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姓名 邱清顯(Ching-hsien Chiu) 電子郵件信箱 E-mail 資料不公開
畢業系所 企業管理學系研究所(Business Management)
畢業學位 博士(Ph.D.) 畢業時期 95學年第1學期
論文名稱(中) 不確定下研發投資決策:實質選擇權賽局方法之應用
論文名稱(英) R &D Investment Decisions under Uncertainty:
An Application of a Real Options Game Approach
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    摘要(中) 本文假設企業之R&D投資案未來現金流量,依循算術布朗運動與布阿松(跳躍)過程。本研究考量不同隨機影響,分別是常態分佈、負指數分佈、與Laplace分佈,進行不同市場結構下之R&D投資決策評估。
    本研究的第一個模型,旨在建構不同隨機影響下獨占R&D投資決策。研究結果與Cossin et al. (2002)不同,發現Cossin et al.在評估一次性投入投資決策價值、兩階段投資決策價值及撤資決策價值,存在低估情形。而敏感度分析結果顯示: (1)對一次性投入R&D投資決策價值呈正向關係的參數,為專案現金流量成長率、突發事件發生頻率、常態分佈平均數、常態分佈標準差,期初投資;(2)而與撤資決策價值正向關係的參數,為專案現金流量成長率、突發事件發生頻率、常態分佈平均數、常態分佈標準差。
    本研究的第二個模型,將獨占擴展為雙占,旨建立不同隨機影下雙占R&D投資決策。研究結果符合Tsekrekos (2003)的預期,不確定性愈大,雙占企業之投資門檻愈高。而敏感度分析結果顯示:(1)與領導者研發投資門檻正向關係的參數,為標準差、突發事件發生頻率、折現率、投資成本、常態分佈平均數與常態分佈標準差。而與領導者研發投資門檻呈負向關係的參數為成長率及市占率;(2)與追隨者研發投資門檻正向關係的參數,為標準差、市占率、突發事件發生頻率、折現率、投資成本、常態分佈平均數與常態分佈標準差。而與追隨者研發投資門檻呈負向關係的參數,為成長率。
    本研究第三個模型,將模型擴展為寡占,旨在建立不同隨機影響下寡占R&D投資決策。研究結果符合Grenadier(2002)的預期,在其他情況不變下,如果產業競爭程度越高,投資門檻值愈低。即在產業企業數愈多,寡占產業中企業有提前投資的威脅存在。而敏感度分析結果顯示:(1)與寡占研發投資門檻正向關係的參數,為標準差、突發事件發生頻率、折現率、單位投資成本、產業供給;(2)與寡占研發投資門檻呈負向關係的參數,為產業企業數目、成長率及需求彈性。
    摘要(英) This dissertation assumes the R&D investment future cash flows of a firm which follows an arithmetic Brownian motion and Poisson (jump) process. This study evaluates the R&D investment decisions under different market structure while considering the stochastic impact scales are the normal, negative exponential, and Laplace distributions, respectively.
    The first model of this dissertation aims to build monopoly R&D investment decisions under different stochastic impact scales. The result of this study is different from Cossin et al. (2002), since it shows that the outcome of Cossin et al. (2002) has underestimated decision values in assessing lump-sum investment, staging investment, and liquidation decisions.
    Sensitivity analysis reveals the following: (1) the positive relation parameter for the lump-sum investment is the cash flow growth rate of project, frequency of jump event, time of jump event, mean and deviation of normal distribution, and initial cost. (2) The positive relation parameter for liquidation decisions is the cash flow growth rate of project, frequency of jump event, time of jump event, and mean and deviation of normal distribution.
    The second model of this dissertation extends the monopoly to duopoly, and it aims to build the duopoly R&D investment decisions under different stochastic impact scales. The result of the study accords with Tsekrekos (2003) that with more uncertainty, there are more duopoly investment thresholds.
    Sensitivity analysis reveals the following: (1) the positive relation parameter for the leading R&D investment thresholds is deviation, frequency of jump event, discount rate, investment cost, and mean and deviation of normal distribution, while the negative relation parameter is the growth rate and market share. (2) The positive relation parameter for the follower R&D investment thresholds is deviation, market share, frequency of jump event, discount rate, investment cost, and mean and deviation of normal distribution, while the negative relation parameter is the growth rate.
    The third model of this dissertation extends to oligopoly, and it aims to build the oligopoly R&D investment decisions under different stochastic impact scales. The result of the study accords with the expectancy of Grenadier (2002), that while other things being equal, the more industry's competition degree, the lower oligopoly investment thresholds. Namely the higher the numbers of firms in an industry, those oligopoly firms have more incentives to invest early.
    Sensitivity analysis shows the following: (1) The positive relation parameter for the oligopoly R&D investment thresholds is deviation, frequency of jump event, discount rate, unitary investment cost, and mean and oligopoly supply, while the negative relation parameter is the growth rate and market share. (2) The negative relation parameter is the number of firms in the industry, growth rate, and demand elasticity.
    關鍵字(中)
  • 隨機影響
  • R&D投資
  • 實質選擇權
  • 實質選擇權賽局
  • 關鍵字(英)
  • real options game
  • stochastic impact
  • R&D investment
  • real options
  • 論文目次 致謝詞                         I
    中文摘要                         II
    英文摘要                         Ⅳ
    第一章、緒論                     1
    第一節 研究動機                     1
    第二節 研究目的                     4
    第三節 論文架構                     5
    第四節 研究限制                     8
    第二章、文獻回顧                     9
    第一節 有關實質選擇權在R&D投資研究            9
    第二節 有關實質選擇權賽局的研究           14
    第三章、不確定下R&D投資決策—獨占模型       20
    第一節 模式建構與模型擴展                20
    第二節 數值分析與敏感度分析           31
    第三節 本章結論                    43
    第四章、不確定下R&D投資決策—雙占模型       45
    第一節 模式建構與追隨者之R&D投資價值函數       45
    第二節 領導者之R&D投資價值函數           48
    第三節 數值分析與敏感度分析           51
    第四節 本章結論                    61
    第五章、不確定下R&D投資決策—寡占模型       63
    第一節 模式建構與寡占下R&D投資價值函數       63
    第二節 數值分析與敏感度分析           67
    第三節 本章結論                    77
    第六章、研究結論與未來研究方向           79
    第一節 研究結論                    79
    第二節 未來研究方向                81
    參考文獻                         83
    附錄一 第一折現因子與第二折現因子證明       87
    附錄二 符號表                    90
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    口試委員
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  • 口試日期 2006-10-21 繳交日期 2006-12-19

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