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