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題名:模糊理論在現金流量折現問題之應用
作者:林惠文
作者(外文):Huei-Wen Lin
校院名稱:淡江大學
系所名稱:管理科學研究所博士班
指導教授:張紘炬
學位類別:博士
出版日期:2005
主題關鍵詞:模糊理論現金流量折現模式評價永續年金資本預算淨現值法三角模糊數Lambda符號距離方法均勻收斂Fuzzy theoryDiscounted cash flow modelValuationPerpetuityCapital budgetingNet present value approachTriangular fuzzy numberLambda signed distance methodUniform convergence
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本文提出模糊理論在現金流量折現問題的應用。以現金流量折現模式的基本架構為基礎,藉由模糊集合理論的導入,來探討財務管理領域中運用現金流量模式評價的相關議題。
第三章主要發展出模糊邏輯系統來延伸傳統現金流量折現模式,其中模糊現金流量及折現率同時被考慮在模式中。為了明確地構建出一個較符合實際的評價模型,模式中具不確定性的參數將被模糊化為三角模糊數來量化及評估公司或金融資產的內在真實價值。利用 符號距離方法解模糊化後,可證明本章所提出之模糊現金流量折現模式為傳統現金流量折現模式的一種延伸。
延續第三章之架構,第四章將現金流量折現模式轉換為永續年金模式,進一步分析不同現金流量形式的永續年金現值。相同地,不確定性與決策者評估態度等基本概念被同時用來解釋不精確現金流量、必要報酬率及成長率等參數,並同時模糊化為三角模糊數。結果提供決策者在評估一般永續年金及成長型永續年金的價值,同時亦說明模糊永續年金模式為傳統永續年金模式的一種延伸。
第五章以模糊現金流量折現模式為基礎,進一步發展出模糊資本預算模式,模式中涵蓋未來各期間之不確定現金流量及必要報酬率。藉由模糊資本預算模式的推導與模擬分析結果,說明此一模糊模式對財務管理者在分析資本預算時是較有實用價值的。
本文透過嚴謹及具體的數學分析,導入模糊集合理論,指出模糊現金流量折現模式為傳統評價模式的一種合理的延伸,在不失模式簡單易懂的本質下發展出較適合一般財務管理者使用之評價模型及資本預算模型。分析結果亦可解釋過去資料無法完全精確預測複雜財務環境中之不確定資訊現象。另外,本文所推導之定理皆以數值範例加以說明模糊觀點下各個模糊模式的義涵,並討論真實價值與資本預算決策是如何受到不確定現金流量水準、公司成長率及必要報酬率的影響。因此,本文之主要貢獻在於構建較實際的現金流量折現模式,並將其應用於財務管理領域的一些重要模型。
This thesis puts forward some applications of fuzzy theory in discounted cash flow (DCF) problems. On the basis of the basic frameworks of financial valuation models, the fuzzy set theory is introduced to deal with the related topics of DCF models such as perpetuity and capital budgeting.
A fuzzy logic system has been developed and it can be concluded that it is some extensions of the classical DCF models. In order to explicitly construct a more appropriate valuation model, uncertain information will be fuzzified as triangular fuzzy numbers to quantify and evaluate the intrinsic value of a company or a financial asset. Using signed distance method to defuzzify the fuzzy model, we find that the fuzzy discounted cash flow (FDCF) model proposed in this thesis is some extensions of classical (crisp) model and it is considered to be more suitable to capture the imprecise elements of valuation.
Following the basic framework of Chapter 3, Chapter 4 stresses the DCF model on the analysis of perpetuity model, in which the impreciseness and the decision maker’s attitude toward the estimate of the uncertain parameters are simultaneously incorporated into the descriptions of different cash flow streams, required rate of return and growth rate. Similar to Chapter 3, the uncertain information will be fuzzified as triangular fuzzy numbers; therefore it would be more realistic for typical decision maker to analyze the present values of ordinary and growing perpetuities. We also find that the fuzzy perpetuity models are one extension of crisp perpetuity models.
In Chapter 5, we further develop a fuzzy capital budgeting model by extending the classical net present value (NPV) method that takes the vague future cash flows and required rates of returns in different time periods into account. The results are more useful and practical for financial manager to analyze the capital budgeting decision of firms by means of the derivations of fuzzy model with numerical simulation.
Through conscientious and concrete mathematical analyses, this thesis addressed that the FDCF model is one reasonable extension of the crisp models. In addition, numerical examples are also used to illustrate each theorem in this thesis. In summary, the main contributions of this thesis are to construct the easier understand and more realistic FDCF model and then apply it to extend some important valuation models in financial management without losing the essence of original models.
[1] Biddle, G. C., Bowen, R. M., and Wallace, J. S., (1999), “Evidence on EVA,” Journal of Applied Corporate Finance, Vol. 12, pp. 69-79.
[2] Bierman, H., (1993), “Capital budgeting in 1993: A survey,” Financial Management, Autumn 24.
[3] Borgonovo, E. and Peccati, L., (2004), “Sensitivity analysis in investment project evaluation,” International Journal of Production Economics, Vol. 90, pp. 17-25.
[4] Brand, L., (1955)., Advance Calculus: An Introduction to Classical Analysis. New York.
[5] Brealey, R. A. and Myers, S. C., (2000), Principles of Corporate Finance, 6th ed. McGraw-Hill, New York.
[6] Brigham, E. F., (1992), Fundamentals of Financial Management, The Dryden Press, New York.
[7] Brigham, E. F. and Ehrhardt, M. C., (2005), Financial Management, 11th ed. Thomson.
[8] Buckley, J. J., (1987), “The fuzzy mathematics of finance”, Fuzzy Sets and Systems, Vol. 21, pp. 257-273.
[9] Copeland, T., Koller, T., and Murrin, J., (1994), Valuation: Measuring and Managing the Value of Companies, Second Edition, Mckinsey and Company, Inc.
[10] Dixit, A. K. and Pindyck, R. S., (1994), Investment under Uncertainty, Princeton University Press, Princeton, NJ.
[11] Dourra, H. and Siy, P., (2002), “Investment using technical analysis and fuzzy logic,” Fuzzy Sets and Systems, Vol. 127, pp. 221-240.
[12] Francis, J., Olsson, P., and Oswald, J., (2000), “Comparing accuracy and explainability of dividend, free cash flow and abnormal earnings equity value estimates,” Journal of Accounting Research, pp. 45-70.
[13] Gordon, M. J., (1962), The Investment, Financing, and Valuation of the Corporation, Homewood, Illinois: Richard D. Irwin.
[14] Graham, J. R. and Harvey, C. R., (2001), “The theory and practice of corporate finance: evidence from the field,” Journal of Financial Economics, Vol. 60 (2-3), pp. 187-243.
[15] Hill, R. M. and Pakkala, T. P. M., (2005), “A discounted cash flow approach to the base stock inventory model,” International Journal of Production Economics, Vol 93-94, pp. 439-445.
[16] Hodder, J. E. and Riggs, H. E., (1984), “Pitfalls in evaluating risky projects,” Harvard Business Review, Vol. 62-1, pp. 26-30.
[17] Hurley, W. J. and Johnson, L. D., (1994), “A realistic dividend valuation model,” Financial Analysts Journal, July-August, pp. 50-54.
[18] Kahraman, C., Ruan, D., and Tolga, E., (2002), “Capital budgeting techniques using discounted fuzzy versus probabilistic cash flows,” Information Sciences, Vol. 142, pp. 57-76.
[19] Kaplan, R. S., (1986), “Must CIM be justified by faith alone?” Harvard Business Review, Vol. 64-2, pp. 87-95.
[20] Kaufmann, A. and Gupta, M. M., (1991), Introduction to Fuzzy Arithmetic: Theory and Applications, Van Nostrand Reinhold, New York.
[21] Kuchta, D., (2000), “Fuzzy capital budgeting,” Fuzzy Sets and Systems, Vol. 111, pp. 367-385.
[22] Kuo, R. J., Chen, C. H., and Hwang, Y. C., (2001), “An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network”, Fuzzy Sets and Systems, Vol. 118, pp. 21-45.
[23] Leibowitz, M. L. and Kogelman, S., (1994), “The growth illusion: the P/E ‘cost’ of earnings growth,” Financial Analysts Journal, March-April, pp. 36-48.
[24] Liang, P. and Song, F., (1994), “Computer-aided risk evaluation system for capital investment,” Omega, Vol. 22 (4), pp. 391-400.
[25] Li Calzi, M., (1990), “Towards a general setting for the fuzzy mathematics of finance,” Fuzzy Sets and Systems, Vol. 35, pp. 265-280.
[26] Magni, C. A., (2002), “Investment decisions in the theory of finance: some antinomies and inconsistencies,” European Journal of Operational Research, Vol. 137, pp. 206-217.
[27] Myers, S. C., (1984), “Finance theory and finance strategy,” Interfaces, Vol. 14-1, pp. 126-137.
[28] Penman, S. H. and Sougiannis, T., (1998), “A comparison of dividend, cash flow, and earnings approaches to equity valuation,” Contemporary Accounting Research, pp. 343-383.
[29] Plenborg, T., (2002), “Firm valuation: comparing the residual income and discounted cash flow approaches,” Scandinavian Journal of Management, Vol. 18, pp. 303-318.
[30] Pu, P. M. and Liu, Y. M., (1980), “Fuzzy topology 1, neighborhood structure of a fuzzy point and Moore-smith convergence,” Journal of Mathematical Analysis and Applications, Vol. 76, pp. 571-599.
[31] Rao, R. K. S., (1992), Financial Management, MacMillan, New York.
[32] Ross, S. A., Westerfield, R., and Jaffe, J. F., (1993), Corporate Finance, 3th ed. Irwin, Homewood.
[33] Sharpe, W. F., Alexander, G. J., and Bailey, J. V., (1999), Investments, Englewood Cliffs Prentice-Hall Inc.
[34] Shiller, R. J., (1981), “Do stock prices move too much to be justified by subsequent change in dividends?” American Economic Review, Vol. 71, pp. 421-436.
[35] Slater, S. F., Reddy, V. K., and Zwirlein, T. J., (1998), “Evaluating strategic investments: complementing discounted cash flow analysis with options analysis,” Industrial Marketing Management, Vol. 27, pp. 447-458.
[36] Sorensen, E. H. and Williamson, D. A., (1985), “Some evidence on the value of dividend discount models,” Financial Analysts Journal, November-December, pp. 60-69.
[37] Stewart, G. B., (1991), The quest for value: the EVATM Management Guide HarperCollins, Publishers Inc.
[38] Turner S. and Morrell, P., (2003), “An evaluation of airline beta values and their application in calculating the cost of equity capital,” Journal of Air Transport Management, Vol. 9, pp. 201-209.
[39] Wang, Y. F., (2002), “Predicting stock price using fuzzy grey prediction system,” Expert Systems with Applications, Vol. 22, pp. 33-39.
[40] Wang, Y. F., (2003), “Mining stock price using fuzzy rough set system,” Expert Systems with Applications, Vol. 24, pp. 13-23.
[41] Yao, J. S., Chen, M. S., and Lin, H. W., (2005),” Valuation by using a fuzzy discounted cash flow model,” Expert Systems with Applications, Vol. 28, pp. 209-222.
[42] Yao, J. S. and Wu, K. M., (2000), “Ranking fuzzy numbers based on decomposition principle and signed distance,” Fuzzy Sets and Systems, Vol. 116, pp. 275-288.
[43] Zadeh, L. A., (1965), “Fuzzy set,” Information and Control, Vol. 8, pp. 338-353.
[44] Zhao, R. and Govind, R., (1991), “Defuzzification of fuzzy intervals,” Fuzzy Sets and Systems, Vol. 43, pp. 45-55.
 
 
 
 
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