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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/77383

    Title: 運用計算智慧預測上市首日收盤價與投資組合最適化
    Other Titles: Adopting computation intelligence to forecast initial listing closing price and investment portfolio optimization
    Authors: 周世昊;Chou, Shi-hao
    Contributors: 淡江大學管理科學學系博士班
    Keywords: 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);Initial Public Offering (IPO);Back Propagation Neural Network (BPNN);Adaptive Neuro-Fuzzy Inference System (ANFIS);Conditional Value at Risk (CVaR)
    Date: 2012
    Issue Date: 2012-06-21 06:38:36 (UTC+8)
    Abstract: 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。
    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.
    Appears in Collections:[Department of Management Sciences] Thesis

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