請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83367
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蔡彥卿(Yann-Ching Tsai) | |
dc.contributor.advisor | 蔡彥卿(Yann-Ching Tsai | yanntsai@ntu.edu.tw | ), | |
dc.contributor.author | Han Lee | en |
dc.contributor.author | 李涵 | zh_TW |
dc.date.accessioned | 2023-03-19T21:05:47Z | - |
dc.date.copyright | 2022-09-30 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-09-22 | |
dc.identifier.citation | Abarbanell, J. S., and Bushee, B. J. (1997). Fundamental Analysis, future earnings, and stock prices.?Journal of Accounting Research,?35(1), 1-24. https://doi.org/10.2307/2491464 Callen, J. L., Kwan, C. C. Y., Yip, P. C. Y., and Yuan, Y. (1996). Neural network forecasting of quarterly accounting earnings. International Journal of Forecasting, 12, 475-482. https://doi.org/10.1016/S0169-2070(96)00706-6 Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., and Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351-1362. https://doi.org/10.1016/j.procs.2018.05.050 Nelson, D. M. Q., Pereira, A. C. M., and de Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. 2017 International Joint Conference on Neural Networks (IJCNN), 1419-1426. https://doi.org/10.1109/IJCNN.2017.7966019 Ou, J. A., and Penman, S. H. (1989). Financial statement analysis and the prediction of stock?returns. Journal of Accounting and Economics, 11, 295-329. https://doi.org/10.1016/0165-4101(89)90017-7 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., M?ller, A., Nothman, J., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830. https://doi.org/10.48550/arXiv.1201.0490 Sezer, O. B., Gudelek, M. U., and Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: a systematic literature review: 2005-2019. Applied Soft Computing Journal, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181 Siami-Namini, S., Tavakoli, N., and Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 1394-1401. https://doi.org/10.1109/ICMLA.2018.00227 Zhang, W., Cao, Q., and Schniederjans, M. J. (2004). Neural network earnings per share forecasting models: a comparative analysis of alternative methods. Decision Sciences, 35, 205-237. https://doi.org/10.1111/j.00117315.2004.02674.x | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83367 | - |
dc.description.abstract | 本論文以公司過去資產負債表、綜合損益表和現金流量表中的會計科目資訊以及過往的每股盈餘和稀釋每股盈餘資料預測公司下一年年末的每股盈餘或稀釋每股盈餘,並建立了144個機器學習模型以及12個深度學習模型。 分析各模型的預測結果後,我獲得了以下四點結論。第一點為122個機器學習模型和全部的深度學習模型皆優於基準預測值,顯見本論文建立的模型具有一定的預測能力。第二點為模型認為預測稀釋每股盈餘是個較簡單的任務。第三點為不同種類的機器學習模型對於自變數欄位和資料特徵處理方法的偏好不同。最後一點則是訓練資料不充足以及市場和產業變化會導致模型預測較不準確,但透過增添更多歷史資料和與公司前景預測相關之自變數欄位至資料集能提升模型的預測表現。 | zh_TW |
dc.description.abstract | This essay used accounting information from companies' past balance sheet, comprehensive income statement and cash flow statement, as well as historical earnings per share (EPS) and diluted EPS data to forecast companies' EPS or diluted EPS for the next year, and established 144 machine learning models and 12 deep learning models. After analyzing the results of each model, I came to the following four conclusions. First, 122 machine learning models and all deep learning models are better than benchmark prediction values, which shows that models built in this essay have predictive ability. Secondly, models consider forecasting diluted EPS to be a simpler task. Thirdly, different types of machine learning models have different preferences for independent variable fields and data feature processing methods. Lastly, insufficient training data and changes of markets and industries can lead to less accurate model predictions, but performance of models can be improved by adding more diverse data and future-related independent variable fields to dataset. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T21:05:47Z (GMT). No. of bitstreams: 1 U0001-0409202201280500.pdf: 6048318 bytes, checksum: e88e79cb601d33758c021ebb8c6e500b (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | 中文摘要 i ABSTRACT ii 目 錄 iii 圖目錄 v 表目錄 ix 第一章 緒論 1 第一節 研究動機 1 第二節 研究流程 1 第三節 研究目標 2 第二章 文獻回顧 3 第一節 歷史財務資料包含未來盈餘資訊 3 第二節 深度學習模型與會計領域之初步結合 6 第三節 長短期記憶模型之預測能力 10 第四節 機器學習與深度學習模型在財務領域之應用:股票價格漲跌預測 12 第五節 機器學習與深度學習模型在財務領域之應用:股票收盤價預測 14 第三章 資料及研究方法 17 第一節 資料說明 17 第二節 資料前處理流程 20 第三節 模型建立流程 28 第四節 機器學習模型 35 第五節 深度學習模型 43 第四章 預測結果分析 58 第一節 模型之預測結果 58 第二節 模型預測失準之原因 75 第五章 結論與建議 83 參考文獻 85 | |
dc.language.iso | zh-TW | |
dc.title | 財務報告分析及盈餘預測 – 機器學習模型之應用 | zh_TW |
dc.title | Financial Report Analysis and Earnings Forecast – Application of Machine Learning Models | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李淑華(Shu-Hua Lee),簡雪芳(Hsueh-Fang Chien) | |
dc.subject.keyword | 預測每股盈餘,機器學習,深度學習,財務報表分析,投資建議, | zh_TW |
dc.subject.keyword | Forecasting Earnings Per Share,Machine Learning,Deep Learning,Financial Statement Analysis,Investment Suggestion, | en |
dc.relation.page | 86 | |
dc.identifier.doi | 10.6342/NTU202203124 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2022-09-23 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 會計學研究所 | zh_TW |
顯示於系所單位: | 會計學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-0409202201280500.pdf 目前未授權公開取用 | 5.91 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。