英文论文
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文献类型
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Article
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题名
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Deep learning, textual sentiment, and financial market
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作者
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Jiang, Fuwei; Liu, Yumin; Meng, Lingchao; Zhang, Huajing
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作者单位
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[Jiang, Fuwei; Liu, Yumin] Cent Univ Finance & Econ, Sch Finance, Beijing, Peoples R China. [Jiang, Fuwei] Xiamen Univ, Sch Econ, Xiamen, Peoples R China. [Jiang, Fuwei] Xiamen Univ, Wang Yanan Inst Studies Econ WISE, Xiamen, Peoples R China. [Meng, Lingchao] Univ Int Business & Econ, China Sch Banking & Finance, Beijing, Peoples R China. [Zhang, Huajing] Shandong Technol & Business Univ, Sch Finance, Yantai, Peoples R China.
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通讯作者地址
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Cent Univ Finance & Econ, Sch Finance, Beijing, Peoples R China.; Xiamen Univ, Sch Econ, Xiamen, Peoples R China.; Xiamen Univ, Wang Yanan Inst Studies Econ WISE, Xiamen, Peoples R China.; Shandong Technol & Business Univ, Sch Finance, Yantai, Peoples R China.
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Email
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jfuwei@gmail.com; yuminliu123@163.com; lingchao_meng@pku.edu.cn; hjingzhang@126.com
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ResearchID
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ORCID
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期刊名称
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INFORMATION TECHNOLOGY & MANAGEMENT
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出版社
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SPRINGER
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ISSN
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1385-951X
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出版信息
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2025-12, 26 (4):441-465.
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JCR
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影响因子
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ISBN
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基金
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National Social Science Fund of China [22ZD063]; National Natural Science Foundation of China [72072193,71872195, 72342019]; Program for Innovation Research in CUFE
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会议名称
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会议地点
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会议开始日期
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会议结束日期
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关键词
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Textual sentiment; Deep learning; Sentiment dictionary; Asset pricing
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摘要
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In this paper, we apply the BERT model, a cut-edging deep learning model, to construct a novel textual sentiment index in the Chinese stock market. By introducing the stock market returns as sentiment labels, our BERT model effectively extracts textual sentiment-related information useful for asset pricing. We find that the BERT-based sentiment has much greater predictive power for stock market returns than the traditional dictionary method as well as the Baker-Wurgler investor sentiment index both in and out of sample. The BERT-based sentiment shows strong predictive power during economic downturns and can significantly predict future macroeconomic conditions. Overall, our BERT model offers a better measure of textual investor sentiment, highlighting the potentially significant value of deep learning, AI, and FinTech in financial market.
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一级学科
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Information Science & Library Science; Management
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WOS入藏号
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WOS:001589287600001
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EI收录号
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DOI
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10.1007/s10799-024-00428-z
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ESI
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收录于
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SSCI
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