83 citations · 159 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Jingyang Rui +6
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement le…
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting
Berend Jelmer Dirk Gort, Xiao-Yang Liu, Xinghang Sun +3
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and opt…
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance
Xiao-Yang Liu, Jingyang Rui, Jiechao Gao +5
Deep reinforcement learning (DRL) has shown huge potentials in building financial market simulators recently. However, due to the highly complex and dynamic nature of real-world ma…
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
Xiao-Yang Liu, Hongyang Yang, Jiechao Gao +1
Deep reinforcement learning (DRL) has been envisioned to have a competitive edge in quantitative finance. However, there is a steep development curve for quantitative traders to ob…
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
Xiao-Yang Liu, Hongyang Yang, Qian Chen +4
As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to trai…
Risk Management via Anomaly Circumvent: Mnemonic Deep Learning for Midterm Stock Prediction
Xinyi Li, Yinchuan Li, Xiao-Yang Liu +1
Midterm stock price prediction is crucial for value investments in the stock market. However, most deep learning models are essentially short-term and applying them to midterm pred…