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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
| dc.contributor.author | Yu-Heng Houng | en |
| dc.contributor.author | 洪御恆 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:36:09Z | - |
| dc.date.copyright | 2019-07-31 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-07-26 | |
| dc.identifier.citation | Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3)1–27. https://github.com/ldkrsi/jieba-zh_TW Radinsky, K., Davidovich, S., & Markovitch, S. (2012, April). Learning causality for news events prediction. In Proceedings of the 21st International Conference on World Wide Web, 909–918. Association for Computing Machinery. Le, Q., & Mikolov, T. (2014, January). Distributed representations of sentences and documents. In Proceedings of the International Conference on Machine Learning, 1188–1196. Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 10(3), 61–74. Qian, B., & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence, 26(1), 25–33. Kogan, S., Levin, D., Routledge, B. R., Sagi, J. S., & Smith, N. A. (2009, May). Predicting risk from financial reports with regression. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, 272–280. Association for Computational Linguistics. Ding, X., Zhang, Y., Liu, T., & Duan, J. (2015, June). Deep learning for event-driven stock prediction. In Proceedings of Twenty-Fourth International Joint Conference on Artificial Intelligence, 2327–2333. Ding, X., Zhang, Y., Liu, T., & Duan, J. (2014). Using structured events to predict stock price movement: An empirical investigation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1415–1425. Peng, Y., & Jiang, H. (2015). Leverage financial news to predict stock price movements using word embeddings and deep neural networks. arXiv preprint arXiv:1506.07220. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21506 | - |
| dc.description.abstract | 本論文主要討論以新聞標題作為依據預測股價的漲跌方向，對於以消息面來做為依據的投資策略，新聞是很重要的一個消息來源，於是我們嘗試利用自然語言處理領域中有名的Doc2vec將新聞標題以向量的方式解讀，一個以神經網路與機率作為基本架構的模型來表示新聞標題，再使用經典機器學習模型預測特定股價的漲跌，預測準確率最高可達70%，以期望用於輔助投資策略的決策。 | zh_TW |
| dc.description.abstract | This thesis mainly discusses how to use news headlines as features to predict stock price directions. It is well-known that some investors believe in news analysis strategy, which is highly depending on the news. Thus we will use the popular method Doc2vec, which uses vectors to represent words based on neural networks and probability, to represent each headline as a vector. Then we use the classical machine learning model to predict individual stock price directions. Our method best perform 70% accuracy for stock price directions prediction and expect to help investors making
the right strategy. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:36:09Z (GMT). No. of bitstreams: 1 ntu-108-R06922122-1.pdf: 1213243 bytes, checksum: b3c6beb1e9ff7e269b555b5b043064a2 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 目錄
致謝 i 摘要 ii Abstract iii 目錄 iv 圖目錄 v 表目錄 vi 第一章 緒論 1 1.1 實驗動機．．．．．．．．．．．．．．．．．．．．．．．．．．．1 1.2 論文架構．．．．．．．．．．．．．．．．．．．．．．．．．．．1 第二章 背景 5 2.1 文獻回顧．．．．．．．．．．．．．．．．．．．．．．．．．．5 2.2 使用模型．．．．．．．．．．．．．．．．．．．．．．．．．．6 2.2.1 Logistic Regression．．．．．．．．．．．．．．．．．．．．6 2.2.2 SVM．．．．．．．．．．．．．．．．．．．．．．．．．．6 2.2.3 AdaBoost．．．．．．．．．．．．．．．．．．．．．．．．7 2.3 Document to Vector．．．．．．．．．．．．．．．．．．．．．．8 第三章 實驗 11 3.1 資料與預先處理．．．．．．．．．．．．．．．．．．．．．．．．11 3.1.1 特徵處理．．．．．．．．．．．．．．．．．．．．．．．．11 3.1.2 標籤處理．．．．．．．．．．．．．．．．．．．．．．．．11 3.2 結果．．．．．．．．．．．．．．．．．．．．．．．．．．12 第四章 結論 17 文獻回顧 19 | |
| dc.language.iso | zh-TW | |
| dc.title | 利用機率模型與機器學習，向量表示詞語並預測股價方向 | zh_TW |
| dc.title | Using Probability Models and Machine Learning to Represent Words in the Prediction of Stock Price Directions | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張經略(Ching-Lueh Chang),金國興(Guo-Xing Jin),陸裕豪(U-Hou Lok) | |
| dc.subject.keyword | 漲跌預測,股價,自然語言,機器學習,新聞, | zh_TW |
| dc.subject.keyword | Prediction in the Price Directions,Stock Price,Nature Language,Machine Learning,News, | en |
| dc.relation.page | 20 | |
| dc.identifier.doi | 10.6342/NTU201901597 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-07-26 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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| ntu-108-1.pdf 未授權公開取用 | 1.18 MB | Adobe PDF |
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