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系統識別號 U0002-2512201116154000
中文論文名稱 運用計算智慧預測上市首日收盤價與投資組合最適化
英文論文名稱 Adopting Computation Intelligence to Forecast Initial Listing Closing Price and Investment Portfolio Optimization
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 100
學期 1
出版年 101
研究生中文姓名 周世昊
研究生英文姓名 Shi-hao Chou
學號 892560441
學位類別 博士
語文別 中文
口試日期 2011-12-20
論文頁數 83頁
口試委員 指導教授-林蒼祥
指導教授-倪衍森
委員-吳榮義
委員-魏啟林
委員-莊忠柱
委員-蔡蒔銓
委員-李沃牆
委員-涂登才
中文關鍵字 初次公開募集發行(IPO)  倒傳遞類神經網路(BPNN)  適應性類神經模糊推論系統(ANFIS)  條件風險值(CVaR) 
英文關鍵字 Initial Public Offering (IPO)  Back Propagation Neural Network (BPNN)  Adaptive Neuro-Fuzzy Inference System (ANFIS)  Conditional Value at Risk (CVaR) 
學科別分類
中文摘要 台灣於2004年3月1日以前,受到每一交易日漲跌幅7%限制,無法研究上市首日收盤價是否已充分反應上市公司真實內涵價值,亦無從判斷承銷價格的合理水準,但俟後實施上市首五日無漲跌幅制度與證券承銷參考價新制度,本文得以取樣新制之上市股票,觀察上市首日收盤價與證券承銷價格差異,並分析此上市首日超常報酬對此項活動參與者間之利益得失。本文運用倒傳遞類神經網路及適應性類神經模糊推論系統,預測上市首日收盤價,從而據以制定出最能平衡各方利益的證券承銷價格,實證結果顯示兩種類神經網路的準確率皆大幅超越新制度之承銷價,而其中又以適應性類神經模糊推論系統表現較為優異。衡量績效亦發現,兩種類神經網路的預測誤差都相當小。
延續對新制度初次上市股票的研究,探討以此類股票納為投資組合,其績效是否出現長期不佳的現象,實證顯示,建構投資組合初期,因受限投資標的數額,當處於市場空頭期間,因無法有效分散風險,致表現略遜於大盤報酬,惟上市家數逐漸增多,可納為投資組合標的亦隨之增多後,實證顯示其績效優於市場大盤表現,本文以Markowitz及CVaR方法建置投資組合,實證評估其報酬率績效,發現CVaR之500交易日模型,在平均及累積報酬均為最佳,且其Sharpe指標也是唯一為正值者。
英文摘要 Prior to March 1st, 2004, the Taiwan stock market was subject to the daily 7% up/down limit. Hence, it was not possible to research whether the closing price of the first listing day of initial public offerings (IPOs) had been fully reflected their intrinsic value. After promulgating the new rule which sets the trading of the first five listing days without a price limit, we can observe the gap between an IPO price and the first listing day’s closing price; academia refers to this gap as “IPO under-pricing”. In order to assist involved parties in underwriting activities to find out the best IPO price for their interest, this paper adopts the BPNN and ANFIS model to forecast the first trading closing price of an IPO. By referencing the forecast price, all stakeholders can consider a reasonable price level. The empirical study shows both BPNN and ANFIS possess the superior forecasting power. Both tracking errors are under the projected range, and the ANFIS shows greater performance than BPNN.
In further examining the new rule, this paper investigates another widely discussed topic which is the Post-IPO long-term performance. We adopt the Mean-Variance model and CVaR model to construct the portfolio. The empirical study shows that in the beginning of the sample period, the stock market was in downturn trend and too few stocks could be included in the portfolio to diversify the risk. As a result, the portfolio return underperformed when compared to the benchmark index, TAIEX. Thereafter, as more stocks were included in the portfolio, the return was significantly improved and surpassed the TAIEX by a wide margin. The empirical study shows CVaR with 500 historical trading days performing better than the TAIEX and Mean-Variance model in average and accumulated returns. The CVaR 500 possesses the only positive Sharpe ratio among all returns.
論文目次 目 錄 I
表目錄 III
圖目錄 IV
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 6
第三節 研究目的 7
第四節 研究流程 9
第二章 文獻探討 10
第一節 股票價格預測研究 10
第二節 計算智慧研究 12
第三節 投資組合最適化研究 15
第三章 運用類神經網路預測IPO股票上市首日收盤價格 19
第一節 影響新股上市價格的要素 20
第二節 類神經網路模型 22
第三節 倒傳遞類神經網路 23
第四節 適應性類神經模糊推論系統 27
第五節 實證結果分析 31
第四章 引用CVaR建構最適化之新股投資組合 50
第一節 Markowitz投資組合最適化模型 50
第二節 對CVaR限制導出投資組合最適化模型 51
第三節 實證結果分析 66
第四節 投資組合績效評估 72
第五章 結論與建議 73
第一節 結論 73
第二節 建議 76
參考文獻 78

表目錄
表 3-1 影響新股上市價格形成的指標 22
表 3-2 1BPNN、ANFIS、實際承銷價與上市首日收盤價的統計誤差結果 49
表 4-1 各交易日之投資組合報酬率狀況 70
表 4-2 1Markowitz與CVaR模型報酬績效 72

圖目錄
圖 1-1 研究流程圖 9
圖 3-1 類神經網路處理單元 22
圖 3-2 倒傳遞類神經網路架構 24
圖 3-3 ANFIS架構 28
圖 3-4 模型有3輸入與3歸屬函數,1輸出之27條模糊規則的ANFIS架構 30
圖 3-5 ANFIS訓練流程 31
圖 3-6 類神經模組之建構流程 32
圖 3-7 預測新股上市價格的類神經網路模型 34
圖 3-8 輸入變數6個,隱藏層8節點之網路訓練誤差收斂 35
圖 3-9 輸入變數6個,隱藏層10節點之網路訓練誤差收斂 35
圖 3-10 輸入變數8個,隱藏層8節點之網路訓練誤差收斂 36
圖 3-11 輸入變數8個,隱藏層10節點之網路訓練誤差收斂 36
圖 3-12 輸入變數6個,隱藏層8節點之訓練樣本迴歸 37
圖 3-13 輸入變數6個,隱藏層10節點之訓練樣本迴歸 38
圖 3-14 輸入變數8個,隱藏層8節點之訓練樣本迴歸 38
圖 3-15 輸入變數8個,隱藏層10節點之訓練樣本迴歸 39
圖 3-16 輸入變數6個,隱藏層8節點之訓練樣本結果 40
圖 3-17 輸入變數6個,隱藏層10節點之訓練樣本結果 40
圖 3-18 輸入變數8個,隱藏層8節點之訓練樣本結果 41
圖 3-19 輸入變數8個,隱藏層10節點之訓練樣本結果 41
圖 3-20 ANFIS模型架構 42
圖 3-21 ANFIS網路訓練誤差收斂過程 43
圖 3-22 變數1(上市日總股本)之歸屬函數 43
圖 3-23 變數2(上市去年度EPS)之歸屬函數 44
圖 3-24 變數3(上市前一年度EPS增長率)之歸屬函數 44
圖 3-25 變數4(上市日前月每股淨值)之歸屬函數 45
圖 3-26 變數5(上市年度EPS)之歸屬函數 45
圖 3-27 變數6(上市前1個月發行量加權股價指數漲跌)之歸屬函數 46
圖 3-28 變數7(上市前1季發行量加權股價指數漲跌)之歸屬函數 46
圖 3-29 變數8(上市前1年發行量加權股價指數漲跌)之歸屬函數 47
圖 3-30 ANFIS訓練樣本結果 47
圖 4-1 VaR與CVaR之關係 56
圖 4-2 投資組合報酬率研究之建構流程 67
圖 4-3 信賴水準90%下各投資組合報酬率曲線圖 69
圖 4-4 信賴水準95%下各投資組合報酬率曲線圖 69
圖 4-5 信賴水準99%下各投資組合報酬率曲線圖 70
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