教師資料查詢 | 類別: 會議論文 | 教師: 戴敏育 Min-Yuh Day (瀏覽個人網頁)

標題:Deep Learning for Financial Sentiment Analysis on Finance News Providers
學年105
學期1
發表日期2016/08/18
作品名稱Deep Learning for Financial Sentiment Analysis on Finance News Providers
作品名稱(其他語言)
著者Min-Yuh Day; Chia-Chou Lee
作品所屬單位
出版者
會議名稱2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016)
會議地點San Francisco, California, USA
摘要Investors have always been interested in stock price forecasting. Since the development of electronic media, hundreds pieces of financial news are released on different media every day. Numerous studies have attempted to examine whether the stock price forecasting through text mining technology and machine learning could lead to abnormal returns. However, few of them involved the discussion on whether using different media could affect forecasting results. Financial sentiment analysis is an important research area of financial technology (FinTech). This research focuses on investigating the influence of using different financial resources to investment and how to improve the accuracy of forecasting through deep learning. The experimental result shows various financial resources have significantly different effects to investors and their investments, while the accuracy of news categorization could be improved through deep learning.
關鍵字Deep Learning; Financial Sentiment Analysis; Financial Technology (FinTech); Finance News Providers; Stock Prediction
語言英文(美國)
收錄於
會議性質國際
校內研討會地點
研討會時間20160818~20160821
通訊作者Chia-Chou Lee
國別美國
公開徵稿
出版型式
出處Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016)
相關連結
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