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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曹承礎(Seng-Cho Chou) | |
| dc.contributor.author | Po-Hsun Chen | en |
| dc.contributor.author | 陳柏勳 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:39:53Z | - |
| dc.date.available | 2021-08-04 | |
| dc.date.available | 2022-11-24T03:39:53Z | - |
| dc.date.copyright | 2021-08-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-26 | |
| dc.identifier.citation | [1] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. (2017). ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 6, 84–90. https://doi.org/10.1145/3065386 [2] Anginer, Deniz and Han, Xue and Yıldızhan, Çelim. (2018). Do Individual Investors Ignore Transaction Costs? http://dx.doi.org/10.2139/ssrn.2972845 [3] Chen, JH., Tsai, YC. (2020). Encoding candlesticks as images for pattern classification using convolutional neural networks. Financ Innov 6, 26. https://doi.org/10.1186/s40854-020-00187-0 [4] Gary S. Wagner, Bradley L. Matheny. (1993). Trading Applications of Japanese Candlestick Charting. ISBN: 978-0-471-58728-6 [5] Gregory L. Morris. (2006). Candlestick Charting Explained:Timeless Techniques for Trading Stocks and Futures: Timeless Techniques for Trading stocks and Sutures. ISBN: 978-0071461542 [6] Hércules A. do Prado, Edilson Ferneda, Luis C.R. Morais, Alfredo J.B. Luiz, Eduardo Matsura. (2013). On the Effectiveness of Candlestick Chart Analysis for the Brazilian Stock Market. Procedia Computer Science, Volume 22, Pages 1136-1145, ISSN 1877-0509. https://doi.org/10.1016/j.procs.2013.09.200. [7] Jigar Patel, Sahil Shah, Priyank Thakkar, K Kotecha. (2015). Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques. Expert Systems with Applications, Volume 42, Issue 1, Pages 259-268, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2014.07.040. [8] Kusuma, Rosdyana Ho, Trang-Thi Kao, Wei-Chun Ou, Yu Yen Hua, Kai-Lung. (2019). Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market. arXiv:1903.1258 [9] Lecun, Yann Bottou, Leon Bengio, Y. Haffner, Patrick. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE. 86. 2278 - 2324. http://dx.doi.org/10.1109/5.726791. [10] Raphael H. Heiberger. (2018). Predicting economic growth with stock networks. Physica A: Statistical Mechanics and its Applications, 489, 102-111. http://doi.org/10.1016/j.physa.2017.07.022 [11] Selvin, Sreelekshmy R, Vinayakumar Gopalakrishnan, E. A Menon, Vijay Kp, Soman. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1643-1647. https://doi.org/10.1109/ICACCI.2017.8126078. [12] Sharpe, W.F. (1964). CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK*. The Journal of Finance, 19: 425-442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x [13] S. Roy, I. Kiral-Kornek and S. Harrer, 'Deep Learning Enabled Automatic Abnormal EEG Identification,' 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 2018, pp. 2756-2759, https://doi.org/10.1109/EMBC.2018.8512756. [14] Thomas Fischer, Christopher Krauss. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, Volume 270, Issue 2, Pages 654-669, ISSN 0377-2217. https://doi.org/10.1016/j.ejor.2017.11.054. [15] Wang, Zhiguang Oates, Tim. (2015). Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence [16] Wing-Keung Wong, Meher Manzur Boon-Kiat Chew. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13:7, 543-551. https://doi.org/10.1080/0960310022000020906 [17] Xiong, R., Nichols, E. P., Shen, Y. (2015). Deep learning stock volatility with Google domestic trends. arXiv:1512.04916. [18] Market capitalization of listed domestic companies (current US$), https://data.worldbank.org/indicator/CM.MKT.LCAP.CD [19] sklearn.linear_model.LogisticRegression. scikit-learn. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html [20] yfinance · PyPI. PyPI.org. https://pypi.org/project/yfinance/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81270 | - |
| dc.description.abstract | 在2020 年初，由於Covid-19 的大流行，全球股票市場面臨了災難般地崩跌。這反映了即使處在看漲的「牛市」中，股票價格也隨時都可能崩盤。 在此研究中，我們希望透過避免未來的「價格修正」以獲取超額報酬，藉此改進「買入並持有」的投資策略。我們設計了「價格修正模型」，通過調整此模型的門檻值之組合，此標記演算法便可以精確地針對不同資產標記其「價格修正」。 接著，我們提出了一個2D GADF-CNN 模型以學習「價格修正」之間的共通規律。股票時間序列會先轉換為GAF 矩陣，再輸入到此模型中。在所有模型中，給定NASDAQ 指數最大的ETF－QQQ 之資料，CNN 模型在統計指標和回測報酬率上都表現最好。 最後，為了進一步測試我們模型的強健性，我們將其套用在TSM 和TSLA的資料上，分別為和QQQ 相似以及不相似的股票。2021 年1 至3 月的回測結果顯示，無論是直接對相似資料集進行遷移學習，亦或是針對相似與不相似的資料集微調「價格修正模型」的門檻值，我們的模型皆能習得有用的規律進而避開未來的下跌趨勢，最終得到超越「買入並持有」的投資報酬率。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:39:53Z (GMT). No. of bitstreams: 1 U0001-2607202111270900.pdf: 2320870 bytes, checksum: d593b55e4274298e8b17f09ab2891b5e (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員會審定書 ii 謝辭 iii 中文摘要 iv Abstract v Contents vi List of Figures viii List of Tables ix Chapter 1 Introduction 10 1.1 Background 10 1.2 Motivation 11 1.3 Objective 12 1.4 Thesis Organization 14 Chapter 2 Literature Review 15 2.1 Candlestick 15 2.2 Convolutional Neural Network 16 2.3 CNN for Stocks Prediction 18 2.4 Gramian Angular Summation Fields 19 Chapter 3 Methodology 22 3.1 Price Correction Model 22 3.2 Features 27 3.3 Deep Learning Architecture 32 3.4 Performance Evaluation 33 Chapter 4 Experiment and Results 35 4.1 Dataset 35 4.2 Baseline 35 4.3 Generic Model: QQQ (NASDAQ) 36 4.4 Similar Case: TSM 45 4.5 Disparity Case: TSLA 48 Chapter 5 Conclusions 53 5.1 Conclusions 53 5.2 Limitations and Future Works 55 Reference 57 | |
| dc.language.iso | en | |
| dc.subject | 格拉姆角場 | zh_TW |
| dc.subject | 價格修正 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 交易策略 | zh_TW |
| dc.subject | 趨勢預測 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | Price Correction | en |
| dc.subject | Convolutional Neural Network | en |
| dc.subject | Trend Prediction | en |
| dc.subject | Trading Strategy | en |
| dc.subject | Machine Learning | en |
| dc.subject | Gramian Angular Field | en |
| dc.title | 應用卷積神經網路於股票時間序列進行股市修正之預測 | zh_TW |
| dc.title | Applying Convolutional Neural Network to Stock Time Series to Predict Corrections of Stock Market | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳建錦(Hsin-Tsai Liu),盧信銘(Chih-Yang Tseng) | |
| dc.subject.keyword | 價格修正,機器學習,交易策略,趨勢預測,卷積神經網路,格拉姆角場, | zh_TW |
| dc.subject.keyword | Price Correction,Machine Learning,Trading Strategy,Trend Prediction,Convolutional Neural Network,Gramian Angular Field, | en |
| dc.relation.page | 59 | |
| dc.identifier.doi | 10.6342/NTU202101742 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-07-27 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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