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題名:主機板高科技司公司市值模擬研究
作者:蕭立文
作者(外文):Hsiao, Liwen
校院名稱:國立臺灣大學
系所名稱:商學研究所
指導教授:游張松
學位類別:博士
出版日期:2000
主題關鍵詞:市值類神經網路模擬事件理論價值主機板Market ValueNeural NetworkSimulationEventTheoretical ValueMother Borad
原始連結:連回原系統網址new window
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本研究以提出主機板公司市值成長率的理論模型為出發,假設公司理論價值、事件變數和市值成長率之間的存在著函數關係,並透過類神經網路模式,來模擬主機板公司的市值成長率或成長等級。從實驗結果中,驗證了以DCF 指標與25種事件變數,透過某種型態的函數,可以描述主機板公司市值成長率或等級的80%。
模擬方法的發展過程,首先收集25種事件變數與DCF指標,並將事件變數以「方向變碼」和「強度編碼」兩種方式進行整理。接著以類神經網路以及相同的樣本,同時進行兩種不同導向的模擬:第一種是成長率數值的模擬﹔第二種是成長率等級的模擬。比較兩種策略的模擬結果,發現強度編碼的數值模擬能力比方向編碼提昇很多,同時等級模擬的正負號正確率高達92%,而三等級的正確率也高達89%。
為了深入了解模擬模型的特性,研究中更進一步分析了模擬錯誤的樣本狀況,發現可以藉由適當的投資決策邏輯,來迴避模擬模型的缺點。因此,我們也針對模擬模型的應用,整理出低風險導向的投資決策邏輯,來搭配模擬結果的應用。以模擬結果搭配決策邏輯,可以高度掌握獲利機會,並可迴避大部分的風險。
最後,回顧模擬模型發展的過程與體驗,去除不必要的動作,將之整理成步驟化的程序。其中步驟包括:輸入變數的收集整理、樣本期間的考量、編碼方式的要點、類神經網路結構設計、以及變數維度確認、網路適用性實驗,以及模式預測能力的檢驗。這個程序將可供本研究推廣至其他科技業時之參考。
This research examines a new theoretical model for market value growth of motherboard technology companies. The model assumed that market value growth is function of theoretical value and events happened in market. By a series of experiments and simulations with Artificial Neural Network (ANN), the research verify DCF indicator and 25 categories of events variables, which can describe more than 80% variation of market value growth for motherboard companies.
When constructing the ANN simulation model, 25 categories of event variables and DCF indicator were collected. The events were coded with both “affect direction” and “relative strength” concurrently. Than ANN simulations were implemented and compared in two strategies, numerical simulation and growth level classification-oriented. As a result, “relative strength” is out performed than “affect direction”. At the same time, the simulation accuracy of 2 levels and 3 levels classification are up to 92% and 89%.
To investigate the characteristics and avoid the weakness of simulation model, an analysis about error samples is introduced to figure out the proper decision-support logics for investment. After test, it’s approved that the decision support logics can help investors gather profitable investment opportunities with very low risk.
At the end of the dissertation, the researcher summarize the simulation model construction methodology in a step by step procedure, including variables collection and coding, sampling period definition, ANN structure design, input variables verification and convergence test for neural model. This procedure is the key reference for implementing this research on other industries.
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