arXiv:2608. 06108v1 Announce Type: new Abstract: Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals, horizons, portfolios, and risk boundaries.
By Yuanhong Jiang, Jingjie Zou, Zhenghong Lin, Xusheng Yu, Qiqi Huang, Shuai Jia, Shijie Dai
arXiv:2607. 19453v1 Announce Type: cross Abstract: We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance Spot paper policies after assumed costs.
By Ayoub Jadouli
arXiv:2608.23308v1 Announce Type: cross
Abstract: An LLM asked for a trading strategy returns three artifacts at once: a natural-language rationale, an executable implementation, and once run, a trac...
By Arther Tian, Alex Ding, Simon Wu, Aaron Chan
FinRiskAtlas is a Chinese-language benchmark designed to evaluate large language models (LLMs) for financial risk review by focusing on decision‑aligned tasks rather than generic financial knowledge. It contains 9,742 instances across 53 task families, including 42 domain‑knowledge families and 11 downstream review operations defined by explicit evaluation contracts. The extended FinRisk‑Ask framework replays 680 pre‑action states from 104 professional trajectories, withholding future evidence during inference to assess evidence‑state control and request targeting. Results across 33 model configurations show that operation‑level evaluation yields distinct rankings and that knowledge‑based shortlisting can incur significant regret, while frequent use of the Ask branch does not necessarily improve evidence acquisition, highlighting gaps in broad financial capability scores.
By Suyang Zhong, Jingzhe Zhu, Qi Xu, Liyao Sun, Yin Wang, Qingqing Sun, Shuai Chen, Tianyi Zhang
The study examines how different harness configurations affect a fixed large language model’s compliance with banking conduct rules. Four open‑weight models were tested across five harnesses on 156 pre‑registered retail‑banking scenarios, yielding 3,120 two‑turn episodes scored for rule violations and over‑refusals. Results show that while overall compliance rates varied modestly (87.3%–92.1%), the impact of a harness differed markedly between models, with some models’ best and worst harnesses differing by over 10 percentage points and a same‑model compliance reviewer reducing violations from 9.6% to 6.4%.
whyItMatters":"The findings suggest that validating a model’s compliance requires testing the specific model‑harness pair rather than the model alone, highlighting the importance of action logs over conversation transcripts for accurate conduct assessment."
By Mehmet Murat Huyut
An LLM asked for a trading strategy returns three artifacts at once: a natural-language rationale, an executable implementation, and once run, a track record. Whether these are the same object is rare...