13 papers
This paper evaluates the impact of LLM-based news sentiment analysis on stock price prediction, demonstrating that DeBERTa outperforms other models and that an ensemble approach achieves 80% accuracy, while sentiment features provide modest improvements to various time-series forecasting architectures.
This paper proposes a Bitcoin price prediction model using Combinatorial Fusion Analysis (CFA) to integrate diverse machine learning models via rank-score characteristics and weighted combinations, achieving a superior Mean Absolute Percentage Error (MAPE) of 0.19% that outperforms individual models and existing prediction methods.
This paper introduces an ensemble Gaussian Process Regression method that significantly reduces computational complexity while outperforming existing machine learning models in predicting US stock returns and constructing superior mean-variance optimal portfolios based on prediction uncertainty.
This paper proposes a novel operator-level ARCH framework for data in general separable Hilbert spaces that models the full evolution of the conditional covariance operator, establishing conditions for stationarity and deriving consistent estimators, which are validated through simulations and an application to high-frequency intraday returns.
This paper proposes a hybrid Hidden Markov Model that combines Laplace quantile-defined market states with a Poisson-driven jump-duration mechanism to generate synthetic equity excess growth rates that simultaneously preserve heavy-tailed distributions, volatility clustering, and realistic tail-state dwell times, outperforming standard GARCH and HMM models in joint distributional and temporal fidelity.
This paper proposes an integrated framework combining a node transformer architecture with BERT-based sentiment analysis to model stock market graphs and social media sentiment, demonstrating superior forecasting accuracy (0.80% MAPE) and directional precision compared to traditional ARIMA and LSTM models across 20 S&P 500 stocks from 1982 to 2025.
This paper proposes that the negative skew and positive mean observed in stock returns stem from the broken symmetry of stochastic volatility between gains and losses, a phenomenon effectively modeled by a modified Jones-Faddy skew t-distribution that is validated using daily S&P 500 return data.
This paper addresses the challenge of measuring the "temperature" parameter in agent-based econophysics models beyond capital markets by establishing a measurement equation for a two-option decision system in a news environment and demonstrating its application to influence average opinion in competing subsystems.
This paper introduces Temporal Hierarchy Forecasting (THieF) as a computationally efficient method that significantly improves day-ahead electricity price prediction accuracy across diverse models and markets by reconciling forecasts for hourly and block products.
This paper introduces EDINET-Bench, a challenging open-source benchmark derived from ten years of Japanese financial reports to evaluate LLMs on complex tasks like fraud detection and earnings forecasting, revealing that current models struggle significantly without specialized scaffolding and highlighting the need for more realistic evaluation frameworks.
This paper proposes a robust parametric framework based on the minimum density power divergence estimator (MDPDE) that achieves asymptotically consistent jump detection in high-frequency CIR and CKLS models by exploiting the scale separation between diffusion and jump increments to establish a Gumbel-distributed detection threshold.
This paper proposes a practical framework for analyzing extreme values in finite, multivariate, and correlated systems—demonstrated through high-frequency finance data—by rotating returns into the correlation matrix's eigenbasis to isolate collective and idiosyncratic effects, thereby enabling the use of univariate peaks-over-threshold methods to estimate tail risks while accounting for nonstationarity.
This paper proposes and validates a dynamic cryptocurrency portfolio optimization strategy that integrates technical indicators (RSI and SMA) with AI-driven sentiment analysis (VADER and Google Gemini) into a mean-variance framework, demonstrating superior risk-adjusted returns and cumulative growth compared to traditional benchmarks while highlighting the need for enhanced risk management to mitigate drawdowns during market stress.