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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希,陳昇瑋 | |
| dc.contributor.author | Tsun-Hsien Tang | en |
| dc.contributor.author | 湯忠憲 | zh_TW |
| dc.date.accessioned | 2021-06-17T08:46:43Z | - |
| dc.date.available | 2024-08-13 | |
| dc.date.copyright | 2019-08-13 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-05 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74630 | - |
| dc.description.abstract | 大量網路新聞分析有利於股市趨勢的預測，先前的研究往往只考慮有提及公司或股票名稱的新聞作為分析的對象，但是並非每間公司每天都會被新聞報導。如果只考慮具有上述特徵的新聞，容易造成資料的稀疏。同時通過產業相關新聞的分析，做為財務決定之參考較符合真實人類的行為。因此，本研究將使用無明確提及公司名稱之新聞，即隱性相關新聞，來豐富預測模型訓練之資料來源。為能充分利用隱性相關新聞，本研究提出基於新聞篩選機制的深度神經網絡框架，該框架結合文件表徵學習和偕同過濾來捕捉新聞和股票之間的關係。本研究實驗於真實世界資料，並且與最先進的模型比較，實驗結果顯示有效性以及準確率都有提升。 | zh_TW |
| dc.description.abstract | Analyzing online news content benefits stock price trend prediction. Previous studies on news-oriented stock market prediction focus mainly on news with explicit stock mentions for a specific prediction target, and may suffer from data sparsity. As taking into consideration other related news - e.g., sector-related news - is a crucial part of real-world decision-making, we explore the use of news without explicit target mentions to enrich the information for the prediction model. We first conduct an empirical analysis on real-world news collected from a well-known financial website. To leverage implicit-related information, we devise a neural network framework that jointly learns with a news selection mechanism to extract implicit news from the chaotic daily news pool. The news distilling network (NDN), our proposed model, takes advantage of neural representation learning and collaborative filtering to capture the relationship between stocks and news. With NDN, we learn latent stock and news representations to facilitate similarity measurements, and apply a gating mechanism to prevent noisy news representations from flowing to a higher level encoding stage, which encodes each day's selected news representation. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework and show improvements over state-of-the-art techniques. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T08:46:43Z (GMT). No. of bitstreams: 1 ntu-108-R06946003-1.pdf: 2022170 bytes, checksum: 5a716c3b0fbf59257e00a396b17da87a (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 誌謝 ii
中文摘要 iii Abstract iv Chapter 1 Introduction 5 Chapter 2 Related Work 8 2.1 Lexicon-based method 8 2.2 Deep learning based method 10 Chapter 3 Empirical Analysis 12 3.1 Influence of Implicitly Related News 12 3.2 Scarcity of Explicit Target Mentions 13 Chapter 4 Methodology 17 4.1 Problem Formulation 17 4.2 News Distilling Networks 18 4.2.1 Collaborative Filtering Module 19 4.2.2 News-level Encoding 20 4.2.3 News Article Selection 21 4.2.4 Dense Temporal Encoding 22 4.2.5 Prediction and Loss Function 22 4.2.6 Training and Inference Procedures 23 Chapter 5 Experiments 25 5.1 Dataset 25 5.2 Experimental Setup 27 5.3 Model Evaluation 29 5.4 Effect of Collaborative Filtering 30 5.4.1 Ablation Study on CFM 30 5.4.2 Effect of Negative Sampling 31 5.4.3 Visualization of Learned Embeddings 32 5.5 Zero-seed Inference 33 5.6 News Selection Case Study 35 5.7 Market Simulation 36 Chapter 6 Conclusions 39 Reference 40 | |
| dc.language.iso | en | |
| dc.subject | 股票趨勢預測 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 協同過濾 | zh_TW |
| dc.subject | Stock Movement Prediction | en |
| dc.subject | Deep learning | en |
| dc.subject | Collaborative Filtering | en |
| dc.title | 基於密集隱性相關新聞序列之股票趨勢預測 | zh_TW |
| dc.title | News-Oriented Stock Movement Prediction on Dense Temporal Sequence Using Implicit News | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張元顯,蔡銘峰 | |
| dc.subject.keyword | 股票趨勢預測,深度學習,協同過濾, | zh_TW |
| dc.subject.keyword | Stock Movement Prediction,Deep learning,Collaborative Filtering, | en |
| dc.relation.page | 44 | |
| dc.identifier.doi | 10.6342/NTU201902595 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-06 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資料科學學位學程 | zh_TW |
| 顯示於系所單位： | 資料科學學位學程 | |
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