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題名 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作者 黃怡綾
Huang, Yi-Ling貢獻者 盧敬植
Lu, Ching-Chih
黃怡綾
Huang, Yi-Ling關鍵詞 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日期 2021 上傳時間 4-Aug-2021 14:43:38 (UTC+8) 摘要 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.參考文獻 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. 描述 碩士
國立政治大學
財務管理學系
108357001資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108357001 資料類型 thesis dc.contributor.advisor 盧敬植 zh_TW dc.contributor.advisor Lu, Ching-Chih en_US dc.contributor.author (Authors) 黃怡綾 zh_TW dc.contributor.author (Authors) Huang, Yi-Ling en_US dc.creator (作者) 黃怡綾 zh_TW dc.creator (作者) Huang, Yi-Ling en_US dc.date (日期) 2021 en_US dc.date.accessioned 4-Aug-2021 14:43:38 (UTC+8) - dc.date.available 4-Aug-2021 14:43:38 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2021 14:43:38 (UTC+8) - dc.identifier (Other Identifiers) G0108357001 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/136326 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 財務管理學系 zh_TW dc.description (描述) 108357001 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章 緒論 7第一節 研究動機與目的 7第二節 研究方法與章節安排 7第二章 文獻探討 9第一節 IPO折價原因 9第二節 IPO折價研究使用之模型 11第三章 研究方法 13第一節 資料蒐集 13第二節 變數處理與說明 14第三節 研究流程 18第四章、實證結果 19第一節 敘述性分析 19第二節 加權平均最小平方法 19第三節 線性與非線性模型比較 23第四節 WALS變數組合與所有變數的多層感知器模型比較 27第五節 隨機森林變數重要程度 29第六節 WALS變數組合與隨機森林變數組合模型比較 29第五章、結論與後續建議 36第一節 研究結論 36第二節 研究限制與後續建議 37參考文獻 38附錄 41 zh_TW dc.format.extent 1974143 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108357001 en_US dc.subject (關鍵詞) IPO 折價 zh_TW dc.subject (關鍵詞) 預測能力 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 多元線性迴歸 zh_TW dc.subject (關鍵詞) 加權平均最小平方法 zh_TW dc.subject (關鍵詞) 多層感知器 zh_TW dc.subject (關鍵詞) 隨機森林 zh_TW dc.subject (關鍵詞) IPO underpricing, en_US dc.subject (關鍵詞) Predictive power en_US dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Multiple linear regression en_US dc.subject (關鍵詞) Weighted-average least squares en_US dc.subject (關鍵詞) Multilayer perceptron en_US dc.subject (關鍵詞) Random forest feature importance en_US dc.title (題名) 線性與非線性模型對IPO折價預測能力之影響 zh_TW dc.title (題名) A comparison between the predictive power of linear and non-linear models in IPO underpricing en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202100740 en_US
