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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 胡星陽(Shing-Yang Hu) | |
dc.contributor.author | Tung-Li Chen | en |
dc.contributor.author | 陳同力 | zh_TW |
dc.date.accessioned | 2021-06-08T03:28:16Z | - |
dc.date.copyright | 2020-02-17 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-04-23 | |
dc.identifier.citation | 一、國內文獻
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21182 | - |
dc.description.abstract | 機器學習為人工智慧的範疇,其應用層面不斷擴大,也包括了金融投資領域。運用機器學習預測股價,有效協助投資決策,為眾多研究努力的方向;但是金融資訊具有小樣本、高雜訊、及非穩態時間序列的特性,使得以往研究的預測準確率不易提高;雖然將預測期間縮短可以增加預測的準確率,但是過短期的預測同時也降低了實務上運用的可行性。
本研究以台灣股市投資為例,結合領域知識,將金融資料做基本面、趨勢面、動能面、及籌碼面等多面向的特徵轉換,再運用不同的機器學習技術和模型來克服上述股價預測的困難。 本研究結果顯示,我們利用長短期記憶模型(Long short-term memory, LSTM)加上整合學習(Ensemble learning),預測上市櫃14檔權值個股未來一個月股價漲跌的準確率為83.23%。若以台積電(2330.TT)為例,預測未來一至三個月的股價漲跌的準確率高達84.57%。我們進一步利用此模型預測結果做2016/1/4~2018/12/28三年期間的回溯測試,所獲得的模擬投資報酬率為341.66%,遠勝過同期間台積電的77.92%漲幅。模型對於國巨(2327.TT)的模擬回測報酬率更高達2,888.36%,大幅超越該股票同期間364.74%的漲幅。 本研究未來發展方向為:1)廣度發展。將研究、測試、及應用範圍擴大至全台灣股市個股、海外股市、或是其他金融商品市場。2)深度發展。以本研究為基礎,利用強化學習(Reinforcement learning)實現人工智慧投資,取代經理人投資操作,並獲取更優異的報酬。 | zh_TW |
dc.description.abstract | One of the category of artificial intelligence, machine learning and its applications continue to expand, including the financial investment field. Using machine learning to predict stock prices in order to effectively assist investment decisions is the top goal of many research efforts; However, the financial data has the characteristics of small sample size, high noise, and nonstationary time series dependency, which make the prediction accuracy too low to be reliable. Although previous researches indicate that shortening the forecast horizon can increase the accuracy of predictions, it also reduces the feasibility of practical application.
This study takes the Taiwan equities as examples by combining feature transformations of the domain knowledge, together with fundamental, technical, momentum, trend, trade factors and a variety of machine learning techniques so as to overcome the difficulties that pertain to the stock price prediction. The outcome of our study is encouraging. Among the five candidate machine learning models we use, the designed LSTM generates highest predictive accuracy of 84.57% for predicting TSMC(2330.TT)’s price movement in the next 1~3 months. We also attain comparable results of 83.23% accuracy rate for the total 14 large cap names. Moreover, the 3-year back-testing simulations we conduct on TSMC(2330.TT), Yageo(2327.TT), Largan(3008.TT), and Fubon(2881.TT) shows astonishing returns of 341.66%, 2,888.36%, 371.13%, and 131.03%, compared with their same period returns of 77.92%, 364.74%, 13.41%, and 34.82%, respectively. There are two aspects for our future study. First, we will increase the coverage to all stocks in Taiwan equity market and expand the application to overseas and other financial markets if possible. Second, based on our finding, we can then use the model predictions as policies of reinforcement learning so that we can truly realize artificial intelligence investment. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T03:28:16Z (GMT). No. of bitstreams: 1 ntu-108-P05745003-1.pdf: 5229392 bytes, checksum: 28c58d300423cd54445800084206b621 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 II
摘要 III THESIS ABSTRACT IV 目錄 VI 圖目錄 VIII 表目錄 X 第一章 緒 論 1 第一節、 研究動機與背景 1 第二節、 研究目的 5 第三節、 研究方法 6 第二章 股票預測方法探討 9 第一節、 市場效率假說 9 第二節、 台灣股市市場效率驗證 11 第三節、 基本面分析預測 14 第四節、 股價技術分析 16 第五節、 行為財務分析 17 第六節、 資產定價模型 18 第七節、 其他分析方法 21 第八節、 機器學習預測文獻探討 25 第九節、 小結 28 第三章 機器學習概述 29 第一節、 機器學習發展背景 29 第二節、 機器學習原理與發展現況 31 第三節、 機器學習應用於財金領域概況 37 第四節、 機器學習應用於股市投資原理與限制 45 第四章 機器學習應用於台灣股市投資 47 第一節、 資料準備 47 第二節、 特徵工程及轉換 50 第三節、 模型選擇 52 第四節、 模型驗證、測試及比較 60 第五節、 模型回溯測試績效 69 第五章 結論 73 第一節、 研究結論 73 第二節、 未來發展建議 73 參考文獻 74 附錄.. 79 | |
dc.language.iso | zh-TW | |
dc.title | 運用機器學習預測台股股價走勢 | zh_TW |
dc.title | Predicting Stock Movement in Taiwan Equity Market Using Machine Learning Techniques | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 曹承礎(Seng-Cho Chou) | |
dc.contributor.oralexamcommittee | 李存修(Tsun-Siou Lee) | |
dc.subject.keyword | 機器學習,金融科技,台股股價預測,深度學習,人工智慧, | zh_TW |
dc.subject.keyword | machine learning,fintech,Taiwan equity,stock prediction,deep learning, | en |
dc.relation.page | 81 | |
dc.identifier.doi | 10.6342/NTU201900718 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2019-04-24 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 財務金融組 | zh_TW |
顯示於系所單位: | 財務金融組 |
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