1 code implementation • 23 May 2024 • Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang
As financial institutions and professionals increasingly incorporate Large Language Models (LLMs) into their workflows, substantial barriers, including proprietary data and specialized knowledge, persist between the finance sector and the AI community.
1 code implementation • 7 Oct 2023 • Neng Wang, Hongyang Yang, Christina Dan Wang
This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts.
2 code implementations • 6 Oct 2023 • Boyu Zhang, Hongyang Yang, Tianyu Zhou, Ali Babar, Xiao-Yang Liu
Financial sentiment analysis is critical for valuation and investment decision-making.
1 code implementation • 19 Jul 2023 • Xiao-Yang Liu, Guoxuan Wang, Hongyang Yang, Daochen Zha
In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.
1 code implementation • 22 Jun 2023 • 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.
2 code implementations • 9 Jun 2023 • Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang
While proprietary models like BloombergGPT have taken advantage of their unique data accumulation, such privileged access calls for an open-source alternative to democratize Internet-scale financial data.
4 code implementations • 25 Apr 2023 • Xiao-Yang Liu, Ziyi Xia, Hongyang Yang, Jiechao Gao, Daochen Zha, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.
4 code implementations • 6 Nov 2022 • Xiao-Yang Liu, Ziyi Xia, Jingyang Rui, Jiechao Gao, Hongyang Yang, Ming Zhu, Christina Dan Wang, Zhaoran Wang, Jian Guo
However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage.
1 code implementation • 13 Dec 2021 • Xiao-Yang Liu, Jingyang Rui, Jiechao Gao, Liuqing Yang, Hongyang Yang, Zhaoran Wang, Christina Dan Wang, Jian Guo
In this paper, we present a FinRL-Meta framework that builds a universe of market environments for data-driven financial reinforcement learning.
no code implementations • 7 Nov 2021 • Xiao-Yang Liu, Hongyang Yang, Jiechao Gao, Christina Dan Wang
In this paper, we present the first open-source framework \textit{FinRL} as a full pipeline to help quantitative traders overcome the steep learning curve.
6 code implementations • 19 Nov 2020 • Xiao-Yang Liu, Hongyang Yang, Qian Chen, Runjia Zhang, Liuqing Yang, Bowen Xiao, Christina Dan Wang
In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies.
4 code implementations • 20 Dec 2019 • Xinyi Li, Yinchuan Li, Hongyang Yang, Liuqing Yang, Xiao-Yang Liu
In this paper, we propose a novel deep neural network DP-LSTM for stock price prediction, which incorporates the news articles as hidden information and integrates difference news sources through the differential privacy mechanism.
9 code implementations • 19 Nov 2018 • Xiao-Yang Liu, Zhuoran Xiong, Shan Zhong, Hongyang Yang, Anwar Walid
We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return.