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題名 新聞輿情分析在台灣股票市場之應用: 文字轉向量與動能策略
Application of News Analysis in Taiwan Stock Market: Word to Vector and Momentum Strategy作者 李昱穎 貢獻者 林士貴<br>王釧茹
Lin, Shih-Kuei<br>Wang, Chuan-Ju
李昱穎關鍵詞 文字探勘
程式交易
文字轉向量
動能策略
Text mining
Algorithm trading
Word2Vec
Momentum strategy日期 2019 上傳時間 7-八月-2019 16:11:02 (UTC+8) 摘要 機器學習與人工智慧的技術能夠應於金融交易之決策，並獲得創新的交易策略，本研究則希望發掘文字探勘應用於金融交易之決策領域之可能。文字探勘將非結構化資料轉化為結構化資料以利使用者進行後續分析，具有將文字間的隱藏訊息轉化為數據的能力，本研究希望藉由採用新聞文本之分析來建構台灣股票市場之程式交易策略。在系統設計上，我們先運用文字轉向量方法將個股實施分類，再運用量化及資料分析方法將切割後的文本轉換為情緒溫度，最後再依據各則新聞的輿論溫度加總在動能策略之上決定做多或放空。為檢測該系統的可行性，我們選用台灣加權股價指數中佔比前十大之上市股票自2010年01月04日至2018年07月27日的日資料以及同段時間的鉅亨網台股新聞資料予以回測。實證發現，套用本研究所量化之新聞溫度計的策略會有比起未套用新聞溫度計的策略有較佳之報酬與風報比。
The technique of machine learning and artificial intelligence applies in the area of financial decisions, which helps us build innovative trading strategies. This research aims to discover ways how text mining tempts to gain application in trading. Text mining transforms the unstructured data into structured ones, which gains the ability to turn word into analyzable data. This research tries to analyze stock news to construct algorithm trading strategies in Taiwan stock market. In terms of system design, we divide the stocks into several groups by word2vec, then by text mining we can gain access to the mood of stock market. Based on it, constructing a beta strategy by determining whether the stock is bear or bull will be the work of stock thermometer. To test the feasibility of the research, the research back-tested the ten biggest stocks of the Taiwan stock market (2010/1/4-2018/7/27) and the stock news of Anue. Our findings illustrate that strategies using the news data tend to perform better and have higher profit than those who didn’t.參考文獻 [1] 李春安, 羅進水, & 蘇永裕. (2006). 動能策略報酬, 投資人情緒與景氣循環之研究. 財務金融學刊, 14(2), 73-109.\n[2] Alostad, H., & Davulcu, H. (2015). Directional prediction of stock prices using breaking news on Twitter. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 523-530). IEEE.\n[3] Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.\n[4] Chan, K., Hameed, A., & Tong, W. (2000). Profitability of momentum strategies in the international equity markets. Journal of Financial and Quantitative analysis, 35(2), 153-172.\n[5] Gidofalvi, G., & Elkan, C. (2001). Using news articles to predict stock price movements. Research Report. Department of Computer Science and Engineering, University of California, San Diego.\n[6] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.\n[7] Mao, Y., Wei, W., & Wang, B. (2013, August). Twitter volume spikes: analysis and application in stock trading. In Proceedings of the 7th Workshop on Social Network Mining and Analysis (p. 4).\n\n[8] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.\n[9] Schumaker, R., & Chen, H. (2006). Textual analysis of stock market prediction using financial news articles. Americas Conference on Information Systems (AMCIS) 2006 Proceedings, 185.\n[10] Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing & Management, 45(5), 571-583.\n[11] 網際網路資料，Word2vec是如何得到詞向量的？民106年12月25日，取自：https://www.zhihu.com/question/44832436\n[12] 網際網路資料，詞嵌入(Word embeddings)的基本概念。民107年8月20日。取自：https://www.kesci.com/home/project/5b7a359e31902f000f55152f\n[13] 網際網路資料，Jieba分詞的原理。民106年12月03日，取自：https://www.itread01.com/content/1512314575.html 描述 碩士
國立政治大學
金融學系
106352019資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106352019 資料類型 thesis dc.contributor.advisor 林士貴<br>王釧茹 zh_TW dc.contributor.advisor Lin, Shih-Kuei<br>Wang, Chuan-Ju en_US dc.contributor.author (作者) 李昱穎 zh_TW dc.creator (作者) 李昱穎 zh_TW dc.date (日期) 2019 en_US dc.date.accessioned 7-八月-2019 16:11:02 (UTC+8) - dc.date.available 7-八月-2019 16:11:02 (UTC+8) - dc.date.issued (上傳時間) 7-八月-2019 16:11:02 (UTC+8) - dc.identifier (其他 識別碼) G0106352019 en_US dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=143193 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 106352019 zh_TW dc.description.abstract (摘要) 機器學習與人工智慧的技術能夠應於金融交易之決策，並獲得創新的交易策略，本研究則希望發掘文字探勘應用於金融交易之決策領域之可能。文字探勘將非結構化資料轉化為結構化資料以利使用者進行後續分析，具有將文字間的隱藏訊息轉化為數據的能力，本研究希望藉由採用新聞文本之分析來建構台灣股票市場之程式交易策略。在系統設計上，我們先運用文字轉向量方法將個股實施分類，再運用量化及資料分析方法將切割後的文本轉換為情緒溫度，最後再依據各則新聞的輿論溫度加總在動能策略之上決定做多或放空。為檢測該系統的可行性，我們選用台灣加權股價指數中佔比前十大之上市股票自2010年01月04日至2018年07月27日的日資料以及同段時間的鉅亨網台股新聞資料予以回測。實證發現，套用本研究所量化之新聞溫度計的策略會有比起未套用新聞溫度計的策略有較佳之報酬與風報比。 zh_TW dc.description.abstract (摘要) The technique of machine learning and artificial intelligence applies in the area of financial decisions, which helps us build innovative trading strategies. This research aims to discover ways how text mining tempts to gain application in trading. Text mining transforms the unstructured data into structured ones, which gains the ability to turn word into analyzable data. This research tries to analyze stock news to construct algorithm trading strategies in Taiwan stock market. In terms of system design, we divide the stocks into several groups by word2vec, then by text mining we can gain access to the mood of stock market. Based on it, constructing a beta strategy by determining whether the stock is bear or bull will be the work of stock thermometer. To test the feasibility of the research, the research back-tested the ten biggest stocks of the Taiwan stock market (2010/1/4-2018/7/27) and the stock news of Anue. Our findings illustrate that strategies using the news data tend to perform better and have higher profit than those who didn’t. en_US dc.description.tableofcontents 第一章 緒論 1\n第一節 研究動機 1\n第二節 研究目的 2\n第三節 研究架構 3\n第二章 文獻探討 4\n第一節 文字探勘 4\n第二節 文字轉向量 5\n第三節 動能策略模型 5\n第三章 研究方法 6\n第一節 文字轉向量 6\n第二節 中文斷詞方法 10\n第三節 新聞量化方法 13\n第四節 績效評估 15\n第四章 實證分析 17\n第一節 實證資料與研究期間 17\n第二節 文字轉向量分類結果 21\n第三節 動能策略比較 26\n第四節 溫度計策略比較 31\n第五章 建議與結論 37\n第一節 結論 37\n第二節 未來研究建議 38\n參考文獻 39 zh_TW dc.format.extent 3357695 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106352019 en_US dc.subject (關鍵詞) 文字探勘 zh_TW dc.subject (關鍵詞) 程式交易 zh_TW dc.subject (關鍵詞) 文字轉向量 zh_TW dc.subject (關鍵詞) 動能策略 zh_TW dc.subject (關鍵詞) Text mining en_US dc.subject (關鍵詞) Algorithm trading en_US dc.subject (關鍵詞) Word2Vec en_US dc.subject (關鍵詞) Momentum strategy en_US dc.title (題名) 新聞輿情分析在台灣股票市場之應用: 文字轉向量與動能策略 zh_TW dc.title (題名) Application of News Analysis in Taiwan Stock Market: Word to Vector and Momentum Strategy en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] 李春安, 羅進水, & 蘇永裕. (2006). 動能策略報酬, 投資人情緒與景氣循環之研究. 財務金融學刊, 14(2), 73-109.\n[2] Alostad, H., & Davulcu, H. (2015). Directional prediction of stock prices using breaking news on Twitter. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 523-530). IEEE.\n[3] Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.\n[4] Chan, K., Hameed, A., & Tong, W. (2000). Profitability of momentum strategies in the international equity markets. Journal of Financial and Quantitative analysis, 35(2), 153-172.\n[5] Gidofalvi, G., & Elkan, C. (2001). Using news articles to predict stock price movements. Research Report. Department of Computer Science and Engineering, University of California, San Diego.\n[6] Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.\n[7] Mao, Y., Wei, W., & Wang, B. (2013, August). Twitter volume spikes: analysis and application in stock trading. In Proceedings of the 7th Workshop on Social Network Mining and Analysis (p. 4).\n\n[8] Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.\n[9] Schumaker, R., & Chen, H. (2006). Textual analysis of stock market prediction using financial news articles. Americas Conference on Information Systems (AMCIS) 2006 Proceedings, 185.\n[10] Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing & Management, 45(5), 571-583.\n[11] 網際網路資料，Word2vec是如何得到詞向量的？民106年12月25日，取自：https://www.zhihu.com/question/44832436\n[12] 網際網路資料，詞嵌入(Word embeddings)的基本概念。民107年8月20日。取自：https://www.kesci.com/home/project/5b7a359e31902f000f55152f\n[13] 網際網路資料，Jieba分詞的原理。民106年12月03日，取自：https://www.itread01.com/content/1512314575.html zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900188 en_US
