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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88135完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡彥卿 | zh_TW |
| dc.contributor.advisor | Yann-Ching Tsai | en |
| dc.contributor.author | 張達元 | zh_TW |
| dc.contributor.author | Ta-Yuan Chang | en |
| dc.date.accessioned | 2023-08-08T16:27:23Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-08 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-18 | - |
| dc.identifier.citation | 台灣經濟新報社. (2018). 臺灣經濟新報 財務資料庫 科目說明 (IFRS 9版 ). https://www.tej.com.tw/webtej/plus/wim4.htm#:~:text=%E6%87%89%E6%94%B6%E6%AC%BE%E3%80%82-,%E5%82%99%E4%BE%9B%E5%87%BA%E5%94%AE%E9%87%91%E8%9E%8D%E8%B3%87%E7%94%A2%E6%87%89%E4%BE%9D%E5%85%B6%E6%B5%81%E5%8B%95%E6%80%A7,%E9%87%91%E8%9E%8D%E8%B3%87%E7%94%A2%EF%BC%8D%E9%9D%9E%E6%B5%81%E5%8B%95%E3%80%95%E3%80%82
李涵（2022）。財務報告分析及盈餘預測 – 機器學習模型之應用，國立臺灣大學，台北市。 Amani, F. A., & Fadlalla, A. M. (2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems, 24, 32-58. Bao, D. H., Lewis, M. T., Lin, W. T., & Manegold, J. G. (1983). Applications of time‐series analysis in accounting: A review. Journal of Forecasting, 2(4), 405-423. Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552-567. Bird, R., Gerlach, R., & Hall, A. D. (2001). The prediction of earnings movements using accounting data: an update and extension of Ou and Penman. Journal of Asset Management, 2, 180-195. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons. Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140. Callen, J. L., Kwan, C. C., Yip, P. C., & Yuan, Y. (1996). Neural network forecasting of quarterly accounting earnings. International journal of forecasting, 12(4), 475-482. Chen, K., Zhou, Y., & Dai, F. (2015). A LSTM-based method for stock returns prediction: A case study of China stock market. 2015 IEEE international conference on big data (big data), Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, Chen, X., Cho, Y. H., Dou, Y., & Lev, B. (2022). Predicting future earnings changes using machine learning and detailed financial data. Journal of Accounting Research, 60(2), 467-515. Cheng, C., Sa-Ngasoongsong, A., Beyca, O., Le, T., Yang, H., Kong, Z., & Bukkapatnam, S. T. (2015). Time series forecasting for nonlinear and non-stationary processes: a review and comparative study. Iie Transactions, 47(10), 1053-1071. de Wet, J. (2013). Earnings per share as a measure of financial performance: does it obscure more than it reveals? De Wet, JH v H, 265-275. Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. the Journal of Finance, 47(2), 427-465. Fawagreh, K., Gaber, M. M., & Elyan, E. (2014). Random forests: from early developments to recent advancements. Systems Science & Control Engineering: An Open Access Journal, 2(1), 602-609. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European journal of operational research, 270(2), 654-669. Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. icml, Fukunaga, K. (2013). Introduction to statistical pattern recognition. Elsevier. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471. Graham, J. R., Harvey, C. R., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of accounting and economics, 40(1-3), 3-73. Ho, T. K. (1995). Random decision forests. Proceedings of 3rd international conference on document analysis and recognition, Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. Kabiraj, S., Raihan, M., Alvi, N., Afrin, M., Akter, L., Sohagi, S. A., & Podder, E. (2020). Breast cancer risk prediction using XGBoost and random forest algorithm. 2020 11th international conference on computing, communication and networking technologies (ICCCNT), LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. Li, B., Hou, Y., & Che, W. (2022). Data augmentation approaches in natural language processing: A survey. AI Open, 3, 71-90. Liu, Z. (2011). A method of SVM with normalization in intrusion detection. Procedia Environmental Sciences, 11, 256-262. Mullainathan, S., & Spiess, J. (2017). Machine learning: an applied econometric approach. Journal of Economic Perspectives, 31(2), 87-106. 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. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. Qian, X.-Y., & Gao, S. (2017). Financial series prediction: Comparison between precision of time series models and machine learning methods. arXiv preprint arXiv:1706.00948, 1-9. Sharpe, W. F., Alexander, G. J., & Bailey, J. V. (1999). Investment. Prentice Hall Incorporated. Shekar, B., & Dagnew, G. (2019). Grid search-based hyperparameter tuning and classification of microarray cancer data. 2019 second international conference on advanced computational and communication paradigms (ICACCP), Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., & Xu, H. (2020). Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478. Xiong, R., Nichols, E. P., & Shen, Y. (2015). Deep learning stock volatility with google domestic trends. arXiv preprint arXiv:1512.04916. Zhang, W., Cao, Q., & Schniederjans, M. J. (2004). Neural network earnings per share forecasting models: A comparative analysis of alternative methods. Decision Sciences, 35(2), 205-237. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88135 | - |
| dc.description.abstract | 本論文使用公司過去發佈之資產負債表、綜合損益表和現金流量表之會計科目資訊，搭配三種資料特徵處理方式，並分別使用隨機森林迴歸器、極限梯度提升迴歸器和長短期記憶模型，以預測下一年度之調整每股盈餘和調整稀釋每股盈餘。另外，本論文接著使用模型所預測下一年度的調整每股盈餘和調整稀釋每股盈餘，以該數值相對於本年度調整和調整稀釋每股盈餘之漲跌為基礎，建立不同投資組合以實證套利之可能。
經過模型和實證結果，即使在樣本期間具新會計公報發行或疫情之影響，本論文得到以下三個結論：第一，以決策樹為基礎的機器學習模型和深度學習模型皆有預測下一年度調整每股盈餘和調整稀釋每股盈餘之能力，其中又以深度學習之長短期記憶模型表現更為精確。第二，三種資料特徵處理方式中，以所使用的第三種資料特徵處理方式—以流通在外股數為基礎做資料標準化—較為準確。最後，本論文發現在假設市場完美、沒有交易成本下，在每一個年度預測下一年度調整每股盈餘和調整稀釋每股盈餘所形成之投資組合中，以當期年度的真實值為基準下，使用長短期記憶模型幾乎皆具有套利之空間，且使用預測較準之長短期記憶模型搭配第三種資料特徵處理方式，更為套利結果前五名。 | zh_TW |
| dc.description.abstract | This study utilizes accounting information from the historically published balance sheets, income statements, and cash flow statements of companies. Three different data preprocessing methods are applied, and three models—Random Forest Regressor, Extreme Gradient Boosting Regressor, and Long Short-Term Memory (LSTM) model—are used to predict the adjusted earnings per share or adjusted diluted earnings per share for the next fiscal year. Additionally, based on the predicted values, the study establishes different investment portfolios to explore potential empirical arbitrage opportunities.
Based on the model and empirical results, even with the impact of the adoption of IFRS 9, IFRS 15, and IFRS 16 and the pandemic during the sampling period, this paper draws the following three conclusions: Firstly, both tree-based machine learning and deep learning have the ability to predict the adjusted earnings per share and adjusted diluted earnings per share for the next fiscal year. Among them, deep learning, specifically the LSTM model, performs more accurately. Secondly, among the three data feature selection methods, the third method, which involves normalizing the data based on the number of outstanding shares, is more accurate. Lastly, this paper finds that assuming a perfect market with no transaction costs, in each year's forecast of the next fiscal year's adjusted earnings per share and adjusted diluted earnings per share, there is consistently an arbitrage opportunity when using the LSTM model along with the third data feature selection method. This combination ranks among the top five in terms of arbitrage results. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-08T16:27:23Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-08T16:27:23Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 I
中文摘要 II ABSTRACT III 圖目錄 VI 表目錄 IX 第一章 緒論 1 第一節 研究動機與目標 1 第二節 研究架構 2 第二章 文獻回顧 3 第一節 預測每股盈餘與套利 3 第二節 機器學習與深度學習方法 5 第三章 研究及實證方法 8 第一節 資料自變數及應變數 8 第二節 資料選取 9 第三節 資料特徵處理方式 11 第四節 模型建立與評估方式 13 第五節 機器學習與深度學習之調整技巧與方式 16 第六節 套利模型介紹 18 第四章 結果分析 21 第一節 探討各模型下不同資料前處理方式之差異 21 第二節 探討各資料特徵處理方式在不同模型下之差異 26 第三節 不同模型與不同資料特徵處理方式綜合分析 35 第四節 探討欄位重要性 36 第五節 探討各模型下持有部位不同之套利結果差異 43 第六節 探討具套利空間之各年度表現 50 第五章 結論與建議 59 第一節 研究結論 59 第二節 研究限制 60 第三節 研究建議 60 參考文獻 61 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 套利 | zh_TW |
| dc.subject | 每股盈餘預測 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 投資策略 | zh_TW |
| dc.subject | 財報分析 | zh_TW |
| dc.subject | Investment Strategies | en |
| dc.subject | Machine Learning | en |
| dc.subject | Earnings per Share Prediction | en |
| dc.subject | Deep Learning | en |
| dc.subject | Financial Statement Analysis | en |
| dc.subject | Arbitrage | en |
| dc.title | 每股盈餘預測及套利投資策略探討-機器學習模型應用 | zh_TW |
| dc.title | Earnings per Share Prediction and Arbitrage Strategy – an Application of Machine Learning Models | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 劉心才 | zh_TW |
| dc.contributor.coadvisor | Hsin-Tsai Liu | en |
| dc.contributor.oralexamcommittee | 李淑華;簡雪芳 | zh_TW |
| dc.contributor.oralexamcommittee | Shu-Hua Lee;Hsueh-Fang Chien | en |
| dc.subject.keyword | 每股盈餘預測,機器學習,深度學習,財報分析,套利,投資策略, | zh_TW |
| dc.subject.keyword | Earnings per Share Prediction,Machine Learning,Deep Learning,Financial Statement Analysis,Arbitrage,Investment Strategies, | en |
| dc.relation.page | 63 | - |
| dc.identifier.doi | 10.6342/NTU202301650 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-07-18 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 會計學系 | - |
| dc.date.embargo-lift | 2028-07-17 | - |
| 顯示於系所單位： | 會計學系 | |
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