activity
20192025
most citedFinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance

83 citations · 159 across the 12 of their papers we have counts for

collaborators

12 papers

cs.AI2025★ 1 cited

FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance

Hongyang Yang, Likun Lin, Yang She +5

Enterprise Resource Planning (ERP) systems serve as the digital backbone of modern financial institutions, yet they continue to rely on static, rule-based workflows that limit adap…

cs.CL2024★ 2 cited

FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs

Yixuan Liang, Yuncong Liu, Neng Wang +3

Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose du…

q-fin.ST2024★ 2 cited

FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models

Hongyang Yang, Boyu Zhang, Neng Wang +8

As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and special…

cs.CL2023★ 8 cited

FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets

Neng Wang, Hongyang Yang, Christina Dan Wang

In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-based models for the financial sector is increasingly evident. However, the integration o…

q-fin.ST2023★ 24 cited

FinGPT: Open-Source Financial Large Language Models

Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang

Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-qu…

cs.LG2023★ 1 cited

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

Xiao-Yang Liu, Ziyi Xia, Hongyang Yang +6

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environmen…