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題名 基於公司財報及產業表現基本面分析與集成模型多任務遷移學習之股價預測
Decision Support for Stock Investment with Ensemble-based Multitasking Transfer Learning centric to Fundamental Analysis on Financial Statements and Industry Status作者 黃柏勳
Ng, Bo-Xun貢獻者 姜國輝<br>劉文卿
Chiang, Kuo-Huie<br>Liou, Wen-Ching
黃柏勳
Ng, Bo-Xun關鍵詞 基本面分析
遷移學習
多任務學習
內在價值
GRU
股市預測
財務報表
Fundamental Analysis
Transfer Learning
Multi-task Learning
Intrinsic Value
Gate Recurrent Unit
Stock Market Forecasting
Financial Statement日期 2022 上傳時間 1-Aug-2022 17:26:31 (UTC+8) 摘要 隨著資訊科技的蓬勃發展，諸多科技技術與創新應用蜂擁而出，而在應用機器學習對個別股票的短期價格進行技術分析和情緒分析的大潮中，提供股票趨勢長期預測的基本面分析仍然是機器學習尚未開發的領域。雖然基於財務報表的基本面分析能夠解決股票未來表現的複雜性，做出基於價值的股票交易策略，但財務報表中會計項目的高維度特性與不確定的勾稽關系阻礙了機器學習的應用。為了解決這個問題，本研究用不同的特徵工程技術準備了數據集，以提高預測模型的性能。此外，對某一特定公司的盈利能力於潛力的分析通常是獨立的，不適用於其他公司。為此，我們開發了一個兩層轉移學習模型，以實現所獲知識的可轉移性並提高訓練的效率。最後，GRU被用來將獲得的特徵轉化為股票的比較內在價值，用於評估功能。利用台灣半導體行業11年來的上市公司財務報表和產業類股指數，實現了基於特徵的轉移學習和GRU的統一框架，用於基於財務報表和工業狀況的基本面分析，可以覆蓋決策任務的的三個階段，並在效率、準確性、精確性和轉移性及回報方面進行了評價。
In the stride of applying machine learning for short-term price prediction of individual stocks with technical analysis and sentiment analysis, fundamental analysis which provides the long-term prediction of stock trends remains unexplored territory for machine learning. Whilst fundamental analysis based on financial statements is capable of resolving the complexity of future performance of the stocks and leading to the value-based stock trading strategy, the high dimensionality and undetermined collinearity of accounting items in financial statements hinder the application of machine learning. To solve this problem, this research prepared datasets with different feature engineering techniques to improve the performance of the predictive model.Further, the machine learning analysis of profitability and potential for a specific company are usually unique and not applicable to the others. For this, a two-layer Transfer Learning model is developed for the transferability of gained knowledge and to increase the efficiency of the training. At last, GRU is used to transform the gained features into the comparative intrinsic value of the stock for the evaluation function. With the financial statements of the list companies and industrial stock indexes of the Taiwanese semiconductor industry and industrial stock index over 11 years, a unified framework of feature-based surrogate function, transfer learning, and GRU for fundamental analysis based on financial statements and industrial status which can cover the tasks of all of the three phases of decision-making was realized and evaluated with respect to efficiency, accuracy, precision, transferability and return.參考文獻 張惠珊. (2016). 財務危機預警分析-以財務報表、公司治理、商品品質三大指標探討. (碩士). 國立中正大學, 嘉義縣. Retrieved from https://hdl.handle.net/11296/4t78jn林佑南. (2020). 運用財務比率分析企業財務危機之研究. (碩士). 淡江大學, 新北市. Retrieved from https://hdl.handle.net/11296/vs3x65林宜隆. (1989). 專家系統之探討. 資訊縮影管理(14&15), 83-97.林怡君. (2020). 應用機器學習於臺灣股市投資組合框架之研究. (碩士). 中國文化大學, 台北市. Retrieved from https://hdl.handle.net/11296/h9tqa4甄典蕙. (2015). 財務報表舞弊偵測模型之建立-以中國上市公司為例. (碩士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/f82cs6羅願合. (2010). 財務指標對汽車類股報酬預測之關聯性研究.許勝豪. (2005). 模糊多準則選股模型之研究與系統設計—以台灣股票市場為例.許政宏. (2007). 研究發展, 財務指標與經營績效之關連性研究-以台灣上市上櫃 IC 設計產業為例.陳心如. (2018). 基本面因素與深度學習對台灣股票報酬率預測分析. (碩士). 國立成功大學, 台南市. Retrieved from https://hdl.handle.net/11296/4r6jmv黃奕文. (2020). 一個有效的繼續經營預測模型. (碩士). 中國文化大學, 台北市. Retrieved from https://hdl.handle.net/11296/n266a9黃耀平. (2020). 機器學習方法下的證券分析--著重於集成算法預測未來股價走勢. (碩士). 國立成功大學, 台南市. Retrieved from https://hdl.handle.net/11296/4242m9英文文獻Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016). Deep learning for stock prediction using numerical and textual information. Paper presented at the 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS).Alberg, J., & Lipton, Z. C. (2017). Improving factor-based quantitative investing by forecasting company fundamentals. arXiv preprint arXiv:1711.04837.Asad, M. (2015). Optimized stock market prediction using ensemble learning. Paper presented at the 2015 9Th international conference on application of information and communication technologies (AICT).Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of accounting research, 159-178.Chiang, J. K. (2021). Optimization of ICT Common Wealth Planning and Sharing based on Organic Economic Ecology and Theory of Knowledge Value Transformation. Paper presented at the Proceedings of the International Symposium on Grids & Clouds.Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2015). Gated feedback recurrent neural networks. 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國立政治大學
資訊管理學系
109356051資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356051 資料類型 thesis dc.contributor.advisor 姜國輝<br>劉文卿 zh_TW dc.contributor.advisor Chiang, Kuo-Huie<br>Liou, Wen-Ching en_US dc.contributor.author (Authors) 黃柏勳 zh_TW dc.contributor.author (Authors) Ng, Bo-Xun en_US dc.creator (作者) 黃柏勳 zh_TW dc.creator (作者) Ng, Bo-Xun en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:26:31 (UTC+8) - dc.date.available 1-Aug-2022 17:26:31 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:26:31 (UTC+8) - dc.identifier (Other Identifiers) G0109356051 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141050 - 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) 109356051 zh_TW dc.description.abstract (摘要) 隨著資訊科技的蓬勃發展，諸多科技技術與創新應用蜂擁而出，而在應用機器學習對個別股票的短期價格進行技術分析和情緒分析的大潮中，提供股票趨勢長期預測的基本面分析仍然是機器學習尚未開發的領域。雖然基於財務報表的基本面分析能夠解決股票未來表現的複雜性，做出基於價值的股票交易策略，但財務報表中會計項目的高維度特性與不確定的勾稽關系阻礙了機器學習的應用。為了解決這個問題，本研究用不同的特徵工程技術準備了數據集，以提高預測模型的性能。此外，對某一特定公司的盈利能力於潛力的分析通常是獨立的，不適用於其他公司。為此，我們開發了一個兩層轉移學習模型，以實現所獲知識的可轉移性並提高訓練的效率。最後，GRU被用來將獲得的特徵轉化為股票的比較內在價值，用於評估功能。利用台灣半導體行業11年來的上市公司財務報表和產業類股指數，實現了基於特徵的轉移學習和GRU的統一框架，用於基於財務報表和工業狀況的基本面分析，可以覆蓋決策任務的的三個階段，並在效率、準確性、精確性和轉移性及回報方面進行了評價。 zh_TW dc.description.abstract (摘要) In the stride of applying machine learning for short-term price prediction of individual stocks with technical analysis and sentiment analysis, fundamental analysis which provides the long-term prediction of stock trends remains unexplored territory for machine learning. Whilst fundamental analysis based on financial statements is capable of resolving the complexity of future performance of the stocks and leading to the value-based stock trading strategy, the high dimensionality and undetermined collinearity of accounting items in financial statements hinder the application of machine learning. To solve this problem, this research prepared datasets with different feature engineering techniques to improve the performance of the predictive model.Further, the machine learning analysis of profitability and potential for a specific company are usually unique and not applicable to the others. For this, a two-layer Transfer Learning model is developed for the transferability of gained knowledge and to increase the efficiency of the training. At last, GRU is used to transform the gained features into the comparative intrinsic value of the stock for the evaluation function. With the financial statements of the list companies and industrial stock indexes of the Taiwanese semiconductor industry and industrial stock index over 11 years, a unified framework of feature-based surrogate function, transfer learning, and GRU for fundamental analysis based on financial statements and industrial status which can cover the tasks of all of the three phases of decision-making was realized and evaluated with respect to efficiency, accuracy, precision, transferability and return. en_US dc.description.tableofcontents 摘要 IABSTRACT II目次 III表次 V圖次 VI第一章 緒論 1第一節 研究背景 1第二節 研究動機與目的 2第二章 文獻探討 5第一節 財務報表運用於預測之研究 5第二節 特徵工程 9一、主成分分析 9二、相關係數 10第三節 機器學習 10一、人工神經網路 10二、卷積神經網路 11三、遞歸神經網路 12四、閘門遞迴單位神經網路 13五、集成學習 14六、Inception 14七、深度殘差網路 14八、密集連接卷積網絡 15九、遷移學習 16十、多輸出學習 17第三章 研究方法 18第一節 資料蒐集 18第二節 資料前處理 19一、特徵指標 19二、補值 24三、一維時間序列資料二維化 25第三節 系統設計 26一、知識價值轉化理論 26二、多任務學習 27三、多輸出模型 28四、集成學習 28五、預測模型設計 29第四章 研究結果 31一、股價預測與真實股價誤差 31二、主要輸出損失函數 31三、公司特徵指標損失函數 32四、產業特徵指標損失函數 32五、驗證結果 33(1)長期(一年) 34(2)中期(半年) 34(3)短期(一季) 35第五章 研究結果與未來建議 37參考文獻 38中文文獻 38英文文獻 39 zh_TW dc.format.extent 2639921 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356051 en_US dc.subject (關鍵詞) 基本面分析 zh_TW dc.subject (關鍵詞) 遷移學習 zh_TW dc.subject (關鍵詞) 多任務學習 zh_TW dc.subject (關鍵詞) 內在價值 zh_TW dc.subject (關鍵詞) GRU zh_TW dc.subject (關鍵詞) 股市預測 zh_TW dc.subject (關鍵詞) 財務報表 zh_TW dc.subject (關鍵詞) Fundamental Analysis en_US dc.subject (關鍵詞) Transfer Learning en_US dc.subject (關鍵詞) Multi-task Learning en_US dc.subject (關鍵詞) Intrinsic Value en_US dc.subject (關鍵詞) Gate Recurrent Unit en_US dc.subject (關鍵詞) Stock Market Forecasting en_US dc.subject (關鍵詞) Financial Statement en_US dc.title (題名) 基於公司財報及產業表現基本面分析與集成模型多任務遷移學習之股價預測 zh_TW dc.title (題名) Decision Support for Stock Investment with Ensemble-based Multitasking Transfer Learning centric to Fundamental Analysis on Financial Statements and Industry Status en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 張惠珊. (2016). 財務危機預警分析-以財務報表、公司治理、商品品質三大指標探討. (碩士). 國立中正大學, 嘉義縣. Retrieved from https://hdl.handle.net/11296/4t78jn林佑南. (2020). 運用財務比率分析企業財務危機之研究. (碩士). 淡江大學, 新北市. Retrieved from https://hdl.handle.net/11296/vs3x65林宜隆. (1989). 專家系統之探討. 資訊縮影管理(14&15), 83-97.林怡君. (2020). 應用機器學習於臺灣股市投資組合框架之研究. (碩士). 中國文化大學, 台北市. Retrieved from https://hdl.handle.net/11296/h9tqa4甄典蕙. (2015). 財務報表舞弊偵測模型之建立-以中國上市公司為例. (碩士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/f82cs6羅願合. (2010). 財務指標對汽車類股報酬預測之關聯性研究.許勝豪. (2005). 模糊多準則選股模型之研究與系統設計—以台灣股票市場為例.許政宏. (2007). 研究發展, 財務指標與經營績效之關連性研究-以台灣上市上櫃 IC 設計產業為例.陳心如. (2018). 基本面因素與深度學習對台灣股票報酬率預測分析. (碩士). 國立成功大學, 台南市. Retrieved from https://hdl.handle.net/11296/4r6jmv黃奕文. (2020). 一個有效的繼續經營預測模型. (碩士). 中國文化大學, 台北市. Retrieved from https://hdl.handle.net/11296/n266a9黃耀平. (2020). 機器學習方法下的證券分析--著重於集成算法預測未來股價走勢. (碩士). 國立成功大學, 台南市. Retrieved from https://hdl.handle.net/11296/4242m9英文文獻Akita, R., Yoshihara, A., Matsubara, T., & Uehara, K. (2016). 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