Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Yang, Hongyang"'
As financial markets grow increasingly complex, there is a rising need for automated tools that can effectively assist human analysts in equity research, particularly within sell-side research. While Generative AI (GenAI) has attracted significant at
Externí odkaz:
http://arxiv.org/abs/2411.08804
In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully
Externí odkaz:
http://arxiv.org/abs/2411.04788
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