Financing Decision and Productivity Growth for the Venture Capital Industry Using High-Order Fuzzy Time Series
Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.
[1] Y. H. Deng, J. M. Quigley and R. Van Order, ”Mortgage default and low
downpayment loans: The costs of public subsidy,” Regional Science and
Urban Economics, vol.26, pp.263-285, 1996.
[2] Y. H. Deng, “Mortgage Termination: An empirical harzard model with
stochastic term structure,” Journal of Real Estate Finance and
Economics, vol.14, pp.309-331, 1997.
[3] Y. H. Deng, J. M. Quigley and R. Van Order, “Mortgage terminations,
heterogeneity and the exercise of mortgage options,” Econometrica,
vol.68, no.2, pp.275-307, 2000.
[4] C. Marrison, The fundamentals of Risk measurement, New York:
McGraw-Hill, 2002.
[5] B. M. Lambrecht, W. R. M. Perraudin and S. Satchell, “Mortgage Default
and Possession under Recourse: A Competing Hazards Approach,”
Journal of Money, Credit and Banking, vol.35, no.3, pp.425-442, 2003.
[6] V. Hartarska and C. Gonzalez-Vega, “Credit Counseling and Mortgage
Termination by Low-Income Households.” The Journal of Real Estate
Finance and Economics, vol.30, no.3, pp.227-243, 2005.
[7] V. Hartarska and C. Gonzalez-Vega, “Evidence on the effect of credit
counseling on mortgage loan default by low-income households,”
Journal of Housing Economics, vol.15, no.1, pp.63-79, 2006.
[8] D. Collett, Modeling binary data (2nd ed.), London: Chapman & Hall,
2003.
[9] P. D. Allison, Logistic regression using the SAS system: Theory and
application, Cary, NC: SAS Institute, 1999.
[10] R. H. Myers, D. C. Montgomery and G. G. Vining, Generalized linear
models: With applications in engineering and sciences, New York : John
Wiley, 2002.
[11] B. Lawal, Categorical data analysis with SAS and SPSS applications,
London: Lawrence Erlbaum Associates, 2003.
[12] D. Pregibon, “Logistic regression diagnostics,” Annals of statistics, vol.9,
no.3: pp.705-724, 1981.
[13] A. Agresti, An Introduction to Categorical Date Analysis (2nd ed.), New
York: John Wiley, 2007.
[14] B. A. Ciochetti, G. Lee, J. Shilling, and R. Yao, “Aproportional hazards
model of commercial mortgage default with originator bias,”
Umpublished working paper, 2001.
[15] M. Davidian, and R. A. Gallant, “The nonlinear mixed effects with a
smooth random effects density,” Biometrika, vol.80, pp.475-488, 1993.
[16] M. Davidian, and D. M. Giltinan, “Nonlinear models for repeated
measurement data,” New York: Chapman & Hall, 1995.
[17] T. Yamamoto, A. Yoshida, T. Ijima, Dynamics of Elastically Moored
Floating Objects, In DYNAMIC ANALYSIS OF OFFSHORE
STRUCTURES, Kirk, C.L. (ed.), 106-113. CML, Southampton, 1982.
[18] C.C. Mei, Numerical Methods in Water Wave Diffraction and Radiation,
Ann. Rev. Fluid Mech. 10, 393, 1978.
[19] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-353.
[20] T. Takagi, M. Sugeno, Fuzzy identification of systems and its
applications to modeling and control, IEEE Trans. Syst., Man, Cybern.,
15 (1985) 116-132.
[21] T.Y. Hsieh, M.H.L. Wang, C.W. Chen et al., A new viewpoint of S-curve
regression model and its application to construction management, Int. J.
Artif. Intell. Tools, 15 (2006) 131-142.
[22] M. Cococcioni, P. Guasqui, B. Lazzerini et al., Identification of
Takagi-Sugeno fuzzy systems based on multi-objective genetic
algorithms, Lect. Note Artif. Int., 3849 (2006) 172-177.
[23] Z.Y. Zhang, H.L. Zhou, S.D. Liu et al., An application of Takagi-Sugeno
fuzzy system to the classification of cancer patients based on elemental
contents in serum samples, Chemometr. Intell. Lab. Syst., 82 (2006)
294-299.
[24] M. Sugeno, G.T. Kang, Fuzzy modeling and control of multilayer
incinerator, Fuzzy Sets Syst., 18 (1986) 329-346.
[25] K. Tanaka, M. Sugeno, Stability analysis and design of fuzzy control
systems, Fuzzy Sets Syst., 45 (1992) 135-156.
[26] H.O. Wang, K. Tanaka, M.F. Griffin, “Parallel distributed compensation
of nonlinear systems by Tanaka-Sugeno fuzzy model,” Proc. FUZZ
IEEE/IFES’95, (1995) 531-538.
[27] C.W. Chen, W.L. Chiang, F.H. Hsiao, Stability analysis of T-S fuzzy
models for nonlinear multiple time-delay interconnected systems, Math.
Comput. Simul., 66 (2004) 523-537.
[28] C.W. Chen, W.L. Chiang, C.H. Tsai et al., Fuzzy Lyapunov method for
stability conditions of nonlinear systems, Int. J. Artif. Intell. Tools, 15
(2006) 163-171.
[29] K. Tanaka, H.O. Wang, Fuzzy Control Systems Design and Analysis,
John Wiley & Sons. Inc., New York, 2001.
[30] E. F. Vonesh and V. M. Chinchilli, Linear and nolinear models for the
analysis of repeated measurement, New York: Marcel Dekker, 1996.
[1] Y. H. Deng, J. M. Quigley and R. Van Order, ”Mortgage default and low
downpayment loans: The costs of public subsidy,” Regional Science and
Urban Economics, vol.26, pp.263-285, 1996.
[2] Y. H. Deng, “Mortgage Termination: An empirical harzard model with
stochastic term structure,” Journal of Real Estate Finance and
Economics, vol.14, pp.309-331, 1997.
[3] Y. H. Deng, J. M. Quigley and R. Van Order, “Mortgage terminations,
heterogeneity and the exercise of mortgage options,” Econometrica,
vol.68, no.2, pp.275-307, 2000.
[4] C. Marrison, The fundamentals of Risk measurement, New York:
McGraw-Hill, 2002.
[5] B. M. Lambrecht, W. R. M. Perraudin and S. Satchell, “Mortgage Default
and Possession under Recourse: A Competing Hazards Approach,”
Journal of Money, Credit and Banking, vol.35, no.3, pp.425-442, 2003.
[6] V. Hartarska and C. Gonzalez-Vega, “Credit Counseling and Mortgage
Termination by Low-Income Households.” The Journal of Real Estate
Finance and Economics, vol.30, no.3, pp.227-243, 2005.
[7] V. Hartarska and C. Gonzalez-Vega, “Evidence on the effect of credit
counseling on mortgage loan default by low-income households,”
Journal of Housing Economics, vol.15, no.1, pp.63-79, 2006.
[8] D. Collett, Modeling binary data (2nd ed.), London: Chapman & Hall,
2003.
[9] P. D. Allison, Logistic regression using the SAS system: Theory and
application, Cary, NC: SAS Institute, 1999.
[10] R. H. Myers, D. C. Montgomery and G. G. Vining, Generalized linear
models: With applications in engineering and sciences, New York : John
Wiley, 2002.
[11] B. Lawal, Categorical data analysis with SAS and SPSS applications,
London: Lawrence Erlbaum Associates, 2003.
[12] D. Pregibon, “Logistic regression diagnostics,” Annals of statistics, vol.9,
no.3: pp.705-724, 1981.
[13] A. Agresti, An Introduction to Categorical Date Analysis (2nd ed.), New
York: John Wiley, 2007.
[14] B. A. Ciochetti, G. Lee, J. Shilling, and R. Yao, “Aproportional hazards
model of commercial mortgage default with originator bias,”
Umpublished working paper, 2001.
[15] M. Davidian, and R. A. Gallant, “The nonlinear mixed effects with a
smooth random effects density,” Biometrika, vol.80, pp.475-488, 1993.
[16] M. Davidian, and D. M. Giltinan, “Nonlinear models for repeated
measurement data,” New York: Chapman & Hall, 1995.
[17] T. Yamamoto, A. Yoshida, T. Ijima, Dynamics of Elastically Moored
Floating Objects, In DYNAMIC ANALYSIS OF OFFSHORE
STRUCTURES, Kirk, C.L. (ed.), 106-113. CML, Southampton, 1982.
[18] C.C. Mei, Numerical Methods in Water Wave Diffraction and Radiation,
Ann. Rev. Fluid Mech. 10, 393, 1978.
[19] L. A. Zadeh, Fuzzy sets, Information and Control, 8 (1965) 338-353.
[20] T. Takagi, M. Sugeno, Fuzzy identification of systems and its
applications to modeling and control, IEEE Trans. Syst., Man, Cybern.,
15 (1985) 116-132.
[21] T.Y. Hsieh, M.H.L. Wang, C.W. Chen et al., A new viewpoint of S-curve
regression model and its application to construction management, Int. J.
Artif. Intell. Tools, 15 (2006) 131-142.
[22] M. Cococcioni, P. Guasqui, B. Lazzerini et al., Identification of
Takagi-Sugeno fuzzy systems based on multi-objective genetic
algorithms, Lect. Note Artif. Int., 3849 (2006) 172-177.
[23] Z.Y. Zhang, H.L. Zhou, S.D. Liu et al., An application of Takagi-Sugeno
fuzzy system to the classification of cancer patients based on elemental
contents in serum samples, Chemometr. Intell. Lab. Syst., 82 (2006)
294-299.
[24] M. Sugeno, G.T. Kang, Fuzzy modeling and control of multilayer
incinerator, Fuzzy Sets Syst., 18 (1986) 329-346.
[25] K. Tanaka, M. Sugeno, Stability analysis and design of fuzzy control
systems, Fuzzy Sets Syst., 45 (1992) 135-156.
[26] H.O. Wang, K. Tanaka, M.F. Griffin, “Parallel distributed compensation
of nonlinear systems by Tanaka-Sugeno fuzzy model,” Proc. FUZZ
IEEE/IFES’95, (1995) 531-538.
[27] C.W. Chen, W.L. Chiang, F.H. Hsiao, Stability analysis of T-S fuzzy
models for nonlinear multiple time-delay interconnected systems, Math.
Comput. Simul., 66 (2004) 523-537.
[28] C.W. Chen, W.L. Chiang, C.H. Tsai et al., Fuzzy Lyapunov method for
stability conditions of nonlinear systems, Int. J. Artif. Intell. Tools, 15
(2006) 163-171.
[29] K. Tanaka, H.O. Wang, Fuzzy Control Systems Design and Analysis,
John Wiley & Sons. Inc., New York, 2001.
[30] E. F. Vonesh and V. M. Chinchilli, Linear and nolinear models for the
analysis of repeated measurement, New York: Marcel Dekker, 1996.
@article{"International Journal of Business, Human and Social Sciences:65068", author = "Shang-En Yu", title = "Financing Decision and Productivity Growth for the Venture Capital Industry Using High-Order Fuzzy Time Series ", abstract = "Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.
", keywords = "Heterogeneity, residential mortgage loans, foreclosure.", volume = "7", number = "9", pages = "2563-3", }
