Fetching the paper…
Reading the bibliography…
We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Stock movement prediction from tweets and historical prices
Yumo Xu and Shay B Cohen. 2018 · 1979
Earlier work this paper cites.
The sharpe ratio
William F Sharpe. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The information ratio
Thomas H Goodwin. 1998 · 1998
Earlier work this paper cites.
Robust market making via adversarial reinforcement learning
Thomas Spooner and Rahul Savani. 2020 · 2003
Earlier work this paper cites.
Where do alphas come from?: A new measure of the value of active investment management
Andrew W Lo. 2007 · 2007
Earlier work this paper cites.
Mixture of experts: a literature survey
Saeed Masoudnia and Reza Ebrahimpour. 2014 · 2014
Earlier work this paper cites.
Fundamentally, momentum is fundamental momentum
Robert Novy-Marx. 2015 · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin. 2016 · 2016
Earlier work this paper cites.
101 formulaic alphas
Zura Kakushadze. 2016 · 2016
Earlier work this paper cites.
A review on time series forecasting techniques for building energy consumption
Chirag Deb, Fan Zhang, Junjing Yang, Siew Eang Lee, and Kwok Wei Shah. 2017 · 2017
Earlier work this paper cites.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017 · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
How are you feeling: A personalized methodology for predicting mental states from temporally observable physical and behavioral information
Suppawong Tuarob, Conrad S. Tucker, Soundar R. T. Kumara, C. Lee Giles, Aaron L. Pincus, David E. Conroy, and Nilam Ram. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Online detection of stealthy false data injection attacks in power system state estimation
Aditya Ashok, Manimaran Govindarasu, and Venkataramana Ajjarapu. 2018 · 2018
Earlier work this paper cites.
Investment behaviors can tell what inside: Exploring stock intrinsic properties for stock trend prediction
Chi Chen, Li Zhao, Jiang Bian, Chunxiao Xing, and Tie-Yan Liu. 2019 · 2019
Cited alongside, same era.
Stock alpha mining based on genetic algorithm
Xiaoming Lin, Ye Chen, Ziyu Li, and Kang He. 2019 · 2019
Cited alongside, same era.
Dynamic relu
Yinpeng Chen and et al. 2020 · 2020
Cited alongside, same era.
Financial regulation as interagency competition? The saga of venture capital rule-making in China
Sirui Han and Chao Xi. 2020 · 2020
Cited alongside, same era.
Maps: Multi-agent reinforcement learning-based portfolio management system
Jinho Lee, Raehyun Kim, Seok-Won Yi, and Jaewoo Kang. 2020 · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive nlp tasks
P. Lewis and et al. 2020 · 2020
Multi-agent collaboration: Harnessing the power of intelligent llm agents
Y. Talebirad and A. Nadiri. 2023 · 2023
Later among the works it cites.
Llama: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample. 2023 · 2023
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
B. W. and et al. 2023 · 2023
Later among the works it cites.
Methods for acquiring and incorporating knowledge into stock price prediction: A survey
Liping Wang, Jiawei Li, Lifan Zhao, Zhizhuo Kou, Xiaohan Wang, Xinyi Zhu, Hao Wang, Yanyan Shen, and Lei Chen. 2023 · 2023
Later among the works it cites.
Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Asset returns in deep learning methods: An empirical analysis on sse 50 and csi 300
Weiping Li and Feng Mei. 2020 · 2020
Cited alongside, same era.
Information coefficient as a performance measure of stock selection models
Feng Zhang, Ruite Guo, and Honggao Cao. 2020 · 2020
Cited alongside, same era.
Alphaportfolio: Direct construction through deep reinforcement learning and interpretable ai
L. W. Cong, K. Tang, J. Wang, and Y. Zhang. 2021 · 2021
Cited alongside, same era.
Alphaevolve: A learning framework to discover novel alphas in quantitative investment
Can Cui, Wei Wang, Meihui Zhang, Gang Chen, Zhaojing Luo, and Beng Chin Ooi. 2021 · 2021
Cited alongside, same era.
Symbolic regression via neural-guided genetic programming population seeding
T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt, Daniel M. Faissol, and Brenden K. Petersen. 2021 · 2021
Cited alongside, same era.
Rest: Relational event-driven stock trend forecasting
Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, and Tie-Yan Liu. 2021b · 2021
Cited alongside, same era.
Later among the works it cites.
Investlm: A large language model for investment using financial domain instruction tuning
Y. Yang, Y. Tang, and K. Y. Tam. 2023 · 2023
Later among the works it cites.
A survey of large language models
W. X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, and et al. 2023 · 2023
Later among the works it cites.
Modal-adaptive knowledge-enhanced graph-based financial prediction from monetary policy conference calls with llm
Ouyang K, Liu Y, Li S, and et al. 2024 · 2024
Closest in time.
Learning to generate explainable stock predictions using self-reflective large language models
K. J. Koa, Y. Ma, R. Ng, and T.-S. Chua. 2024 · 2024
Closest in time.
Rethinking the bounds of llm reasoning: Are multi-agent discussions the key?
Q. Wang, Z. Wang, Y. Su, and et al. 2024 · 2024
Closest in time.
Finmem: A performance-enhanced llm trading agent with layered memory and character design
Yangyang Yu and et al. 2024 · 2024
Closest in time.
Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng, Yupeng Cao, Zhi Chen, Jordan W. Suchow, Rong Liu, Zhenyu Cui, Zhaozhuo Xu, Denghui Zhang, Koduvayur Subbalakshmi, Guojun Xiong, Yueru He, Jimin Huang, Dong Li, and Qianqian Xie. 2024 · 2024
Closest in time.
Revolutionizing finance with llms: An overview of applications and insights
H. Zhao, Z. Liu, Z. Wu, and et al. 2024 · 2024
Closest in time.
Large language models in finance (finllms)
Jean Lee, Nicholas Stevens, and Soyeon Caren Han. 2025 · 2025
Closest in time.
Alphaagent: Llm-driven alpha mining with regularized exploration to counteract alpha decay
Ziyi Tang, Zechuan Chen, Jiarui Yang, Jiayao Mai, Yongsen Zheng, Keze Wang, Jinrui Chen, and Liang Lin. 2025 · 2025
Closest in time.
Thinkpatterns-21k: A systematic study on the impact of thinking patterns in llms
Pengcheng Wen, Jiaming Ji, Chi-Min Chan, Juntao Dai, Donghai Hong, Yaodong Yang, Sirui Han, and Yike Guo. 2025 · 2025
Closest in time.