Focused on AI applications in the fintech sector
The article reviews the 3S‑Trader framework, a training‑free multi‑LLM system that uses scoring, strategy, and selection modules to construct weekly stock portfolios, and shows that it outperforms rule‑based and deep‑learning baselines on DJIA and sector datasets with strong risk‑adjusted returns.
This article summarizes ten recent arXiv papers (Dec 6‑12 2025) that explore AI‑driven techniques—from neural‑network ranking and reinforcement learning to quantum models and LLM agents—for quantitative finance and investment decision‑making.
CoRA freezes pretrained time‑series foundation models, extracts multimodal covariate embeddings, evaluates their causal relevance with a trainable Granger‑Causal Embedding, and injects them via a zero‑initialized condition module, achieving up to 31.1% MSE reduction across single‑ and multi‑modal forecasting tasks.
The paper introduces Agent Market Arena (AMA), a lifelong, real‑time benchmark that evaluates diverse LLM‑based trading agents across crypto and equity markets, revealing that agent architecture, rather than the underlying LLM, drives performance differences and risk‑adjusted returns.
AlphaQuanter tackles the three major limitations of existing LLM trading agents by introducing a single‑agent framework that dynamically orchestrates market tools, learns transparent decision policies via reinforcement learning, and achieves state‑of‑the‑art performance on key financial metrics across extensive stock‑level experiments.
This article presents concise summaries of five recent AI‑driven finance papers, covering a stress‑testing framework for LLM trading agents, an orchestration framework for financial agents, an event‑reflection memory model for stock forecasting, a hybrid LLM‑Bayesian network architecture for options wheel strategies, and their experimental results.
This article analyzes the RETuning framework, which addresses LLMs' bias toward analyst opinions and lack of evidence weighting in stock movement prediction by introducing a two‑stage cold‑start fine‑tuning and reinforcement learning pipeline, evaluating it on the large Fin‑2024 dataset and demonstrating significant F1 gains, inference‑time scaling, and out‑of‑distribution robustness.
The paper introduces DRFN, a dual‑relation fusion network that jointly models static and dynamic stock relationships using multimodal BERT and GRU encodings, achieving significantly lower RMSE and MAE than baseline models on both Chinese and US market datasets.
The Hermes framework introduces a moving‑aggregation module and a multi‑scale fusion module within a hypergraph network to capture industry lead‑lag interactions and multi‑scale stock relationships, achieving superior accuracy and efficiency over existing SOTA methods on three real US stock datasets, as demonstrated by extensive experiments and ablations.
The article reviews the TSci framework, an LLM‑driven multi‑agent system that automates data diagnosis, model selection, ensemble forecasting, and report generation for time‑series prediction, achieving up to 38 % lower MAE than LLM baselines and improving report quality across five evaluation dimensions.
