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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69370Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 呂育道(Yuh-Dauh Lyuu) | |
| dc.contributor.author | Yi-Ke Huang | en |
| dc.contributor.author | 黃奕軻 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:13:59Z | - |
| dc.date.available | 2018-07-19 | |
| dc.date.copyright | 2018-07-19 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-11 | |
| dc.identifier.citation | Borovykh, A., Bohte, S., & Oosterlee, C. W. (2018). Conditional time series forecasting with convolutional neural networks. Retrieved June 15, 2018, from https://arxiv.org/abs/1703.04691v4
Brock, W., Lakonishok, J., & Lebaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 47(5), 1731–1764. doi: 10.1111/j.1540-6261.1992.tb04681.x Chavarnakul, T., & Enke, D. (2009). A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing, 72(16–18), 3517–3528. doi: 10.1016/j.neucom.2008.11.030 Chordia, T., Roll, R., & Subrahmanyam, A. (2002). Order imbalance, liquidity, and market returns. Journal of Financial Economics, 65(1), 111–130. doi: 10.1016/s0304-405x(02)00136-8 Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249–268. doi: 10.1016/j.jfineco.2007.03.005 Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236. doi: 10.1088/1469-7688/1/2/304 Cont, R., Kukanov, A., & Stoikov, S. (2013). The price impact of order book events. Journal of Financial Econometrics, 12(1), 47–88. doi: 10.1093/jjfinec/nbt003 Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4), 303–314. doi: 10.1007/bf02551274 Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383–417. doi: 10.2307/2325486 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge, MA: MIT Press. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of the 25th international conference on neural information processing systems (Vol. 1, pp. 1097–1105). Lake Tahoe, NV: Curran Associates. Liu, R., & Gillies, D. F. (2016). Overfitting in linear feature extraction for classification of high-dimensional image data. Pattern Recognition, 53(C), 73–86. doi: 10.1016/j.patcog.2015.11.015 Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York: New York Institute of Finance. O’Hara, M. (2011). Market microstructure theory. Hoboken, NJ: Wiley-Blackwell. Ozturk, S. R., Van der Wel, M., & Van Dijk, D. (2017). Intraday price discovery in fragmented markets. Journal of Financial Markets, 32(C), 28–48. doi: 10.1016/j.finmar.2016.10.001 Putra, E. F., & Kosala, R. (2011, 12). Application of artificial neural networks to predict intraday trading signals. In Proceedings of the 10th wseas international conference one-activities (e-activities ’11) (pp. 174–179). Jakarta, Island of Java. Schulmeister, S. (2009). Profitability of technical stock trading: Has it moved from daily to intraday data? Review of Financial Economics, 18(4), 190–201. doi: https://doi.org/10.1016/j.rfe.2008.10.001 Sullivan, R., Timmermann, A., & White, H. (1999). Data-snooping, technical trading rule performance, and the bootstrap. Journal of Finance, 54(5), 1647–1691. doi: 10.1111/0022-1082.00163 台灣證券交易所 (民 105 年 1月 15 日)。集中市場交易制度介紹。取自 http://www.twse.com.tw/zh/page/trading/introduce.html。 台灣指數股份有限公司 (民 105年 1 月 24 日)。發行量加權股價指數編製要點。取自 https://www.taiwanindex.com.tw/files/indexfile/2.pdf。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69370 | - |
| dc.description.abstract | 股市指數通常由多家上市公司的股票組合而成。然而其價格決定於複雜的交易過程，並且資產間時常具有非線性的相依性。因此為了預測指數未來的短期走勢，我們考慮以卷積神經網路處理非線性多維時間序列。我們將模型用於預測指數的上漲或下跌，並實證模型的預測性。市場高頻資料 (每5秒) 包含了 2015 年至 2017 年日內的臺灣加權指數與個股價格。實驗結果顯示，模型使用在日內交易預測能得到良好的準確率與績效。 | zh_TW |
| dc.description.abstract | A stock index consists of a board selection of stocks. Stylized facts show non-linear correlations exist between stock prices. Therefore, predicting the direction of an index involves modeling and analyzing sophisticated multi-dimensional time series. We employ the convolutional neural network to forecast the intra-day price movement of the Taiwan Stock Exchange Weighted Index (TAIEX). Furthermore, we verify the prediction performance of our model on the high-frequency data (5 sec) of the TAIEX and its constituent share prices from 2015 to 2017. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:13:59Z (GMT). No. of bitstreams: 1 ntu-107-R05922111-1.pdf: 1501490 bytes, checksum: 3e7bf2bfc8927179f72569b62ef8f791 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 iii 英文摘要 v 圖目錄 vii 表目錄 ix 第一章 研究介紹與文獻回顧 1 第二章 預測模型 5 第一節 卷積神經網路 5 第二節 估計方法 8 第三章 實證研究 11 第一節 資料來源以及特性 11 第二節 趨勢預測 12 第三節 模型預測結果 15 第四章 結論 21 參考文獻 23 | |
| dc.language.iso | zh-TW | |
| dc.subject | 台灣證券市場 | zh_TW |
| dc.subject | 日內資料 | zh_TW |
| dc.subject | 資料探勘 | zh_TW |
| dc.subject | 神經網絡 | zh_TW |
| dc.subject | 股票預測 | zh_TW |
| dc.subject | Taiwan Stock Market | en |
| dc.subject | Intraday trading | en |
| dc.subject | Data mining | en |
| dc.subject | Neural network | en |
| dc.subject | Stock prediction | en |
| dc.title | 上市個股預測台灣加權指數高頻趨勢 | zh_TW |
| dc.title | Prediction of Short-Term Trends of TAIEX via Its High-Frequency Constituent Share Prices | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 金國興(Guo-Xing JIN),張經略(Jing-Lue Zhang),鄧惠文(Hui-Wen Deng),蔡芸琤(Yun-Cheng Cai) | |
| dc.subject.keyword | 台灣證券市場,日內資料,資料探勘,神經網絡,股票預測, | zh_TW |
| dc.subject.keyword | Taiwan Stock Market,Intraday trading,Data mining,Neural network,Stock prediction, | en |
| dc.relation.page | 25 | |
| dc.identifier.doi | 10.6342/NTU201801430 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-07-11 | |
| 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 |
| Appears in Collections: | 資訊工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-107-1.pdf Restricted Access | 1.47 MB | Adobe PDF |
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