequences to handle multiple modes.
result PPR-1v1 stopping rule is asymptotically optimal and significantly more efficient.
Bayesian nonparametric models for data with heterogeneous particles.
problem Deconvolving data with heterogeneous particles, like voter tallies in elections.
method Nonparametric deconvolution models (NDMs) using two tiers of Dirichlet processes.
result NDMs can recover how factor distributions vary locally for each observation.
We propose using canonical correlation analysis (CCA) to generate features from sequences of medical billing codes. Applying this novel use of CCA to a database of medical billing codes for patients with diverticulitis, we first demonstrate that the CCA embeddings capture meaningful relationships among the codes. We th…
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…
Scores political leanings in Web3 betting markets.
problem Understanding political motivations in decentralized prediction markets.
method Constructing PBLS from Polymarket data, analyzing 15k addresses, 4k events, 8k markets.
result Validated PBLS through internal and external comparisons, revealing political and profit motives.
We investigate the spatial and temporal structures of four financial markets in Greater China. In particular, we uncover different characteristics of the four markets by analyzing the sector and subsector structures which are detected through the random matrix theory. Meanwhile, we observe that the Taiwan and Hongkong …
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.
Prediction markets can shape political behavior through persistent signals, not just forecast accuracy.
problem The role of prediction markets beyond forecasting.
method Transaction-level evidence from the 2024 U.S. presidential election, Signal Credibility Index (SCI).
result Price signals in prediction markets are more influential due to persistence, breadth of trader types, and cross-platform consensus.