請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98075完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王泰昌 | zh_TW |
| dc.contributor.advisor | Taychang Wang | en |
| dc.contributor.author | 官歆芸 | zh_TW |
| dc.contributor.author | Hsin-Yun Kuan | en |
| dc.date.accessioned | 2025-07-24T16:05:48Z | - |
| dc.date.available | 2025-07-25 | - |
| dc.date.copyright | 2025-07-24 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-16 | - |
| dc.identifier.citation | Aggarwal, C. C. (2018). Neural Networks and Deep Learning. In: Springer.
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Journal of the Royal Statistical Society Series B: Statistical Methodology, 67(2), 301-320. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98075 | - |
| dc.description.abstract | 本研究探討機器學習（ML）模型在營收預測與投資策略上的表現，特別聚焦於每月揭露的資料。我們評估六種 ML 模型，其中隨機森林（Random Forest）在預測準確度上表現最佳，且優於分析師預測。根據其預測建構的投資策略，在扣除交易成本後可產生年化超額報酬 51.29%。我們提出的大多數 ML 模型在報酬表現上優於大型語言模型（LLMs）與自我回歸整合移動平均（ARIMA）模型，顯示這些方法在提升投資績效方面具有明顯優勢。 | zh_TW |
| dc.description.abstract | This study examines the predictive performance of machine learning (ML) models in revenue forecasting and investment strategies, focusing on monthly disclosures. Six ML models are evaluated, with Random Forest achieving the highest accuracy and exceeding analyst forecasts. Strategies based on its predictions yield an annualized excess return of 51.29% after transaction costs. Most of the ML models we propose generate higher returns than large language models (LLMs) and Autoregressive Integrated Moving Average (ARIMA) models, demonstrating their effectiveness in improving investment performance. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-24T16:05:48Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-24T16:05:48Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 i
中文摘要 ii 英文摘要 iii 目次 iv 圖次 vii 表次 viii 1. Introduction 1 2. Literature Review 8 2.1 The Market Impact of Monthly Revenue Disclosures 8 2.2 The Effectiveness and Limitations of Analyst Revenue Forecasts 10 2.3 The Role of Machine Learning in Financial Forecasting 12 3. Sample Selection and Research Methodology 13 3.1 Data and Sample Selection 13 3.2 Machine Learning Approaches for Revenue Forecasting 17 3.2.1 Decision Tree 17 3.2.2 Random Forest 18 3.2.3 Gradient Boosting 18 3.2.4 Neural Networks 19 3.2.5 Nearest Neighbors 19 3.2.6 Elastic Net 20 3.2.7 Model Training and Optimization 20 3.3 Predictive Performance of Machine Learning and Analysts 21 3.4 Forecasting Revenue Changes Using Machine Learning Models 23 3.5 Stock Portfolio Strategies Based on ML Revenue Forecasts 24 4. Empirical Results 25 4.1 Revenue Forecast Accuracy of Machine Learning and Analyst Estimates 26 4.2 Assessing the Forecast Accuracy of Machine Learning Models for Revenue 27 4.3 Portfolio Performance from Machine Learning Revenue Forecasts 29 4.4 Isolating Predictive Performance from Revenue Announcement Effects 44 4.5 Short-Term Abnormal Returns from Machine Learning Revenue Forecasts 45 4.6 Post-Revenue Announcement Drift and Machine Learning Forecasts 47 4.7 Machine Learning Predictions in the Technology Sector 48 4.8 Robustness tests 51 4.8.1 Effect of Training Window Length in Machine Learning Forecasts 51 4.8.2 Portfolio Performance and Revenue per Share Analysis 53 4.8.3 Portfolio Performance After Excluding the Construction Industry 54 5. Additional Analysis 55 5.1 Strategy Profitability After Accounting for Transaction Costs 55 5.2 Investment Performance of Large Language Model Forecasts 57 5.3 Investment Performance of ARIMA-Based Forecasts 60 5.4 Investment Performance of EPS Forecasts 62 6. Conclusion 64 References 67 Appendix A. Variables Definitions 73 Appendix B. Feature Variables and Economic Significance 74 Appendix C. Intuitive Explanation of Machine Learning Models 77 | - |
| dc.language.iso | en | - |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 大型語言模型 | zh_TW |
| dc.subject | 分析師預測 | zh_TW |
| dc.subject | 財務分析 | zh_TW |
| dc.subject | 台灣股市 | zh_TW |
| dc.subject | ARIMA模型 | zh_TW |
| dc.subject | 營收預測 | zh_TW |
| dc.subject | ARIMA model | en |
| dc.subject | Predicted revenue | en |
| dc.subject | Machine learning | en |
| dc.subject | Large language model | en |
| dc.subject | Analyst forecast | en |
| dc.subject | Financial analysis | en |
| dc.subject | Taiwan stock market | en |
| dc.title | 機器學習在財務預測的應用： 月營收預測與超額報酬表現之比較研究 | zh_TW |
| dc.title | The Power of Machine Learning in Financial Forecasting: A Comparative Study of Monthly Revenue Prediction and Alpha Generation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.oralexamcommittee | 陳業寧;曾怡潔;林瑞青;王曉雯 | zh_TW |
| dc.contributor.oralexamcommittee | Yehning Chen;Yi-Jie Tseng;Ruey-Ching Lin;Hsiao-Wen Wang | en |
| dc.subject.keyword | 營收預測,機器學習,大型語言模型,分析師預測,財務分析,台灣股市,ARIMA模型, | zh_TW |
| dc.subject.keyword | Predicted revenue,Machine learning,Large language model,Analyst forecast,Financial analysis,Taiwan stock market,ARIMA model, | en |
| dc.relation.page | 77 | - |
| dc.identifier.doi | 10.6342/NTU202501949 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-16 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 會計學系 | - |
| dc.date.embargo-lift | 2025-07-25 | - |
| 顯示於系所單位： | 會計學系 | |
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