Keyi Wang

Nikolaus Holzer, Keyi Wang, Kairong Xiao et al.

Reinforcement learning has demonstrated great potential for performing financial tasks. However, it faces two major challenges: policy instability and sampling bottlenecks. In this paper, we revisit ensemble methods with massively parallel simulations on graphics processing units (GPUs), significantly enhancing the computational efficiency and robustness of trained models in volatile financial markets. Our approach leverages the parallel processing capability of GPUs to significantly improve the sampling speed for training ensemble models. The ensemble models combine the strengths of component agents to improve the robustness of financial decision-making strategies. We conduct experiments in both stock and cryptocurrency trading tasks to evaluate the effectiveness of our approach. Massively parallel simulation on a single GPU improves the sampling speed by up to $1,746\times$ using $2,048$ parallel environments compared to a single environment. The ensemble models have high cumulative returns and outperform some individual agents, reducing maximum drawdown by up to $4.17\%$ and improving the Sharpe ratio by up to $0.21$. This paper describes trading tasks at ACM ICAIF FinRL Contests in 2023 and 2024.

Yan Wang, Keyi Wang, Shanshan Yang et al.

The complexity of the Generally Accepted Accounting Principles (GAAP) and the hierarchical structure of eXtensible Business Reporting Language (XBRL) filings make financial auditing increasingly difficult to automate and verify. While large language models (LLMs) have demonstrated strong capabilities in unstructured text understanding, their ability to reason over structured, interdependent, and taxonomy-driven financial documents remains largely unexplored. To fill this gap, we introduce FinAuditing, the first taxonomy-aligned, structure-aware, multi-document benchmark for evaluating LLMs on financial auditing tasks. Built from real US-GAAP-compliant XBRL filings, FinAuditing defines three complementary subtasks, FinSM for semantic consistency, FinRE for relational consistency, and FinMR for numerical consistency, each targeting a distinct aspect of structured auditing reasoning. We further propose a unified evaluation framework integrating retrieval, classification, and reasoning metrics across these subtasks. Extensive zero-shot experiments on 13 state-of-the-art LLMs reveal that current models perform inconsistently across semantic, relational, and mathematical dimensions, with accuracy drops of up to 60-90% when reasoning over hierarchical multi-document structures. Our findings expose the systematic limitations of modern LLMs in taxonomy-grounded financial reasoning and establish FinAuditing as a foundation for developing trustworthy, structure-aware, and regulation-aligned financial intelligence systems. The benchmark dataset is available at Hugging Face.

Yan Wang, Yang Ren, Lingfei Qian et al.

Accurately understanding numbers from financial reports is fundamental to how markets, regulators, algorithms, and normal people read the economy and the world, yet even with XBRL (eXtensible Business Reporting Language) designed to tag every figure with standardized accounting concepts, mapping thousands of facts to over 10,000 U.S. GAAP concepts remains costly, inconsistent, and error-prone. Existing benchmarks define tagging as flat, single-step, extreme classification over small subsets of US-GAAP concepts, overlooking both the taxonomy's hierarchical semantics and the structured nature of real tagging, where each fact must be represented as a contextualized multi-field output. These simplifications prevent fair evaluation of large language models (LLMs) under realistic reporting conditions. To address these gaps, we introduce FinTagging, the first comprehensive benchmark for structure-aware and full-scope XBRL tagging, designed to evaluate LLMs' ability to extract and align financial facts through numerical reasoning and taxonomy alignment across text and tables. We define two subtasks: FinNI for numeric identification, which extracts numerical entities and their types from XBRL reports, and FinCL for concept linking, which maps each extracted entity to the corresponding concept in the full US-GAAP taxonomy. Together, these subtasks produce a structured representation of each financial fact. We evaluate diverse LLMs under zero-shot settings and analyze their performance across both subtasks and overall tagging accuracy. Results show that LLMs generalize well in numeric identification but struggle with fine-grained concept linking, revealing current limitations in structure-aware reasoning for accurate financial disclosure. All code and datasets are available on GitHub and Hugging Face.