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Multiple Criteria Decision Making and Goal Programming for Optimal Venture Capital Investments and Portfolio Management

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Part of the book series: Multiple Criteria Decision Making ((MCDM))

Abstract

The venture capital market plays a significant role in providing capital to a new feasible business idea (new product, service, or retail concept) and businesses of different type. This chapter focuses on the way venture capitalists make their investment decision, a process involving several conflicting and imprecise criteria. We propose three different models to solve these complex decision making contexts, namely a deterministic goal programming model with satisfaction function, a scenario-based stochastic goal programming model with satisfaction function, and a fuzzy goal programming formulation. The three models have been applied to three concrete examples using real data obtained from some Italian venture capital funds. It turns out that these models are easy and simple to be implemented and analyzed, and represent an implementable approach for both scientists and practitioners.

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References

  • Anagnostopoulos, K. P., & Mamanis, G. (2011). The mean–variance cardinality constrained portfolio optimization problem: An experimental evaluation of five multi-objective evolutionary algorithms. Expert Systems with Applications, 38(11), 14208–14217.

    Google Scholar 

  • Aouni, B., Colapinto, C., & La Torre, D. (2010). Solving stochastic multi-objective programming in portfolio selection through the GP model. Journal of Financial Decision Making, 6(2), 17–30.

    Google Scholar 

  • Aouni, B., Colapinto, C., & La Torre, D. (2013). A cardinality constrained stochastic goal programming model with satisfaction functions for venture capital investment decision making. Annals of Operations Research, 205(1), 77–88.

    Article  MathSciNet  MATH  Google Scholar 

  • Aouni, B., Colapinto, C., & La Torre, D. (2014). Portfolio management through goal programming: State-of-the-art. European Journal of Operations Research, 234, 536–545.

    Article  MATH  Google Scholar 

  • Aouni, B., & La Torre, D. (2010). A generalized stochastic goal programming model. Applied Mathematics and Computation, 215, 4347–4357.

    Article  MathSciNet  MATH  Google Scholar 

  • Arenas-Parra, M., Bilbao-Terol, A., & Rodriguez Uria, M. V. (2001). A fuzzy goal programming approach to portfolio selection. European Journal of Operational Research, 133, 287–297.

    Article  MathSciNet  MATH  Google Scholar 

  • Bellman, R. E., & Zadeh, L. A. (1970). Decision making in a fuzzy environment. Management Sciences, 17, B141–B164.

    Article  MathSciNet  MATH  Google Scholar 

  • Ben Abdelaziz, F., Aouni, B., & El Fayedh, R. (2007). Multi-objective stochastic programming for portfolio selection. European Journal of Operational Research, 177, 1811–1823.

    Article  MATH  Google Scholar 

  • Ben Abdelaziz, F., El Fayedh, R., & Rao, A. (2009). A discrete stochastic goal program for portfolio selection: The case of United Arab Emirates equity market. INFOR Information Systems and Operational Research, 47(1), 5–13.

    Article  MathSciNet  Google Scholar 

  • Bertsimas, D., & Shioda, R. (2009). Algorithm for cardinality-constrained quadratic optimization. Computational Optimization and Applications, 43, 1–22.

    Article  MathSciNet  MATH  Google Scholar 

  • Bienstock, D. (1996). Computational study of a family of mixed-integer quadratic programming problems. Mathematical Programming, 74, 121–140.

    MathSciNet  MATH  Google Scholar 

  • Bilbao-Terol, A., Arenas-Parra, M., Jiménez, M., Pérez-Gladish, B., & Rodrìguez Urìa, M. V. (2006). An extension of Sharpe’s single-index model: Portfolio selection with expert betas. Journal of the Operational Research Society, 57, 1442–1451.

    Article  Google Scholar 

  • Bilbao-Terol, A., Arenas-Parra, M., Rodrıguez, M. V., & Antomil, J. (2007). On constructing expert betas for single-index model. European Journal of Operational Research, 183(2), 827–847.

    Article  Google Scholar 

  • Chang, T. J., Meade, N., & Beasley, J. E. (2000a). Heuristics for cardinality constrained portfolio optimization. Computers & Operations Research, 27, 1271–1302.

    Article  MATH  Google Scholar 

  • Chang, T.-J., Meade, N., Beasley, J. E., & Sharaiha, Y. M. (2000b). Heuristics for cardinality constrained portfolio optimization. Mathematical Programming, 74, 121–140.

    Google Scholar 

  • Charnes, A., & Cooper, W. W. (1952). Chance constraints and normal deviates. Journal of the American Statistical Association, 57, 134–148.

    Article  MathSciNet  Google Scholar 

  • Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management Science, 6, 73–80.

    Article  MathSciNet  MATH  Google Scholar 

  • Charnes, A., Cooper, W. W., & Ferguson, R. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1, 138–151.

    Article  MathSciNet  MATH  Google Scholar 

  • Chen, L. H., & Tsai, F. C. (2001). Fuzzy goal programming with different importance and priorities. European Journal of Operational Research, 133, 548–556.

    Article  MATH  Google Scholar 

  • Colapinto, C. (2007). A way to foster innovation: A venture capital district from Silicon Valley and route 128 to Waterloo Region. International Journal of Economics, 3, 319–343.

    Google Scholar 

  • Colapinto, C. (2011a). Exploring academic entrepreneurship in the Milan area. Industry & Higher Education, 25(1), 1–7.

    Article  Google Scholar 

  • Colapinto, C. (2011b). The role of Italian incubators and Science Parks in the Triple-Helix era. The hybrid model developed in Lombardy. International Journal of Technoenterpreneurship, 2(3/4), 290–303.

    Article  Google Scholar 

  • Colapinto, C., & Porlezza, C. (2012). Innovation in creative industries: From the quadruple helix model to the systems theory. Journal of the Knowledge Economy, 3(4), 343–353.

    Article  Google Scholar 

  • Dhingra, A. K., Rao, S. S., & Kumar, V. (1992). Nonlinear membership functions in multiobjective fuzzy optimization of mechanical and structural systems. AIAA Journal, 30, 251–260.

    Article  ADS  MATH  Google Scholar 

  • Fieldsend, J. E., Matatko, J., & Peng, M. (2004). Cardinality constrained portfolio optimisation. In Lecture notes in computer science, vol. 3177, pp. 788–793.

    Google Scholar 

  • Freeling, A. N. S. (1980). Fuzzy sets and decision analysis. IEEE Transactions on Systems, 10, 341–354.

    Google Scholar 

  • Gilbert, B. A., Mcdougall, P. P., & Audretsch, D. B. (2006). New venture growth: A review and extension. Journal of Management, 32(6), 926–950.

    Article  Google Scholar 

  • Gupta, M., & Bhattacharjee, D. (2010). Min sum weighted fuzzy goal programming model in investment management planning: A case study. International Research Journal of Finance and Economics, 56, 76–81.

    Google Scholar 

  • Hall, J., & Hofer, C. W. (1993). Venture capitalists’ decision criteria and new venture evaluation. Journal of Business Venturing, 8, 25–42.

    Article  Google Scholar 

  • Hannan, E. L. (1981). Some further comments on fuzzy priorities. Decision Sciences, 12, 539–541.

    Article  ADS  Google Scholar 

  • Hisrich, R. D., & Jankowicz, A. D. (1990). Intuition in venture capital decisions: An exploratory study using a new technique. Journal of Business Venturing, 5, 19–62.

    Article  Google Scholar 

  • Hofstede, G. (1984). Culture’s consequences: International differences in work-related values. Newbury Park, CA: Sage.

    Google Scholar 

  • Ignizio, J. P. (1982). Notes and communications of the (re)discovery of fuzzy goal programming. Decision Sciences, 13, 331–336.

    Article  Google Scholar 

  • Inuiguchi, M., & Ramik, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3–28.

    Article  MathSciNet  MATH  Google Scholar 

  • La Torre, D. (2003). On generalized derivatives for C1,1 vector optimization problems. Journal of Applied Mathematics, 7, 365–376.

    Article  Google Scholar 

  • Laughun, D. J., Payne, J. W., & Crum, R. (1980). Managerial risk preferences for below target returns. Management Science, 26, 1238–1249.

    Article  Google Scholar 

  • Li, D., Sun, X., & Wang, J. (2006). Optimal lot solution to cardinality constrained mean–variance formulation for portfolio selection. Mathematical Finance, 16, 83–101.

    Article  CAS  MathSciNet  MATH  Google Scholar 

  • Manigart, S., Joos, P., & De Vos, D. (1994). The performance of publicly traded European venture capital companies. Journal of Small Business Finance, 3(2), 111–125.

    Google Scholar 

  • Mansour, N., Rebaï, A., & Aouni, B. (2007). Portfolio selection through the imprecise goal programming model: Integration of manager’s preferences. Journal of Industrial Engineering International, 3(5), 1–8.

    Google Scholar 

  • Maringer, D. G., & Kellerer, H. (2003). Optimization of cardinality constrained portfolios with a hybrid local search algorithm. OR Spectrum, 25, 481–495.

    Article  MathSciNet  MATH  Google Scholar 

  • Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7, 77–91.

    Google Scholar 

  • Martel, J.-M., & Aouni, B. (1990). Incorporating the decision-maker’s preferences in the goal programming model. Journal of the Operational Research Society, 41, 1121–1132.

    Article  MATH  Google Scholar 

  • Morgan, G. (1986). Images of organization. Beverly Hills, CA: Sage.

    Google Scholar 

  • Narasimhan, R. (1980). Goal programming in a fuzzy environment. Decision Sciences, 11, 325–336.

    Article  MathSciNet  Google Scholar 

  • Piol, E. (2004). Il sogno di un’impresa. Dall’Olivetti al venture capital: una vita nell’information technology. Milano: Il Sole 24 Ore Libri.

    Google Scholar 

  • Poindexter, J. B. (1976). The efficiency of financial markets: The venture capital case. Unpublished doctoral dissertation, New York University, New York.

    Google Scholar 

  • Rao, S. S. (1987). Multi-objective optimization of fuzzy structural systems. International Journal for Numerical Methods in Engineering, 24, 1157–1171.

    Article  MathSciNet  ADS  MATH  Google Scholar 

  • Romero, C. (1991). Handbook of critical issues in goal programming. Oxford: Pergamon Press.

    MATH  Google Scholar 

  • Sahlman, W. (1990). The structure and governance of venture-capital organizations. Journal of Financial Economics, 27(2), 473–521.

    Article  Google Scholar 

  • Sawaragi, Y., Nakayama, H., & Tanino, T. (1985). Theory of multiobjective optimization (Mathematics in science and engineering, Vol. 176). Orlando: Academic Press.

    MATH  Google Scholar 

  • Schaffer, M. (1989). Are profit maximizers survivors? Journal of Economic Behaviour and Organization, 12, 29–45.

    Article  Google Scholar 

  • Sharma, H., Sharma, D., & Jana, R. K. (2009). Credit union portfolio management—An additive fuzzy goal programming approach. International Research Journal of Finance and Economics, 30, 19–28.

    Google Scholar 

  • Shaw, D. X., Liu, S., & Kopman, L. (2008). Lagrangian relaxation procedure for cardinality-constrained portfolio optimization. Optimization Methods and Software, 23, 411–420.

    Article  MathSciNet  MATH  Google Scholar 

  • Siskos, J., & Zopounidis, C. (1987). The evaluation criteria of the venture capital investment activity: An interactive assessment. European Journal of Operational Research, 31(3), 304–313.

    Article  Google Scholar 

  • Soleimani, H., Golmakani, H. R., & Salimi, M. H. (2009). Markowitz-based portfolio selection with minimum transaction lots, cardinality constraints and regarding sector capitalization using genetic algorithm. Expert Systems with Applications, 36, 5058–5063.

    Article  Google Scholar 

  • Storey, D. J. (2000). Small business: Critical perspectives on business and management. New York: Taylor and Francis.

    Google Scholar 

  • Tyebjee, T. T., & Bruno, A. V. (1984). A model of venture capitalist investment activity. Management Science, 30(9), 1051–1066. Institute of Management Sciences, Rhode Island.

    Article  Google Scholar 

  • Tyebjee, T. T., & Vickery, L. (1988). Venture capital in Western Europe. Journal of Business Venturing, 3(2), 123–136.

    Article  Google Scholar 

  • Wang, S., & Zhu, S. (2002). On fuzzy portfolio selection problems. Fuzzy Optimization and Decision Making, 1(4), 361–377.

    Article  MathSciNet  MATH  Google Scholar 

  • Watada, J. (1997). Fuzzy portfolio selection and its application to decision making. Tatra Mountains Mathematical Publications, 13, 219–248.

    MathSciNet  MATH  Google Scholar 

  • Wells, W. A. (1974). Venture capital decision-making. Unpublished doctoral dissertation, Carnegie-Mellon.

    Google Scholar 

  • Yang, T., Ignizio, J. P., & Kim, H.-J. (1991). Fuzzy programming with non linear membership functions: Piecewise linear approximation. Fuzzy Sets and System, 41, 39–53.

    Article  MathSciNet  MATH  Google Scholar 

  • Zacharakisa, A. L., & Dale Meyerb, G. (2000). The potential of actuarial decision models: Can they improve the venture capital investment decision? Journal of Business Venturing, 15(4), 323–346.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, X. (2012). Venture capital investment selection decision-making base on fuzzy theory. Physics Procedia, 25, 1369–1375.

    Article  ADS  Google Scholar 

  • Zimmerman, H.-J. (1976). Description and optimization of fuzzy systems. International Journal of General Systems, 2, 209–215.

    Article  Google Scholar 

  • Zimmerman, H.-J. (1978). Fuzzy programming and linear programming with several objectives functions. Fuzzy Sets and Systems, 1, 45–55.

    Article  MathSciNet  Google Scholar 

  • Zimmerman, H.-J. (1983). Using fuzzy sets in operations research. Fuzzy Sets and Systems, 13, 201–216.

    Google Scholar 

  • Zimmerman, H.-J. (1988). Modelling flexibility, vagueness and uncertainty in operations research. Investigación Operativa, 1, 7–34.

    Google Scholar 

  • Zimmerman, H.-J. (1990). Decision making in ill-structured environments and with multiple criteria. In C. A. Bana e Costa (Ed.), Readings in multiple criteria decision aid (pp. 119–151). Heidelberg: Springer.

    Chapter  Google Scholar 

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Correspondence to Cinzia Colapinto .

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Colapinto, C., La Torre, D. (2015). Multiple Criteria Decision Making and Goal Programming for Optimal Venture Capital Investments and Portfolio Management. In: Al-Shammari, M., Masri, H. (eds) Multiple Criteria Decision Making in Finance, Insurance and Investment. Multiple Criteria Decision Making. Springer, Cham. https://doi.org/10.1007/978-3-319-21158-9_2

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