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題名 Discovering finance keywords via continuous-space language models
作者 Tsai, Ming-Feng;Wang, Chuanju;Chien, Pochuan
蔡銘峰
貢獻者 資科系
日期 2016-10
上傳時間 15-十二月-2016 16:17:09 (UTC+8)
摘要 The growing amount of public financial data makes it increasingly important to learn how to discover valuable information for financial decision making. This article proposes an approach to discovering financial keywords from a large number of financial reports. In particular, we apply the continuous bag-of-words (CBOW) model, a well-known continuous-space language model, to the textual information in 10-K financial reports to discover new finance keywords. In order to capture word meanings to better locate financial terms, we also present a novel technique to incorporate syntactic information into the CBOW model. Experimental results on four prediction tasks using the discovered keywords demonstrate that our approach is effective for discovering predictability keywords for post-event volatility, stock volatility, abnormal trading volume, and excess return predictions. We also analyze the discovered keywords that attest to the ability of the proposed method to capture both syntactic and contextual information between words. This shows the success of this method when applied to the field of finance. © 2016, Association for Computing Machinery.
關聯 ACM Transactions on Management Information Systems, Volume 7, Issue 3, October 2016, 文章編號 7
資料類型 article
DOI https://doi.org/10.1145/2948072
dc.contributor 資科系
dc.creator (作者) Tsai, Ming-Feng;Wang, Chuanju;Chien, Pochuan
dc.creator (作者) 蔡銘峰zh_TW
dc.date (日期) 2016-10
dc.date.accessioned 15-十二月-2016 16:17:09 (UTC+8)-
dc.date.available 15-十二月-2016 16:17:09 (UTC+8)-
dc.date.issued (上傳時間) 15-十二月-2016 16:17:09 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/104951-
dc.description.abstract (摘要) The growing amount of public financial data makes it increasingly important to learn how to discover valuable information for financial decision making. This article proposes an approach to discovering financial keywords from a large number of financial reports. In particular, we apply the continuous bag-of-words (CBOW) model, a well-known continuous-space language model, to the textual information in 10-K financial reports to discover new finance keywords. In order to capture word meanings to better locate financial terms, we also present a novel technique to incorporate syntactic information into the CBOW model. Experimental results on four prediction tasks using the discovered keywords demonstrate that our approach is effective for discovering predictability keywords for post-event volatility, stock volatility, abnormal trading volume, and excess return predictions. We also analyze the discovered keywords that attest to the ability of the proposed method to capture both syntactic and contextual information between words. This shows the success of this method when applied to the field of finance. © 2016, Association for Computing Machinery.
dc.format.extent 375726 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) ACM Transactions on Management Information Systems, Volume 7, Issue 3, October 2016, 文章編號 7
dc.title (題名) Discovering finance keywords via continuous-space language models
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1145/2948072
dc.doi.uri (DOI) https://doi.org/10.1145/2948072