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of 20
pro vyhledávání: '"Lopez-Lira, Alejandro"'
Autor:
Xie, Qianqian, Li, Dong, Xiao, Mengxi, Jiang, Zihao, Xiang, Ruoyu, Zhang, Xiao, Chen, Zhengyu, He, Yueru, Han, Weiguang, Yang, Yuzhe, Chen, Shunian, Zhang, Yifei, Shen, Lihang, Kim, Daniel, Liu, Zhiwei, Luo, Zheheng, Yu, Yangyang, Cao, Yupeng, Deng, Zhiyang, Yao, Zhiyuan, Li, Haohang, Feng, Duanyu, Dai, Yongfu, Somasundaram, VijayaSai, Lu, Peng, Zhao, Yilun, Long, Yitao, Xiong, Guojun, Smith, Kaleb, Yu, Honghai, Lai, Yanzhao, Peng, Min, Nie, Jianyun, Suchow, Jordan W., Liu, Xiao-Yang, Wang, Benyou, Lopez-Lira, Alejandro, Huang, Jimin, Ananiadou, Sophia
Large language models (LLMs) have advanced financial applications, yet they often lack sufficient financial knowledge and struggle with tasks involving multi-modal inputs like tables and time series data. To address these limitations, we introduce \t
Externí odkaz:
http://arxiv.org/abs/2408.11878
Autor:
Hu, Gang, Qin, Ke, Yuan, Chenhan, Peng, Min, Lopez-Lira, Alejandro, Wang, Benyou, Ananiadou, Sophia, Huang, Jimin, Xie, Qianqian
While the progression of Large Language Models (LLMs) has notably propelled financial analysis, their application has largely been confined to singular language realms, leaving untapped the potential of bilingual Chinese-English capacity. To bridge t
Externí odkaz:
http://arxiv.org/abs/2403.06249
Autor:
Xie, Qianqian, Han, Weiguang, Chen, Zhengyu, Xiang, Ruoyu, Zhang, Xiao, He, Yueru, Xiao, Mengxi, Li, Dong, Dai, Yongfu, Feng, Duanyu, Xu, Yijing, Kang, Haoqiang, Kuang, Ziyan, Yuan, Chenhan, Yang, Kailai, Luo, Zheheng, Zhang, Tianlin, Liu, Zhiwei, Xiong, Guojun, Deng, Zhiyang, Jiang, Yuechen, Yao, Zhiyuan, Li, Haohang, Yu, Yangyang, Hu, Gang, Huang, Jiajia, Liu, Xiao-Yang, Lopez-Lira, Alejandro, Wang, Benyou, Lai, Yanzhao, Wang, Hao, Peng, Min, Ananiadou, Sophia, Huang, Jimin
LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2402.12659
D\'olares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English
Autor:
Zhang, Xiao, Xiang, Ruoyu, Yuan, Chenhan, Feng, Duanyu, Han, Weiguang, Lopez-Lira, Alejandro, Liu, Xiao-Yang, Ananiadou, Sophia, Peng, Min, Huang, Jimin, Xie, Qianqian
Despite Spanish's pivotal role in the global finance industry, a pronounced gap exists in Spanish financial natural language processing (NLP) and application studies compared to English, especially in the era of large language models (LLMs). To bridg
Externí odkaz:
http://arxiv.org/abs/2402.07405
Autor:
Feng, Duanyu, Dai, Yongfu, Huang, Jimin, Zhang, Yifang, Xie, Qianqian, Han, Weiguang, Chen, Zhengyu, Lopez-Lira, Alejandro, Wang, Hao
In the financial industry, credit scoring is a fundamental element, shaping access to credit and determining the terms of loans for individuals and businesses alike. Traditional credit scoring methods, however, often grapple with challenges such as n
Externí odkaz:
http://arxiv.org/abs/2310.00566
Autor:
Xie, Qianqian, Han, Weiguang, Zhang, Xiao, Lai, Yanzhao, Peng, Min, Lopez-Lira, Alejandro, Huang, Jimin
Although large language models (LLMs) has shown great performance on natural language processing (NLP) in the financial domain, there are no publicly available financial tailtored LLMs, instruction tuning datasets, and evaluation benchmarks, which is
Externí odkaz:
http://arxiv.org/abs/2306.05443
Autor:
Lopez-Lira, Alejandro, Tang, Yuehua
We examine the potential of ChatGPT and other large language models in predicting stock market returns using news headlines. We use ChatGPT to assess whether each headline is good, bad, or neutral for firms' stock prices. We document a significantly
Externí odkaz:
http://arxiv.org/abs/2304.07619
Mining 29,000 accounting ratios for t-statistics over 2.0 leads to cross-sectional return predictability similar to the peer review process. For both methods, about 50% of predictability remains after the original sample periods. Predictors supported
Externí odkaz:
http://arxiv.org/abs/2212.10317
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