FinGPT: Open-Source Financial Large Language Models
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Shijie Wu & Ozan Irsoy & Steven Lu & Vadim Dabravolski & Mark Dredze & Sebastian Gehrmann & Prabhanjan Kambadur & David Rosenberg & Gideon Mann, 2023. "BloombergGPT: A Large Language Model for Finance," Papers 2303.17564, arXiv.org, revised Dec 2023.
- Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org.
- Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Feb 2024.
- Yupeng Cao & Zhi Chen & Qingyun Pei & Fabrizio Dimino & Lorenzo Ausiello & Prashant Kumar & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024. "RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data," Papers 2404.07452, arXiv.org.
- Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Tat-Seng Chua, 2024. "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models," Papers 2402.03659, arXiv.org, revised Feb 2024.
- Neng Wang & Hongyang Yang & Christina Dan Wang, 2023. "FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets," Papers 2310.04793, arXiv.org, revised Nov 2023.
- Zhongyang Guo & Guanran Jiang & Zhongdan Zhang & Peng Li & Zhefeng Wang & Yinchun Wang, 2023. "Shai: A large language model for asset management," Papers 2312.14203, arXiv.org.
- Carolina Camassa, 2023. "Legal NLP Meets MiCAR: Advancing the Analysis of Crypto White Papers," Papers 2310.10333, arXiv.org, revised Oct 2023.
- Yang Li & Yangyang Yu & Haohang Li & Zhi Chen & Khaldoun Khashanah, 2023. "TradingGPT: Multi-Agent System with Layered Memory and Distinct Characters for Enhanced Financial Trading Performance," Papers 2309.03736, arXiv.org.
- Tao Ren & Ruihan Zhou & Jinyang Jiang & Jiafeng Liang & Qinghao Wang & Yijie Peng, 2024. "RiskMiner: Discovering Formulaic Alphas via Risk Seeking Monte Carlo Tree Search," Papers 2402.07080, arXiv.org, revised Feb 2024.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Lezhi Li & Ting-Yu Chang & Hai Wang, 2023. "Multimodal Gen-AI for Fundamental Investment Research," Papers 2401.06164, arXiv.org.
- Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
- Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
- Frank Xing, 2024. "Designing Heterogeneous LLM Agents for Financial Sentiment Analysis," Papers 2401.05799, arXiv.org.
- Seppälä, Timo & Mucha, Tomasz & Mattila, Juri, 2023. "Beyond AI, Blockchain Systems, and Digital Platforms: Digitalization Unlocks Mass Hyper-Personalization and Mass Servitization," ETLA Working Papers 106, The Research Institute of the Finnish Economy.
- Zhaofeng Zhang & Banghao Chen & Shengxin Zhu & Nicolas Langren'e, 2024. "From attention to profit: quantitative trading strategy based on transformer," Papers 2404.00424, arXiv.org.
- Shuyang Wang & Diego Klabjan, 2023. "An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading," Papers 2309.00626, arXiv.org.
- Wentao Zhang & Lingxuan Zhao & Haochong Xia & Shuo Sun & Jiaze Sun & Molei Qin & Xinyi Li & Yuqing Zhao & Yilei Zhao & Xinyu Cai & Longtao Zheng & Xinrun Wang & Bo An, 2024. "A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist," Papers 2402.18485, arXiv.org, revised Feb 2024.
- Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org.
- Masanori Hirano & Kentaro Imajo, 2024. "Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training," Papers 2404.10555, arXiv.org.
- Haoqiang Kang & Xiao-Yang Liu, 2023. "Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination," Papers 2311.15548, arXiv.org.
- Mamalis, Marios & Kalampokis, Evangelos & Karamanou, Areti & Brimos, Petros & Tarabanis, Konstantinos, 2023. "Can Large Language Models Revolutionalize Open Government Data Portals? A Case of Using ChatGPT in statistics.gov.scot," OSF Preprints 9b35z, Center for Open Science.
- Claudia Biancotti & Carolina Camassa, 2023. "Loquacity and visible emotion: ChatGPT as a policy advisor," Questioni di Economia e Finanza (Occasional Papers) 814, Bank of Italy, Economic Research and International Relations Area.
- Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
- Alejandro Lopez-Lira & Yuehua Tang, 2023. "Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models," Papers 2304.07619, arXiv.org, revised Sep 2023.
- Eric Fischer & Rebecca McCaughrin & Saketh Prazad & Mark Vandergon, 2023. "Fed Transparency and Policy Expectation Errors: A Text Analysis Approach," Staff Reports 1081, Federal Reserve Bank of New York.
- Dat Mai, 2024. "StockGPT: A GenAI Model for Stock Prediction and Trading," Papers 2404.05101, arXiv.org, revised Apr 2024.
More about this item
NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2023-07-17 (Artificial Intelligence)
- NEP-BAN-2023-07-17 (Banking)
- NEP-BIG-2023-07-17 (Big Data)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2306.06031. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.