Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions. Large language models can accelerate this process, but directly relying on them to rewrite trading strategies often introduces hallucinated edits, strategy drift, and backtest overfitting.
arXiv:2607. 12455v1 Announce Type: new Abstract: Quantitative strategy optimization remains largely manual, requiring domain experts to identify weak signals, tune risk-control rules, and repeatedly validate iterative revisions.
By Jie Mao, Changlun Li, Xiang Li, Qiqi Duan, Jinhui Yuan, Xiang Liu, Yuyu Luo, Jing Tang, Xiaowen Chu, Nan Tang
arXiv:2607. 15414v1 Announce Type: cross Abstract: Large Language Models (LLMs) have emerged as powerful tools for processing the heterogeneous information environments of modern financial markets.
By Geofrey Ntale
arXiv:2605.28853v2 Announce Type: replace-cross
Abstract: Portfolio optimization in real-world financial markets is notoriously difficult due to non-stationarity, noisy data, and high transaction cos...
By Rahul Fernandes, Travis Desell
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.
By Yifan Hu, Yuante Li, Peiyuan Liu, Yuxia Zhu, Naiqi Li, Tao Dai, Shu-tao Xia, Dawei Cheng, Changjun Jiang
arXiv:2608. 27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns.
By Joshua Le Grice
arXiv:2609.36914v1 Announce Type: new
Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...
By Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang
The paper explores tabular deep learning for equity signal generation, training five model classes on daily data from about 300 large‑cap US stocks over eleven years. By using Bayesian optimisation that targets trading performance across three distinct market regimes, the authors achieve regime‑robust hyperparameter selection, yielding out‑of‑sample signal precision above random and a Hybrid ensemble (XGBoost + TabNet) with an annualised return of 51.26% and a Sharpe ratio of 2.44. The study also finds that alternative data adds limited value beyond technical and fundamental features, and that the ensemble’s outperformance is driven by stock selection rather than market exposure.
arXiv:2608. 11785v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions.
By Chang Zhou, Xingtong Yu, Minbin Huang, Zhennan Wu, Yuan Fang, Hong Cheng, Xinming Zhang
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).
By Giorgos Iacovides, Wuyang Zhou, Danilo Mandic
arXiv:2605. 27887v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM), a critical financial decision-making task, remains poorly benchmarked.
By Yuxuan Zhao, Sijia Chen, Ningxin Su
arXiv:2508. 00554v5 Announce Type: replace-cross Abstract: In financial trading, large language model (LLM)-based agents demonstrate significant potential, but their decisions can be sensitive to noisy and non-stationary market information.
By Li Zhao, Rui Sun, Zuoyou Jiang, Bo Yang, Yuxiao Bai, Mengting Chen, Jing Li, Zuo Bai