學術產出-學位論文
| 題名 | 以星狀生成對抗網路結合系統工程與小波轉換學習動態時序性股票價量動態關係之股價預測 Use StarGAN based on ResNet and GRU Aggregating System Engineering to Learn the Joint Effect of Stock Price and Volume and Wavelet Transformation for Prediction of Stock Price |
| 作者 | 李祈寬 Li, Chi-Kuan |
| 貢獻者 | 姜國輝<br>劉文卿 Jiang, Guo-Huei<br>Liou, Wun-Cing 李祈寬 Li, Chi-Kuan |
| 關鍵詞 | 深度學習 股價預測 時序性神經網路 田口方法 小波轉換 Wavelet Transform Deep Learning Stock Price Prediction GRU Taguchi method |
| 日期 | 2022 |
| 上傳時間 | 1-八月-2022 17:25:24 (UTC+8) |
| 摘要 | 本研究以星狀生成對抗網路將證券的量價關係進行深度學習訓練，並結合系統工程中的系統動態學，建立模擬證券市場的預測模型。運用星狀生成對抗網路多面向轉換特性可以成功的處理證券量價關係以提升預測的準確性。本研究將輸出的量價資料輸入時序性神經網路GRU預測模型，預測未來一交易日或五交易日的成交量與成交價資料，達到股價預測之目的。在深度學習中，參數的選擇採用田口實驗計畫法來選出最佳的參數組合，能大幅降低實驗次數與時間成本。本研究以小波轉換將時間域之資料轉換為頻率域之資料，並發現股票市場中的高頻與低頻之訊號。本研究以深度學習模型，拓展至時間域與頻率域之轉換，並成功找出兩者之間的轉換關係。 In this study, Star Generative Adversarial Network(Star-GAN) is used to conduct deep learning training on the volume-price relationship of securities and combined with the system dynamics in systems engineering. A prediction model for simulating the securities market is established. Using the multi-faceted transformation feature of Star-GAN can successfully deal with the relationship between securities volume and price to improve the prediction accuracy. In this study, the output volume and price data are input data into GRU network prediction model to predict the transaction volume and transaction price data for one or five trading days in the future. In deep learning, the selection of parameters adopts the Taguchi experimental method to select the best combination of parameters. Which can reduce the number of experiments and time costs. This research uses wavelet transform to convert the data in the time domain into the data in the frequency domain and finds the high-frequency and low-frequency signals in the stock market. In this study, the deep learning model is used to the conversion between the time domain and the frequency domain, and find out the conversion relationship between two domains. |
| 參考文獻 | Atil, H., & Unver, Y. (2000). A different approach of experimental design: Taguchi method. Pakistan journal of biological sciences, 3(9), 1538-1540. Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. Choi, Y., Uh, Y., Yoo, J., & Ha, J.-W. (2020). Stargan v2: Diverse image synthesis for multiple domains. Paper presented at the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Dey, R., & Salem, F. M. (2017). Gate-variants of gated recurrent unit (GRU) neural networks. Paper presented at the 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU neural network methods for traffic flow prediction. Paper presented at the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. Homayouni, N., & Amiri, A. (2011). Stock price prediction using a fusion model of wavelet, fuzzy logic and ANN. Paper presented at the International conference on e-business, management and economics. Huang, Y.-P., Chen, S.-H., Hung, M.-C., & Yu, T. (2012). Liquidity cost of market orders in the Taiwan Stock Market: A study based on an order-driven agent-based artificial stock market. International Review of Financial Analysis, 23, 72-80. Karna, S. K., & Sahai, R. (2012). An overview on Taguchi method. International journal of engineering and mathematical sciences, 1(1), 1-7. Kumar, D., Sarangi, P. K., & Verma, R. (2021). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings. Lahmiri, S. (2014). Wavelet low-and high-frequency components as features for predicting stock prices with backpropagation neural networks. Journal of King Saud University-Computer and Information Sciences, 26(2), 218-227. Lan, P.-C., Kung, W.-L., Ou, Y.-L., Lin, C.-Y., Hu, W.-C., & Wang, Y.-H. (2019). Machine learning model with technical analysis for stock price prediction: Empirical study of Semiconductor Company in Taiwan. Paper presented at the 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., & Póczos, B. (2017). Mmd gan: Towards deeper understanding of moment matching network. Advances in neural information processing systems, 30. Lim, J. H., & Ye, J. C. (2017). Geometric gan. arXiv preprint arXiv:1705.02894. Lin, F.-L., Yang, S.-Y., Marsh, T., & Chen, Y.-F. (2018). Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis. International Review of Economics & Finance, 55, 285-294. Lucic, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2018). Are gans created equal? a large-scale study. Advances in neural information processing systems, 31. Porav, H., Musat, V., & Newman, P. (2019). Reducing Steganography In Cycle-consistency GANs. Paper presented at the CVPR Workshops. Reboredo, J. C., & Rivera-Castro, M. A. (2014). Wavelet-based evidence of the impact of oil prices on stock returns. International Review of Economics & Finance, 29, 145-176. Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., & Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: a review. Applied Sciences, 9(7), 1345. Saiti, B. (2017). The Lead-Lag Relationship among East Asian Economies: A Wavelet Analysis. International Business Research, 10(3), 57-68. Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306. Taguchi, G. (1995). Quality engineering (Taguchi methods) for the development of electronic circuit technology. IEEE Transactions on Reliability, 44(2), 225-229. Thiele, J. C., Bichler, O., & Dupret, A. (2019). Spikegrad: An ann-equivalent computation model for implementing backpropagation with spikes. arXiv preprint arXiv:1906.00851. Virgilio, G. P. M. (2019). High-frequency trading: a literature review. Financial markets and portfolio management, 33(2), 183-208. Wang, W., Sun, Y., & Halgamuge, S. (2018). Improving MMD-GAN training with repulsive loss function. arXiv preprint arXiv:1812.09916. Yang, S., Yu, X., & Zhou, Y. (2020). Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example. Paper presented at the 2020 International workshop on electronic communication and artificial intelligence (IWECAI). Yaz, Y., Foo, C.-S., Winkler, S., Yap, K.-H., Piliouras, G., & Chandrasekhar, V. (2018). The unusual effectiveness of averaging in GAN training. Paper presented at the International Conference on Learning Representations. Zhang, C., Tang, Y., Zhao, C., Sun, Q., Ye, Z., & Kurths, J. (2021). Multitask gans for semantic segmentation and depth completion with cycle consistency. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5404-5415. Zhang, K., Zhong, G., Dong, J., Wang, S., & Wang, Y. (2019). Stock market prediction based on generative adversarial network. Procedia computer science, 147, 400-406. 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. Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Paper presented at the Proceedings of the IEEE international conference on computer vision. Zieba, M., & Wang, L. (2017). Training triplet networks with GAN. arXiv preprint arXiv:1704.02227. 吳宗哲. (2019). 運用深度學習建構個股預測模型-以台積電為例. 李中永. (2014). 使用類神經網路結合模糊理論預測標準普爾500指數. (碩士). 國立高雄海洋科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/3y5849 杜芸菩. (2016). 台灣八大類股價量關係. (碩士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/sn9fng 林奕廷. (2019). 以循環生成對抗網路預測股價量能動態關係. 林逸婷. (2011). 倒傳遞類神經網路於股價交易點之預測. (碩士). 國立高雄第一科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/d2nn27 莊向峰. (2018). 基於行為經濟學與價量分析使用增強式學習演算法建立臺灣股票指數期貨交易策略. 陳俊諺. (2018). 運用類神經網路與田口法預測台灣 ETF 指數. 陳家騏. (2016). 牛頓第二運動定律的演變--從速度, 力到動量, 衝量, 以單一質點到多質點 (系統). 科學教育月刊(393), 11-19. 曾琬婷. (2017). 台灣加權股價指數之預測模型: 小波轉換與多項式迴歸模型之應用. 游英裕. (2004). 股價與成交量因果關係之研究-台灣股市的實証. 碩士, 葉雅玲. (2010). 產業別股票價量關係. 潘永浤. (2003). 應用田口方法於類神經網路輸入參數設計-零售商快速回應系統模式之建立為例. 碩士, 蔡尚翰. (2017). 籌碼面選股結合技術分析之投資績效研究. 賴建成. (2012). 小波轉換結合類神經網路於股價預測及價格發現之研究－以香港及中國指數現貨與期貨對台港兩地掛牌ETF為例. (博士). 國立高雄第一科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/rbbbeq 謝璁賦, & 陳安斌. (2010). 應用類神經網路於台股權值股籌碼面的知識發現. https://silverwind1982.pixnet.net/blog/post/1251072 |
| 描述 | 碩士 國立政治大學 資訊管理學系 109356040 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0109356040 |
| 資料類型 | thesis |
| dc.contributor.advisor | 姜國輝<br>劉文卿 | zh_TW |
| dc.contributor.advisor | Jiang, Guo-Huei<br>Liou, Wun-Cing | en_US |
| dc.contributor.author (作者) | 李祈寬 | zh_TW |
| dc.contributor.author (作者) | Li, Chi-Kuan | en_US |
| dc.creator (作者) | 李祈寬 | zh_TW |
| dc.creator (作者) | Li, Chi-Kuan | en_US |
| dc.date (日期) | 2022 | en_US |
| dc.date.accessioned | 1-八月-2022 17:25:24 (UTC+8) | - |
| dc.date.available | 1-八月-2022 17:25:24 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-八月-2022 17:25:24 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0109356040 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/141045 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理學系 | zh_TW |
| dc.description (描述) | 109356040 | zh_TW |
| dc.description.abstract (摘要) | 本研究以星狀生成對抗網路將證券的量價關係進行深度學習訓練，並結合系統工程中的系統動態學，建立模擬證券市場的預測模型。運用星狀生成對抗網路多面向轉換特性可以成功的處理證券量價關係以提升預測的準確性。本研究將輸出的量價資料輸入時序性神經網路GRU預測模型，預測未來一交易日或五交易日的成交量與成交價資料，達到股價預測之目的。在深度學習中，參數的選擇採用田口實驗計畫法來選出最佳的參數組合，能大幅降低實驗次數與時間成本。本研究以小波轉換將時間域之資料轉換為頻率域之資料，並發現股票市場中的高頻與低頻之訊號。本研究以深度學習模型，拓展至時間域與頻率域之轉換，並成功找出兩者之間的轉換關係。 | zh_TW |
| dc.description.abstract (摘要) | In this study, Star Generative Adversarial Network(Star-GAN) is used to conduct deep learning training on the volume-price relationship of securities and combined with the system dynamics in systems engineering. A prediction model for simulating the securities market is established. Using the multi-faceted transformation feature of Star-GAN can successfully deal with the relationship between securities volume and price to improve the prediction accuracy. In this study, the output volume and price data are input data into GRU network prediction model to predict the transaction volume and transaction price data for one or five trading days in the future. In deep learning, the selection of parameters adopts the Taguchi experimental method to select the best combination of parameters. Which can reduce the number of experiments and time costs. This research uses wavelet transform to convert the data in the time domain into the data in the frequency domain and finds the high-frequency and low-frequency signals in the stock market. In this study, the deep learning model is used to the conversion between the time domain and the frequency domain, and find out the conversion relationship between two domains. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 2 第二章 文獻探討 3 第一節 深度學習與類神經網路 3 第二節 時序性神經網路 4 第三節 生成對抗網路 6 第四節 星狀生成對抗網路 6 第五節 股票量價關係 9 第六節 深度學習與股票量價關係 9 第七節 台積電於台灣股市之代表性 10 第八節 系統動態學於股票市場 10 第九節 田口實驗計畫法 11 第十節 小波轉換 12 第三章 研究方法 15 第一節 資料蒐集與資料前處理 15 第二節 星狀生成對抗網路訓練 18 第三節 時間序列網路訓練 20 第四節 模擬系統動態學的採用 20 第五節 田口實驗計畫法 21 第六節 小波轉換的採用 21 第七節 驗證方法 22 第八節 實驗流程圖 23 第九節 實驗設計 23 第四章 實驗結果 25 第一節 時間域之預測結果 25 第二節 頻率域之小波轉換 28 第五章 研究結論與未來建議 33 參考文獻 34 | zh_TW |
| dc.format.extent | 2438030 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0109356040 | en_US |
| dc.subject (關鍵詞) | 深度學習 | zh_TW |
| dc.subject (關鍵詞) | 股價預測 | zh_TW |
| dc.subject (關鍵詞) | 時序性神經網路 | zh_TW |
| dc.subject (關鍵詞) | 田口方法 | zh_TW |
| dc.subject (關鍵詞) | 小波轉換 | zh_TW |
| dc.subject (關鍵詞) | Wavelet Transform | en_US |
| dc.subject (關鍵詞) | Deep Learning | en_US |
| dc.subject (關鍵詞) | Stock Price Prediction | en_US |
| dc.subject (關鍵詞) | GRU | en_US |
| dc.subject (關鍵詞) | Taguchi method | en_US |
| dc.title (題名) | 以星狀生成對抗網路結合系統工程與小波轉換學習動態時序性股票價量動態關係之股價預測 | zh_TW |
| dc.title (題名) | Use StarGAN based on ResNet and GRU Aggregating System Engineering to Learn the Joint Effect of Stock Price and Volume and Wavelet Transformation for Prediction of Stock Price | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | Atil, H., & Unver, Y. (2000). A different approach of experimental design: Taguchi method. Pakistan journal of biological sciences, 3(9), 1538-1540. Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition. Choi, Y., Uh, Y., Yoo, J., & Ha, J.-W. (2020). Stargan v2: Diverse image synthesis for multiple domains. Paper presented at the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Dey, R., & Salem, F. M. (2017). Gate-variants of gated recurrent unit (GRU) neural networks. Paper presented at the 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU neural network methods for traffic flow prediction. Paper presented at the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. Homayouni, N., & Amiri, A. (2011). Stock price prediction using a fusion model of wavelet, fuzzy logic and ANN. Paper presented at the International conference on e-business, management and economics. Huang, Y.-P., Chen, S.-H., Hung, M.-C., & Yu, T. (2012). Liquidity cost of market orders in the Taiwan Stock Market: A study based on an order-driven agent-based artificial stock market. International Review of Financial Analysis, 23, 72-80. Karna, S. K., & Sahai, R. (2012). An overview on Taguchi method. International journal of engineering and mathematical sciences, 1(1), 1-7. Kumar, D., Sarangi, P. K., & Verma, R. (2021). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings. Lahmiri, S. (2014). Wavelet low-and high-frequency components as features for predicting stock prices with backpropagation neural networks. Journal of King Saud University-Computer and Information Sciences, 26(2), 218-227. Lan, P.-C., Kung, W.-L., Ou, Y.-L., Lin, C.-Y., Hu, W.-C., & Wang, Y.-H. (2019). Machine learning model with technical analysis for stock price prediction: Empirical study of Semiconductor Company in Taiwan. Paper presented at the 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., & Póczos, B. (2017). Mmd gan: Towards deeper understanding of moment matching network. Advances in neural information processing systems, 30. Lim, J. H., & Ye, J. C. (2017). Geometric gan. arXiv preprint arXiv:1705.02894. Lin, F.-L., Yang, S.-Y., Marsh, T., & Chen, Y.-F. (2018). Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis. International Review of Economics & Finance, 55, 285-294. Lucic, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2018). Are gans created equal? a large-scale study. Advances in neural information processing systems, 31. Porav, H., Musat, V., & Newman, P. (2019). Reducing Steganography In Cycle-consistency GANs. Paper presented at the CVPR Workshops. Reboredo, J. C., & Rivera-Castro, M. A. (2014). Wavelet-based evidence of the impact of oil prices on stock returns. International Review of Economics & Finance, 29, 145-176. Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., & Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: a review. Applied Sciences, 9(7), 1345. Saiti, B. (2017). The Lead-Lag Relationship among East Asian Economies: A Wavelet Analysis. International Business Research, 10(3), 57-68. Sherstinsky, A. (2020). 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| dc.identifier.doi (DOI) | 10.6814/NCCU202201015 | en_US |