English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113318/144297 (79%)
Visitors : 50983677      Online Users : 866
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/141050
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141050


    Title: 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
    Authors: 黃柏勳
    Ng, Bo-Xun
    Contributors: 姜國輝
    劉文卿

    Chiang, Kuo-Huie
    Liou, Wen-Ching

    黃柏勳
    Ng, Bo-Xun
    Keywords: 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
    Date: 2022
    Issue Date: 2022-08-01 17:26:31 (UTC+8)
    Abstract: 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.
    Reference: 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. Paper presented at the International conference on machine learning.
    Dasarathy, B. V., & Sheela, B. V. (1979). A composite classifier system design: Concepts and methodology. Proceedings of the IEEE, 67(5), 708-713.
    Dietterich, T. G. (2000). Ensemble methods in machine learning. Paper presented at the International workshop on multiple classifier systems.
    El Naqa, I., & Murphy, M. J. (2015). What is machine learning? In machine learning in radiation oncology (pp. 3-11): Springer.
    Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
    Galli, S. (2020). Python feature engineering cookbook: over 70 recipes for creating, engineering, and transforming features to build machine learning models: Packt Publishing Ltd.
    Ghosn, J., & Bengio, Y. (1996). Multi-task learning for stock selection. Advances in neural information processing systems, 9.
    Graham, B., Dodd, D. L. F., & Cottle, S. (1934). Security analysis (Vol. 452): McGraw-Hill New York.
    He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
    Hopfield, J. J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences, 79(8), 2554-2558.
    Howley, T., Madden, M. G., O’Connell, M.-L., & Ryder, A. G. (2005). The effect of principal component analysis on machine learning accuracy with high dimensional spectral data. Paper presented at the International Conference on Innovative Techniques and Applications of Artificial Intelligence.
    Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Kang, H. B. (2010). A Case Study On The Archer Daniels Midland (ADM) Companys Financial Statement Analysis: Strengths And Weaknesses. Journal of Business Case Studies (JBCS), 6(3).
    Kraus, M., & Feuerriegel, S. (2017). Decision support from financial disclosures with deep neural networks and transfer learning. Decision Support Systems, 104, 38-48.
    Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
    Kryzanowski, L., Galler, M., & Wright, D. W. (1993). Using artificial neural networks to pick stocks. Financial Analysts Journal, 49(4), 21-27.
    LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
    McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
    McClanahan, C. (2010). History and evolution of gpu architecture. A Survey Paper, 9.
    McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115-133.
    Nam, K., & Seong, N. (2019). Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market. Decision Support Systems, 117, 100-112.
    Ou, J. A., & Penman, S. H. (1989). Financial statement analysis and the prediction of stock returns. Journal of accounting and economics, 11(4), 295-329.
    Pan, S. J., Kwok, J. T., & Yang, Q. (2008). Transfer learning via dimensionality reduction. Paper presented at the AAAI.
    Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering. 22 (10), 1345.
    Pearson, K. (1895). Notes on Regression and Inheritance in the Case of Two Parents Proceedings of the Royal Society of London, 58, 240-242. K Pearson.
    Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11), 559-572.
    Popa, D., Kiss, M., & Sziki, K. (2012). CONTEMPORARY APPROACHES OF COMPANY PERFORMANCE ANALYSIS BASED ON RELEVANT FINANCIAL INFORMATION. Annals of the University of Oradea, Economic Science Series, 21(2).
    Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. Retrieved from
    Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1249.
    Schaller, R. R. (1997). Moore`s law: past, present and future. IEEE spectrum, 34(6), 52-59.
    Shynkevich, Y., McGinnity, T. M., Coleman, S. A., & Belatreche, A. (2016). Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning. Decision Support Systems, 85, 74-83.
    Silva, B., & Marques, N. C. (2010). Feature Clustering with Self-organizing Maps and an Application to Financial Time-series for Portfolio Selection. Paper presented at the IJCCI (ICFC-ICNC).
    Soekarno, S., & Azhari, D. A. (2010). Analysis of Financial Ratio to Distinguish Indonesia Joint Venture General Insurance Company Performance using Discriminant Analysis. Asian Journal of Technology Management, 3(2), 101-122.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., . . . Rabinovich, A. (2015). Going deeper with convolutions. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
    Tipping, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3), 611-622.
    Wang, Z., & Oates, T. (2015). Imaging time-series to improve classification and imputation. Paper presented at the Twenty-Fourth International Joint Conference on Artificial Intelligence.
    Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and intelligent laboratory systems, 2(1-3), 37-52.
    Xu, D., Shi, Y., Tsang, I. W., Ong, Y.-S., Gong, C., & Shen, X. (2019). Survey on multi-output learning. IEEE transactions on neural networks and learning systems, 31(7), 2409-2429.
    Yan, L. C., Yoshua, B., & Geoffrey, H. (2015). Deep learning. nature, 521(7553), 436-444.
    Description: 碩士
    國立政治大學
    資訊管理學系
    109356051
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109356051
    Data Type: thesis
    DOI: 10.6814/NCCU202201082
    Appears in Collections:[資訊管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    605101.pdf2578KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback