LiveOption is an evaluation framework designed to assess large language model (LLM) agents in option trading, a domain characterized by nonlinear payoffs and multi-leg strategy construction. The framework treats option trading as structured sequential decision-making under realistic execution and capital constraints, offering a reproducible environment with standardized interaction protocols. It includes three task suites—portfolio overlays, event-driven earnings trading, and 0DTE intraday trading—and introduces a hierarchical metric suite that evaluates action validity, decision quality, risk characteristics, and outcome-level performance, revealing that current agents often fail to achieve competitive returns.
By Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo, Zhengzhao Lai, Yuan Zhang, Chen Liu
arXiv:2608.28646v1 Announce Type: new
Abstract: Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic KYC-style constraints on risk, fees, and div...
By Gaurav Kukreja, Parul Kukreja, Mohammed Abraar, Raj Dandekar, Rajat Dandekar, Sreedath Panat
arXiv:2609.31570v1 Announce Type: new
Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captur...
By Damiano Brigo, Rapha\"el Huser, Dan Leonte
arXiv:2609.14839v1 Announce Type: cross
Abstract: The integration of Large Language Models (LLMs) into automated code optimization introduces a critical reliability risk we term the Efficiency Halluc...
By Sarah Wilson, Gail Kaiser, Patrick Musau
arXiv:2509. 13374v2 Announce Type: replace-cross Abstract: We develop and audit a history-aware financial path generator based on Denoising Levy Probabilistic Models (DLPMs) for conditional equity-index path generation.
By Helin Zhao, Junchi Shen
The paper introduces asymptotically-informed neural‑network architectures for computing Black‑Scholes implied volatility. By learning a trainable partition of the price‑log‑moneyness domain and combining specialised local approximations, the models outperform standard feed‑forward networks across a wide range of parameters. The neural‑network outputs also serve as highly accurate initial guesses for a third‑order Householder scheme, enabling near machine‑precision results after only two refinement iterations.
By Samira Amiriyan, Youness Boutaib
arXiv:2608. 02778v1 Announce Type: new Abstract: We present a novel application of Neural Networks with Local Converging Inputs (NNLCI) to improve the efficiency of existing numerical methods for pricing multi-asset options.
By Harris Cobb, Wenbo Hao, Yingjie Liu
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
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. 27188v1 Announce Type: new Abstract: Accurate option prices do not imply accurate recovery of the latent risk-neutral density.
By Lennon J. Shikhman, Michael Galarnyk, Aadi Dash, Nicholas A. Welsh
Accurate option prices do not imply accurate recovery of the latent risk-neutral density. We study this distinction with two complementary benchmarks.
arXiv:2505. 13553v3 Announce Type: replace-cross Abstract: The hallucination of code generation models hinders their applicability to systems requiring higher safety standards.
By Jaewoo Jeong, Taesoo Kim, Sangdon Park