Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Yang, Hongyang"'
Autor:
Yang, Hongyang, Zhang, Boyu, Wang, Neng, Guo, Cheng, Zhang, Xiaoli, Lin, Likun, Wang, Junlin, Zhou, Tianyu, Guan, Mao, Zhang, Runjia, Wang, Christina Dan
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 communit
Externí odkaz:
http://arxiv.org/abs/2405.14767
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 of these models with financial datasets presents challenges, notably in
Externí odkaz:
http://arxiv.org/abs/2310.04793
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 training datasets, which hampers their generalization capabilities and
Externí odkaz:
http://arxiv.org/abs/2310.04027
Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. However, existing LLMs often fall short in the financial field, which is
Externí odkaz:
http://arxiv.org/abs/2307.10485
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 capabilities of large language models (LLMs) in financial natural langua
Externí odkaz:
http://arxiv.org/abs/2306.12659
Large language models (LLMs) have shown the potential of revolutionizing natural language processing tasks in diverse domains, sparking great interest in finance. Accessing high-quality financial data is the first challenge for financial LLMs (FinLLM
Externí odkaz:
http://arxiv.org/abs/2306.06031
Autor:
Liu, Xiao-Yang, Xia, Ziyi, Yang, Hongyang, Gao, Jiechao, Zha, Daochen, Zhu, Ming, Wang, Christina Dan, Wang, Zhaoran, Guo, Jian
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is dif
Externí odkaz:
http://arxiv.org/abs/2304.13174
Autor:
Liu, Xiao-Yang, Xia, Ziyi, Rui, Jingyang, Gao, Jiechao, Yang, Hongyang, Zhu, Ming, Wang, Christina Dan, Wang, Zhaoran, Guo, Jian
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-t
Externí odkaz:
http://arxiv.org/abs/2211.03107
Autor:
Liu, Xiao-Yang, Rui, Jingyang, Gao, Jiechao, Yang, Liuqing, Yang, Hongyang, Wang, Zhaoran, Wang, Christina Dan, Guo, Jian
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 markets, raw historical financial data often involve large noise and may
Externí odkaz:
http://arxiv.org/abs/2112.06753
Publikováno v:
ACM International Conference on AI in Finance, 2021
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 obtain an agent that automatically positions to win in the market, namel
Externí odkaz:
http://arxiv.org/abs/2111.09395