政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/136326

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 118761/149825 (79%)
Visitors : 80972168 Online Users : 2107

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope

please add "double quotation mark" for query phrases to get precise results

please goto advance search for comprehansive author search

Adv. Search

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

Goto mobile version

政大機構典藏 > 商學院 > 財務管理學系 > 學位論文 > Item 140.119/136326

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136326

Title:	線性與非線性模型對IPO折價預測能力之影響 A comparison between the predictive power of linear and non-linear models in IPO underpricing
Authors:	黃怡綾 Huang, Yi-Ling
Contributors:	盧敬植 Lu, Ching-Chih 黃怡綾 Huang, Yi-Ling
Keywords:	IPO 折價預測能力機器學習多元線性迴歸加權平均最小平方法多層感知器隨機森林 IPO underpricing, Predictive power Machine learning Multiple linear regression Weighted-average least squares Multilayer perceptron Random forest feature importance
Date:	2021
Issue Date:	2021-08-04 14:43:38 (UTC+8)
Abstract:	1980-2020年間美國首次公開發行的證券，在發行第一天的收盤價相對於發行價格的平均上漲幅度為18.4%。傳統文獻研究IPO折價時多著重於尋找解釋變數，而未以預測為主要目的，並且文獻中多以線性模型為假設。但影響IPO折價的因素很多，彼此也可能以不同形式影響IPO折價，線性的假設未必能提供模型最好的預測能力。近來也有研究使用機器學習方法，發現機器學習模型能夠很好地預測IPO折價。故本研究將針對線性與非線性的變數挑選方式與函數形式對模型預測能力的影響進行探討。在函數形式方面，研究使用多元線性迴歸與非線性的多層感知器模型做比較，變數的挑選方法則是用線性假設下的加權平均最小平方法以及沒有線性假設的隨機森林特徵重要程度這兩種方法來比較。研究發現加權平均最小平方法所找出的變數較適用於多元線性迴歸模型，而利用隨機森林特徵重要程度所找出之變數較適用於多層感知器模型，但此兩種組合在IPO折價的預測能力並無顯著差異。 IPO underpricing has existed for a long time. The average IPO underpricing is 18.4% in the US stock market in 1980-2020. Conventional IPO studies focused on the explanatory power of the variables often used linear regression as the selected model. However, there may be variables having non-linear explanatory power. Studies show that machine learning methods provide good predictive power in IPO underpricing. This paper analyses the predictive power of linear and non-linear methods in IPO underpricing. Weighted-average least squares (WALS) and multiple linear regression are used to evaluate the performance of linear methods, while random forest feature importance and multilayer perception (MLP) are used to assess the performance of non-linear methods. Results show that when multiple linear regression is selected as the model, WALS is a more appropriate variables selection method than random forest feature importance. Besides, random forest feature importance is a more suitable variables selection method for MLP. However, the two combinations show no statistically significant difference in the predictive power of IPO underpricing.
Reference:	Aggarwal, R., Prabhala, N. R., & Puri, M. (2002). Institutional allocation in initial public offerings: Empirical evidence. The Journal of Finance, 57(3), 1421-1442. Aggarwal, R. K., Krigman, L., & Womack, K. L. (2002). Strategic IPO underpricing, information momentum, and lockup expiration selling. Journal of financial economics, 66(1), 105-137. Aruǧaslan, O., Cook, D. O., & Kieschnick, R. (2004). Monitoring as a motivation for IPO underpricing. The Journal of Finance, 59(5), 2403-2420. Bradley, D. J., & Jordan, B. D. (2002). Partial adjustment to public information and IPO underpricing. Journal of Financial and Quantitative Analysis, 595-616. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Butler, A. W., Keefe, M. O. C., & Kieschnick, R. (2014). Robust determinants of IPO underpricing and their implications for IPO research. Journal of Corporate Finance, 27, 367-383. Carter, R., & Manaster, S. (1990). Initial public offerings and underwriter reputation. The Journal of Finance, 45(4), 1045-1067. Carter, R. B., Dark, F. H., & Singh, A. K. (1998). Underwriter reputation, initial returns, and the long‐run performance of IPO stocks. The Journal of Finance, 53(1), 285-311. Cliff, M. T., & Denis, D. J. (2004). Do initial public offering firms purchase analyst coverage with underpricing? The Journal of Finance, 59(6), 2871-2901. Corwin, S. A., & Schultz, P. (2005). The role of IPO underwriting syndicates: Pricing, information production, and underwriter competition. The Journal of Finance, 60(1), 443-486. Edelen, R. M., & Kadlec, G. B. (2005). Issuer surplus and the partial adjustment of IPO prices to public information. Journal of financial economics, 77(2), 347-373. Esfahanipour, A., Goodarzi, M., & Jahanbin, R. (2016). Analysis and forecasting of IPO underpricing. Neural Computing and Applications, 27(3), 651-658. Habib, M. A., & Ljungqvist, A. P. (2001). Underpricing and entrepreneurial wealth losses in IPOs: Theory and evidence. The Review of Financial Studies, 14(2), 433-458. Hanley, K. W. (1993). The underpricing of initial public offerings and the partial adjustment phenomenon. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366. Jain, B. A., & Nag, B. N. (1995). Artificial neural network models for pricing initial public offerings. Decision Sciences, 26(3), 283-302. Kim, M., & Ritter, J. R. (1999). Valuing ipos. Journal of financial economics, 53(3), 409-437. Ljungqvist, A. (2007). IPO underpricing. Handbook of empirical corporate finance, 375-422. Ljungqvist, A. P., & Wilhelm Jr, W. J. (2002). IPO allocations: Discriminatory or discretionary? Journal of financial economics, 65(2), 167-201. Loughran, T., & Ritter, J. (2004). Why has IPO underpricing changed over time? Financial Management, 5-37. Lowry, M., & Murphy, K. J. (2007). Executive stock options and IPO underpricing. Journal of financial economics, 85(1), 39-65. Lowry, M., Officer, M. S., & Schwert, G. W. (2010). The variability of IPO initial returns. The Journal of Finance, 65(2), 425-465. Lowry, M., & Schwert, G. W. (2002). IPO market cycles: Bubbles or sequential learning? The Journal of Finance, 57(3), 1171-1200. Lowry, M., & Shu, S. (2002). Litigation risk and IPO underpricing. Journal of financial economics, 65(3), 309-335. Magnus, J. R., & Durbin, J. (1999). Estimation of regression coefficients of interest when other regression coefficients are of no interest. Econometrica, 67(3), 639-643. Magnus, J. R., Powell, O., & Prüfer, P. (2010). A comparison of two model averaging techniques with an application to growth empirics. Journal of econometrics, 154(2), 139-153. Purnanandam, A. K., & Swaminathan, B. (2004). Are IPOs really underpriced? The Review of Financial Studies, 17(3), 811-848. Quintana, D., Luque, C., & Isasi, P. (2005). Evolutionary rule-based system for IPO underpricing prediction. Proceedings of the 7th annual conference on Genetic and evolutionary computation, Quintana, D., Sáez, Y., & Isasi, P. (2017). Random forest prediction of IPO underpricing. Applied Sciences, 7(6), 636. Ritter, J. R. (1984). The" hot issue" market of 1980. Journal of Business, 215-240. Slovin, M. B., Sushka, M. E., & Ferraro, S. R. (1995). A comparison of the information conveyed by equity carve-outs, spin-offs, and asset sell-offs. Journal of financial economics, 37(1), 89-104. Smart, S. B., & Zutter, C. J. (2003). Control as a motivation for underpricing: a comparison of dual and single-class IPOs. Journal of financial economics, 69(1), 85-110.
Description:	碩士國立政治大學財務管理學系 108357001
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108357001
Data Type:	thesis
DOI:	10.6814/NCCU202100740
Appears in Collections:	[財務管理學系] 學位論文

Files in This Item:

File	Description	Size	Format
700101.pdf		1927Kb	Adobe PDF2	2	View/Open

All items in 政大典藏 are protected by copyright, with all rights reserved.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - Feedback