arXiv:2608. 12424v1 Announce Type: cross Abstract: This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods.
arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.
arXiv:2607. 04103v3 Announce Type: replace-cross Abstract: Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management.
The paper introduces an AI economist agent that integrates large language models, retrieval‑augmented generation, knowledge graphs, and quantitative models to conduct evidence‑based economic and financial scenario analysis. The framework orchestrates LLM agents to plan analyses, retrieve relevant evidence, and structure economic mechanisms, while registered quantitative models produce numerical outcomes and predefined tests validate intermediate results for inclusion in the final report. Applied to European macro‑financial stress scenarios and bank capital analysis, the empirical study demonstrates the agent’s ability to combine flexible evidence retrieval and scenario construction while maintaining traceability to sources and explicit model calculations.
arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.
arXiv:2606. 03184v1 Announce Type: cross Abstract: Financial forecasting is difficult due to low signal-to-noise ratios, latent factors, heavy tails, regime shifts, and jumps.
EXAONE Forecast for Finance (EXAONE Finance) is a financial time‑series foundation model designed to overcome the limitations of existing models that rely on self‑attention and assume fully observed data. It replaces self‑attention with a causal 1D convolution for temporal mixing and a group‑aware pooling MLP for variate mixing, achieving linear‑time complexity. The model is pretrained on a large, diverse financial corpus and, through masked context augmentation, learns to handle missing data, ultimately topping the FinVerse benchmark across accuracy, ranking, and profitability metrics.
arXiv:2609.23703v1 Announce Type: cross Abstract: Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrat...
arXiv:2607. 04103v1 Announce Type: cross Abstract: The release of SR 26-2 marks a significant modernization of U.
arXiv:2607. 28127v1 Announce Type: cross Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs).
arXiv:2606. 29347v1 Announce Type: cross Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets.
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
arXiv:2608.20343v1 Announce Type: new Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...