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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪茂蔚 | zh_TW |
| dc.contributor.advisor | Mao-Wei Hung | en |
| dc.contributor.author | 林冠妤 | zh_TW |
| dc.contributor.author | Guan-Yu Lin | en |
| dc.date.accessioned | 2025-07-02T16:28:02Z | - |
| dc.date.available | 2025-07-03 | - |
| dc.date.copyright | 2025-07-02 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2025-02-06 | - |
| dc.identifier.citation | Abbai, B., Akinci, O., Benigno, G., di Giovanni, J., Groen, J. J., Heymann, R. C., Lin, L., and Noble, A. I. (2022). The global supply side of inflationary pressures. Technical report, Federal Reserve Bank of New York.
Aoki, K. and Nikolov, K. (2015). Bubbles, banks and financial stability. Journal of Monetary Economics, 74:33–51. Başoğlu Kabran, F. and Ünlü, K. D. (2021). A two-step machine learning approach to predict s&p 500 bubbles. Journal of Applied Statistics, 48(13-15):2776–2794. Biau, G. and Scornet, E. (2016). A random forest guided tour. Test, 25:197–227. Boubaker, S., Liu, Z., Sui, T., and Zhai, L. (2022). The mirror of history: How to statistically identify stock market bubble bursts. Journal of Economic Behavior & Organization, 204:128–147. Brunnermeier, M. K. and Oehmke, M. (2013). Bubbles, financial crises, and systemic risk. Handbook of the Economics of Finance, 2:1221–1288. Dong, F., Miao, J., and Wang, P. (2020). Asset bubbles and monetary policy. Review of Economic Dynamics, 37:S68–S98. Evans, G. W. (1991). Pitfalls in testing for explosive bubbles in asset prices. The American Economic Review, 81(4):922–930. Huang, J.-Y., Tung, C.-L., and Lin, W.-Z. (2023). Using social network sentiment analysis and genetic algorithm to improve the stock prediction accuracy of the deep learning-based approach. International Journal of Computational Intelligence Systems, 16(1):93. Kumar, S. and Krenner, N. (2002). Review of the semiconductor industry and technology roadmap. Journal of science education and technology, 11:229–236. Lee, H., Surdeanu, M., MacCartney, B., and Jurafsky, D. (2014). On the importance of text analysis for stock price prediction. In LREC, volume 2014, pages 1170–1175. LeRoy, S. F. and Porter, R. D. (1981). The present-value relation: Tests based on implied variance bounds. Econometrica: journal of the Econometric Society, pages 555–574. Levitin, A. J. and Wachter, S. M. (2011). Explaining the housing bubble. Geo. LJ, 100:1177. Long, H., Zaremba, A., Zhou, W., and Bouri, E. (2022). Macroeconomics matter: Leading economic indicators and the cross-section of global stock returns. Journal of Financial Markets, 61:100736. Lou, C.-C., Lee, T.-P., Gong, S.-C., and Lin, S.-L. (2010). Effects of technical innovation on market value of the us semiconductor industry. Technological forecasting and social change, 77(8):1322–1338. Mienye, I. D. and Sun, Y. (2022). A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access, 10:99129–99149. Milosevic, N. (2016). Equity forecast: Predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751. Nazareth, N. and Reddy, Y. V. R. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219:119640. Ozgur, O., Yilanci, V., and Ozbugday, F. C. (2021). Detecting speculative bubbles in metal prices: Evidence from gsadf test and machine learning approaches. Resources Policy, 74:102306. Park, S. and Yang, J.-S. (2023). Combining machine learning models with gsadf test for bitcoin market crash prediction. Available at SSRN 4202271. Phillips, P. C., Shi, S., and Yu, J. (2015). Testing for multiple bubbles: Historical episodes of exuberance and collapse in the s&p 500. International economic review, 56(4):1043–1078. Phillips, P. C., Wu, Y., and Yu, J. (2011). Explosive behavior in the 1990s nasdaq: When did exuberance escalate asset values? International economic review, 52(1):201–226. Powers, D. M. (2020). Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061. Reinhart, C. M. and Rogoff, K. S. (2014). Recovery from financial crises: Evidence from 100 episodes. American Economic Review, 104(5):50–55. Roubini, N. (2006). Why central banks should burst bubbles. International Finance, 9(1):87–107. Schaller, R. R. (1997). Moore’s law: past, present and future. IEEE spectrum, 34(6):52–59. Semiconductor Industry Association (2018). Annual semiconductor sales increase 21.6 percent, top $400 billion for first time. Semiconductor Industry Association (2022). 2022 state of the u.s. semiconductor industry. Technical report, Semiconductor Industry Association. Shah, D., Isah, H., and Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2):26. Shiller, R. J. et al. (1981). Do stock prices move too much to be justified by subsequent changes in dividends? Skrobotov, A. (2023). Testing for explosive bubbles: a review. Dependence Modeling, 11(1):20220152. Song, Y.-Y. and Ying, L. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2):130. Stoltzfus, J. C. (2011). Logistic regression: a brief primer. Academic emergency medicine, 18(10):1099–1104. Tan, H. and Mathews, J. A. (2010). Cyclical industrial dynamics: The case of the global semiconductor industry. Technological Forecasting and Social Change, 77(2):344–353. Tran, K. L., Le, H. A., Lieu, C. P., and Nguyen, D. T. (2023). Machine learning to forecast financial bubbles in stock markets: Evidence from vietnam. International Journal of Financial Studies, 11(4):133. West, K. D. (1987). A specification test for speculative bubbles. The quarterly journal of economics, 102(3):553–580. Wu, Z. (2024). Are we in a bubble? financial vulnerabilities in semiconductor, web3, and genetic engineering markets. International Review of Economics & Finance, 90:32–44. Wöckl, I. (2019). Bubble detection in financial markets-a survey of theoretical bubble models and empirical bubble detection tests. Available at SSRN 3460430. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97560 | - |
| dc.description.abstract | 金融領域的過往研究中，資產泡沫一直是一個關注的焦點，相關的子領域，如泡沫的形成、泡沫理論和泡沫檢測等，持續受到學術界的討論。本文探討了機器學習在金融領域的新應用，通過兩階段的機器學習方法，預測了半導體在資本市場中可能出現的泡沫現象。其中選擇費城半導體指數作為產業的代表。實證結果表明，透過總體經濟指標、產業供需指標和財務指標的綜合應用，機器學習模型能夠成功地預測泡沫的出現。此外，本文嘗試對模型中具有經濟意義的變數進行解釋，藉此擴充了對泡沫現象的研究。透過結合模型解釋的完整研究，不僅拓展了當前學術界在這一領域的實證分析，同時也有助於市場參與者更好地理解市場的動態，識別潛在的市場泡沫，進而提升其決策能力和市場反應能力。 | zh_TW |
| dc.description.abstract | Asset bubbles have long been a focal point of concern in financial research, with subfields such as bubble formation, bubble theories, and bubble detection continuously debated within academia. This paper proposes a novel machine learning approach to predict potential asset bubbles in the semiconductor sector. Using the Philadelphia Semiconductor Index, the study demonstrates that machine learning models, trained on macroeconomic indicators, industry data, and financial metrics, can successfully identify bubble formation. Furthermore, the research goes beyond prediction by analyzing the model's internal workings to pinpoint economically meaningful variables that contribute to bubble emergence. This combined approach not only advances academic understanding of bubbles but also equips market participants with valuable tools to navigate market dynamics and make informed investment decisions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-07-02T16:28:02Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-07-02T16:28:02Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 摘要 iii Abstract iv 目次 v 圖次 viii 表次 ix 第一章 緒論 1 1.1 研究背景動機 1 1.2 研究目的 2 1.3 研究架構 3 第二章 文獻回顧 5 2.1 資產泡沫 5 2.1.1 泡沫與特徵 5 2.1.2 資產泡沫形成機制與理論泡沫模型 6 2.1.3 資產泡沫的經濟影響 7 2.2 泡沫檢測方法 8 2.2.1 早期泡沫檢測方法 8 2.2.1.1 方差界限檢定 9 2.2.1.2 West 兩步驟檢定 9 2.2.1.3 基於標準平穩性和協整性的檢定 10 2.2.2 近年泡沫檢測方法 10 2.2.2.1 遞迴式單根檢定 11 2.2.2.2 分數整合檢定 12 2.2.2.3 狀態轉換檢定 12 2.3 機器學習在金融領域的應用 13 2.3.1 主要應用 13 2.3.2 新興應用 14 2.4 半導體市場泡沫 14 2.4.1 半導體產業與市場概況 14 2.4.2 半導體資本市場中的泡沫現象 15 2.4.3 半導體資本市場泡沫的預測 16 第三章 研究方法 18 3.1 研究架構 18 3.2 GSADF 檢定 18 3.3 預測金融泡沫的機器學習方法 20 3.3.1 單一模型 21 3.3.1.1 羅吉斯迴歸 21 3.3.1.2 決策樹 22 3.3.1.3 支援向量機 23 3.3.2 集成學習模型 25 3.3.2.1 隨機森林 25 3.3.2.2 極限梯度提升 25 3.3.3 模型效能評估 26 3.3.3.1 混淆矩陣與分類 26 3.3.3.2 評估指標 27 第四章 實證分析 30 4.1 數據蒐集與前處理 30 4.1.1 費半指數與價格泡沫檢測 30 4.1.2 預測資產泡沫的指標選定 34 4.1.3 資料前處理 36 4.2 基於機器學習演算法的泡沫預測結果 36 4.2.1 模型結果 36 4.2.2 穩健性測試 39 4.2.3 模型結果解釋 40 第五章 結論與建議 46 5.1 結論 46 5.2 研究限制 47 5.3 後續研究建議 47 參考文獻 49 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 費城半導體指數 | zh_TW |
| dc.subject | 泡沫預測 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | GSADF檢定 | zh_TW |
| dc.subject | 資產泡沫 | zh_TW |
| dc.subject | 半導體產業 | zh_TW |
| dc.subject | GSADF Test | en |
| dc.subject | Asset Bubbles | en |
| dc.subject | Philadelphia Semiconductor Index | en |
| dc.subject | Semiconductor Industry | en |
| dc.subject | Bubble Detection | en |
| dc.subject | Machine Learning | en |
| dc.title | 半導體資本市場泡沫的兩階段機器學習預測方法 | zh_TW |
| dc.title | A Two-Stage Machine Learning Approach to Predict Asset Bubbles in the Semiconductor Capital Market | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡佳芬;蔡豐澤 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Fen Tsai;Feng-Tse Tsai | en |
| dc.subject.keyword | 半導體產業,費城半導體指數,資產泡沫,GSADF檢定,機器學習,泡沫預測, | zh_TW |
| dc.subject.keyword | Semiconductor Industry,Philadelphia Semiconductor Index,Asset Bubbles,GSADF Test,Machine Learning,Bubble Detection, | en |
| dc.relation.page | 53 | - |
| dc.identifier.doi | 10.6342/NTU202500316 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-02-06 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 國際企業學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 國際企業學系 | |
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