![]() |
|
以作者查詢圖書館館藏 、以作者查詢臺灣博碩士 、以作者查詢全國書目 、勘誤回報 、線上人數:11 、訪客IP:18.118.119.135
姓名 許博惇(Bo-Dun Hsu) 查詢紙本館藏 畢業系所 財務金融學系 論文名稱 臺灣新上市櫃公司失敗風險預測
(Predicting IPO failures in Taiwan stock market)相關論文 檔案 [Endnote RIS 格式]
[Bibtex 格式]
[相關文章]
[文章引用]
[完整記錄]
[館藏目錄]
[檢視]
[下載]
- 本電子論文使用權限為同意立即開放。
- 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
- 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
摘要(中) 本文透過IPO特性變數以及興櫃市場相關變數預測IPO公司是否會於上市(櫃)後五年內下市(櫃)或轉為全額交割股。研究後發現興櫃市場相關變數的確為預測IPO 是否失敗之重要變數,加入興櫃市場變數的確能提高樣本外預測準確度,且亦能增加羅吉斯迴歸(logistic regression) 之Pseudo ?2。此外,本文使用羅吉斯迴歸、隨機森林 (random forest)
與extreme gradient boosting 方法建構模型,發現以台灣2002-2014 年預測IPO五年內失敗可能性來說,羅吉斯迴歸樣本外預測效果最佳,而模型穩定度最好的則是extreme gradient boosting 方法。摘要(英) This thesis constructs an IPO failure prediction model with IPO characteristics and the trading or financial information in Taiwan’s emerging stock market. The results indicate that the trading or financial information in Taiwan’s emerging stock market are important and significant variables to predict IPO failure risk. We find that AUC and Pseudo ?2 increase after including the variables related to Taiwan’s emerging stock market in the prediction model. In addition, we build the prediction model by logistic regression, random forest and extreme gradient boosting to predict whether IPO will fail or not in Taiwan from 2002 to 2014. The results indicate that logistic regression performs better than random forest or extreme gradient boosting in predicting IPO failures. Finally, we find that the performance of extreme gradient boosting is the most stable in the repeated cross validation. 關鍵字(中) ★ 新上市上櫃公司
★ 機器學習
★ 興櫃市場
★ IPO 失敗風險關鍵字(英) ★ IPO
★ Machine learning
★ Taiwan’s emerging stock market
★ IPO failure risk論文目次 摘要 ................................................................................................................................................... i
Abstract ............................................................................................................................................ ii
誌謝 ................................................................................................................................................. iii
目錄 ................................................................................................................................................. iv
圖目錄............................................................................................................................................. vi
表目錄............................................................................................................................................ vii
第一章 緒論 ............................................................................................................................. 1
第二章 文獻回顧..................................................................................................................... 3
2-1 IPO 相關議題研究 .......................................................................................................... 3
2-2 IPO 失敗風險研究 .......................................................................................................... 3
2-3 機器學習模型於財務領域之應用 ................................................................................ 5
2-4 興櫃市場相關資訊 ......................................................................................................... 6
第三章 資料 ............................................................................................................................. 7
3-1 資料來源 ......................................................................................................................... 7
3-2 樣本選擇 ......................................................................................................................... 7
第四章 研究方法..................................................................................................................... 9
4-1 變數說明 ......................................................................................................................... 9
4-1-1 應變數 ................................................................................................................... 9
4-1-2 自變數 ................................................................................................................. 10
4-2 模型說明 ....................................................................................................................... 15
4-2-1 羅吉斯迴歸Logistic Regression ..................................................................... 16
4-2-2 隨機森林Random Forest ................................................................................. 16
4-2-3 Extreme Gradient Boosting (XGB) ................................................................... 17
4-3 模型表現衡量 ............................................................................................................... 18
4-3-1 ROC & AUC ....................................................................................................... 18
4-3-2 Variable Importance............................................................................................ 19
4-4 假說檢驗 ....................................................................................................................... 20
4-4-1 假說一:興櫃市場相關變數為預測IPO 失敗之重要變數。 .................... 20
4-4-2 假說二:加入興櫃市場相關變數將能提升模型預測能力。 .................... 20
4-4-3 假說三:機器學習模型之預測能力將優於羅吉斯迴歸模型。 ................ 21
第五章 研究結果................................................................................................................... 22
5-1 敘述統計 ....................................................................................................................... 22
5-1-1 台灣新上市(櫃)市場 ........................................................................................ 22
5-1-2 敘述統計 ............................................................................................................ 23
5-2 模型表現 ....................................................................................................................... 25
5-2-1 羅吉斯迴歸(Logistic Regression).................................................................... 25
5-2-2 隨機森林(Random Forest) ............................................................................... 27
5-2-3 Extreme Gradient Boosting................................................................................ 28
5-3 加入興櫃市場相關變數之研究結果........................................................................... 28
5-3-1 變數重要性排序 ............................................................................................... 28
5-3-2 加入興櫃市場相關變數後AUC 與Pseudo ?2變化 ................................... 29
第六章 結論 ........................................................................................................................... 31
參考文獻 ....................................................................................................................................... 56
附錄 ................................................................................................................................................ 60
附錄 1 吉尼係數(Gini Coefficient)................................................................................... 60
附錄 2 重複交叉驗證 ........................................................................................................ 61參考文獻 Aggarwal, R. K., Krigman, L., and Womack, K. L., 2002, Strategic IPO underpricing,
information momentum, and lockup expiration selling, Journal of Financial Economics,
66(1), 105-137.
Akaike, H., 1974, A new look at the statistical model identification, IEEE Transactions on
Automatic Control, 19(6), 716-723.
Allen, F., Faulhaber, G. R., 1989, Signaling by underpricing in the IPO market, Journal of
Financial Economics, 23(2), 303-323.
Altman, E. I., 1968, Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy, Journal of Finance, 23(4), 589-609.
Amihud, Y., 2002, Illiquidity and stock returns: cross-section and time-series effects, Journal
of Financial Markets, 5(1), 31-56.
Beaver, W. H., 1966, Financial ratios as predictors of failure, Journal of Accounting Research,
71-111.
Benninga, S., Helmantel, M., and Sarig, O., 2005, The timing of initial public offerings,
Journal of Financial Economics, 75(1), 115-132.
Breiman, L., 1999, Prediction games and arcing algorithms, Neural Computation, 11(7),
1493-1517.
Carter, R., & Manaster, S., 1990, Initial public offerings and underwriter reputation, Journal
of Finance, 45(4), 1045-1067.
Chang, C., Chiang, Y.-M., Qian, Y., and Ritter, J. R., 2017, Pre-market trading and IPO
pricing, Review of Financial Studies, 30(3), 835-865.
Colak, G., Fu, M., and Hasan, I., 2018, Predicting IPO failures using machine learning
technique, Working Paper.
Cornett, M. M., Marcus, A. J., Saunders, A., and Tehranian, H., 2007, The impact of
institutional ownership on corporate operating performance, Journal of Banking and
Finance, 31(6), 1771-1794.
Demers, E., & Joos, P., 2007, IPO failure risk, Journal of Accounting Research, 45(2),
333-371.
Fama, E. F., & French, K. R., 2004, New lists: Fundamentals and survival rates, Journal of
Financial Economics, 73(2), 229-269.
Grinblatt, M., & Hwang, C. Y., 1989, Signalling and the pricing of new issues. Journal of
Finance, 44(2), 393-420.
Hensler, D. A., Rutherford, R. C., and Springer, T. M., 1997, The survival of initial public
offerings in the aftermarket, Journal of Financial Research, 20(1), 93-110.
Ho, T. K., 1995, Random decision forests, in 3rd International Conference on Document
Analysis and Recognition (ICDAR′95), 278-282.
Ibbotson, R. G., 1975, Price performance of common stock new issues, Journal of Financial
Economics, 2(3), 235-272.
Jain, B. A., & Kini, O., 2000, Does the presence of venture capitalists improve the survival
profile of IPO firms?, Journal of Business Finance and Accounting, 27(9‐10),
1139-1183.
Kumar, M., & Thenmozhi, M., 2006, Forecasting stock index movement: A comparison of
support vector machines and random forest, IndianInstitute of Capital Markets 9th
Capital Markets Conference Paper.
Lin, C. Y., Lee, H. T., and Lee, C. L., 2008, one more step, some more performance? An
empirical study on initial public offerings in the Taiwan emerging stock market,
Emerging Markets Finance and Trade, 44(4), 6-18.
Loughran, T., & Ritter, J. R., 1995, The new issues puzzle, Journal of Finance, 50(1), 23-51.
Lowry, M., & Schwert, G. W., 2002, IPO market cycles: Bubbles or sequential learning?,
Journal of Finance, 57(3), 1171-1200.
Megginson, W. L., & Weiss, K. A., 1991, Venture capitalist certification in initial public
offerings, Journal of Finance, 46(3), 879-903.
Michaely, R., & Shaw, W. H., 1995, Does the choice of auditor convey quality in an initial
public offering?, Financial Management, 15-30.
Ohlson, J. A., 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of
Accounting Research, 109-131.
Rabinovitch, R., Silva, A. C., and Susmel, R., 2003, Returns on ADRs and arbitrage in
emerging markets, Emerging Markets Review, 4(3), 225-247.
Rapach, D. E., Strauss, J. K., Tu, J., and Zhou, G., 2019, Industry return predictability: A
Machine Learning Approach, Journal of Financial Data Science, 1(3), 9-28.
Ritter, J. R., 1984, The" hot issue" market of 1980, Journal of Business, 215-240.
Ritter, J. R., 1991, The long‐run performance of initial public offerings, Journal of Finance,
46(1), 3-27.
Rock, K., 1986, Why new issues are underpriced, Journal of Financial Economics, 15(1-2),
187-212.
Seguin, P. J., & Smoller, M. M., 1997, Share price and mortality: An empirical evaluation of
newly listed Nasdaq stocks, Journal of Financial Economics, 45(3), 333-363.
Stoll, H. R., & Curley, A. J., 1970, Small business and the new issues market for equities,
Journal of financial and quantitative analysis, 5(3), 309-322.
Titman, S., & Trueman, B., 1986, Information quality and the valuation of new issues,
Journal of Accounting and Economics, 8(2), 159-172.
Uzun, H., Szewczyk, S. H., and Varma, R., 2004, Board composition and corporate fraud,
Financial Analysts Journal, 60(3), 33-43.
Yung, C., Ç olak, G., and Wang, W., 2008, Cycles in the IPO market, Journal of Financial
Economics, 89(1), 192-208.
王朝仕、陳振遠, 2008,「申購積極性對新上市公司股票績效的影響」, 管理學報, 25(2),
245-267.
姜堯民、戴維芯, 2016,「台灣股票初次上市櫃相關研究文獻回顧」, 經濟論文叢刊, 44(1),
77-125.
陳安琳、李文智、林宗源, 1999,「新上市公司股票之發行折價-代理成本與公司控制之研
究」, 中國財務學刊, 6(3), 1-23.
陳鎮東、謝政翰, 2019, 「應用機器學習與模糊推論於股價漲跌預測之研究」, 資訊管理
學報, 26(2), 153-177.
鄭仁杰、江彌修, 2019,「漫步於隨機森林-輔以多數決學習的台股指數期貨交易策略」, 經
濟論文, 47(3), 395-448.指導教授 高櫻芬(Yin‑Feng Gau) 審核日期 2020-7-8 推文 plurk
funp
live
udn
HD
myshare
netvibes
friend
youpush
delicious
baidu
網路書籤 Google bookmarks
del.icio.us
hemidemi
myshare