96 citations · 276 across the 16 of their papers we have counts for
16 papers
A Practical Machine Learning Approach for Dynamic Stock Recommendation
Hongyang Yang, Xiao-Yang Liu, Qingwei Wu
Stock recommendation is vital to investment companies and investors. However, no single stock selection strategy will always win while analysts may not have enough time to check al…
Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
Hongyang Yang, Xiao-Yang Liu, Shan Zhong +1
Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we pro…
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…
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Boyu Zhang, Hongyang Yang, Tianyu Zhou +2
Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their t…
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Boyu Zhang, Hongyang Yang, Xiao-Yang Liu
Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive ca…
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…
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…
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
Xinyi Li, Yinchuan Li, Hongyang Yang +2
Stock price prediction is important for value investments in the stock market. In particular, short-term prediction that exploits financial news articles is promising in recent yea…
Practical Deep Reinforcement Learning Approach for Stock Trading
Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong +2
Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the p…