請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88177
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 呂育道 | zh_TW |
dc.contributor.advisor | Yuh-Dauh Lyuu | en |
dc.contributor.author | 張書維 | zh_TW |
dc.contributor.author | Shu-Wei Chang | en |
dc.date.accessioned | 2023-08-08T16:39:00Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-08 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-07-20 | - |
dc.identifier.citation | [1] Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41–66.
[2] Fama, E. F. (1965). The behavior of stock-market prices. Journal of Business, 38(1), 34–105. [3] Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59–82. [4] Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259–268. [5] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541–551. [6] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. [7] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. [8] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680. [9] Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. [10] Roondiwala, M., Patel, H., & Varma, S. (2017). Predicting stock prices using LSTM. International Journal of Science and Research, 6(4), 1754–1756. [11] Eun, C. S., & Resnick, B. G. (1984). Estimating the correlation structure of international share prices. Journal of Finance, 39(5), 1311–1324. [12] Shynkevich, Y., McGinnity, T. M., Coleman, S. A., Belatreche, A., & Li, Y. (2017). Forecasting price movements using technical indicators: Investigating the impact of varying input window length. Neurocomputing, 264, 71–88. [13] Box, G. E., & Pierce, D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American Statistical Association, 65(332), 1509–1526. [14] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327. [15] Pai, P.-F., & Lin, C.-S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497–505. [16] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. [17] Hung, C.-C., Chen, Y.-J., Guo, S. J., & Hsu, F.-C. (2020). Predicting the price movement from candlestick charts: A CNN-based approach. International Journal of Ad Hoc and Ubiquitous Computing, 34(2), 111–120. [18] Staffini, A. (2022). Stock price forecasting by a deep convolutional generative adversarial network. Frontiers in Artificial Intelligence, 5, 837596. [19] Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock market prediction on high-frequency data using generative adversarial nets. Mathematical Problems in Engineering, 2018, 1–11. [20] Zhang, K., Zhong, G., Dong, J., Wang, S., & Wang, Y. (2019). Stock market prediction based on generative adversarial network. Procedia Computer Science, 147, 400406. [21] Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein generative adversarial networks. In Proceedings of the 34th International Conference on Machine Learning, 70, 214–223. [22] Selvin, S., Vinayakumar, R., Gopalakrishnan, E., Menon, V. K., & Soman, K. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. 2017 International Conference on Advances in Computing, Communications and Informatics, 1643–1647. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88177 | - |
dc.description.abstract | 近年來深度學習以強大的數據處理能力被廣泛地運用在金融市場中,如股市預測、高頻交易、投資組合優化和交易策略等。其中具有時間序列資料的股票市場因為高雜訊、非穩態、非線性的特徵成為深度學習熱門的研究領域之一。
本論文使用生成對抗網路架構GAN和WGAN進行股票預測,其中使用雙向長短記憶網路(Bi-LSTM)作為生成模型,用以輸入歷史資料以生成未來一天的股票資料,並使用卷積神經網路(CNN)作為判別器,用以判斷生成股票資料與真實股票資料之間的相似性。 本論文以台股作為標的使用生成對抗網路訓練Bi-LSTM模型,研究生成對抗網路在處理數據方面的能力,再以GAN和WGAN比較LSTM模型,研究結果顯示使用生成對抗網路架構的模型可以提升股價的預測表現,但GAN和WGAN在預測股價的表現不分軒輊。 | zh_TW |
dc.description.abstract | In recent years, deep learning has been widely used in financial market due to its powerful data analysis capabilities. It has been used in various applications such as stock market prediction, high-frequency trading, portfolio optimization, and trading strategies. Stock price, which involves time series data and exhibits high noise, non-stationarity, and non-linearity, has become a popular research area for deep learning.
In this thesis, it proposes Generative Adversarial Network (GAN) architecture for stock prediction. Using Bi-LSTM as the generator model to input historical data and generate future stock data for the next day. Convolutional Neural Network (CNN) is utilized as the discriminator to distinguish the generated stock data and real stock data. The thesis focuses on the Taiwan stock market and trains the Bi-LSTM model using the GAN architecture. It discusses the data analysis capabilities of the generative network. Furthermore, it compares GAN and Wasserstein GAN (WGAN) with the LSTM model in terms of predicting stock prices. Results showed that the model using GAN architecture can improve the performance of stock price prediction. However, there is no significant difference between GAN and WGAN in terms of predicting stock prices. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-08T16:39:00Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-08T16:39:00Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 vii 第一章 緒論 1 1.1 簡介 1 1.2 論文架構 3 第二章 背景 4 2.1 文獻回顧 4 2.1.1 市場效率假說 4 2.1.2 深度學習相關研究 5 2.2 深度學習 6 2.2.1 長短期記憶網路 6 2.2.2 卷積神經網路 8 2.3 生成對抗網路 9 2.3.1 生成器 9 2.3.2 判別器 10 2.3.3 生成對抗網路架構(GAN) 11 2.4 WGAN 13 第三章 實驗方法 15 3.1 資料來源及處理 15 3.1.1 資料介紹 15 3.1.2 資料處理 16 3.2 模型概述 17 3.2.1 生成器架構 17 3.2.2 判別器架構 17 3.2.3 GAN 18 3.2.4 WGAN 19 第四章 實驗結果 20 4.1 模型評估 20 4.2 實驗一 20 4.3 實驗二 22 第五章 結論與未來展望 25 5.1 結論 25 5.2 未來展望 25 第六章 參考文獻 27 | - |
dc.language.iso | zh_TW | - |
dc.title | 利用生成對抗綱路預測股票價格 | zh_TW |
dc.title | Stock Price Prediction Using Generative Adversarial Networks | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 陸裕豪;王釧茹;金國興 | zh_TW |
dc.contributor.oralexamcommittee | U-Hou Lok;Chuan-Ju Wang;Gow-Hsing King | en |
dc.subject.keyword | 生成對抗網路,長短期記憶網路,遞迴類神經網路,卷積神經網路,股價預測, | zh_TW |
dc.subject.keyword | Generative adversarial networks,Long short-term memory,Recurrent neural network,Convolutional neural network,Stock price prediction, | en |
dc.relation.page | 29 | - |
dc.identifier.doi | 10.6342/NTU202301621 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-07-21 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資訊工程學系 | - |
顯示於系所單位: | 資訊工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 目前未授權公開取用 | 1.08 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。