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. Project portfolio selection is an important decision in many venture capital companies, and practically all venture capitalists(VC) seek to creat value by selecting and executing portfolios of venture projects that consume resourse, and behavioral factors of the VC directly influences the result of the optimal portfolio selection. Most studies on project portfolio selection focus on identifying the "right" project portfolio under various criteria, such as reward and risk.
In this paper a portfolio approach is taken to analyze the investment strategy of aVC. Considering the psych-ological characteristics of VC's loss aversion from the perspective of prospect theory, a more practical portfolio optimization model that maximizes the expected utility of VC is constructed, and the model is transformed into a linear mixed integer programming problem by the Monte Carlo simulation method.
Typically, the value of venture capital project is uncertain,and thus venture capitalist must take decisions based on ex ante estimates about what this future value will be. Due to estimation uncertainties, it is difficult to identify the truly best projects, whereby the selected portfolio is typically suboptimal. Furthermore, it can be shown that the value of the selected portfolio is systematically overestimated, causing the VC to experience post-decision disappointment. The phenomenon underlying post-decision disappointment is, in short, that the more the value of a project has been overestimated, the more probable it is that this project will be selected. In this paper, a Bayesian model framework to account for value uncertainties in project portfolio selection is developed. Our analytical and simulation results show that, in comparison with the straightforward portfolio selection based on ex ante value estimates, the explicit Bayesian modeling of estimation uncertainties tends to give more accurate project value estimates, resulting in a higher expected portfolio utility value, and eliminate the expected gap between the realized ex post portfolio utility value and the estimated ex ante portfolio utility value. Moreover, our results have shown that the Bayesian revision of value estimates decrease the level of disappointment that the VC can expect to experience.
With the proposed Bayesian framework VC can gather more precise information about projects' value and mitigate the experienced post-decision disappointment. Apart from the debiasing of value estimates, our frame-work could be extended by developing a model that accounts both value and cost uncertainties. This way number of targets of application could be substantially increased.
HU Zhi-jun, PENG Fei, LI Zhi-xia
. Bayesian Evaluation and Selection Strategies in Venture Project Portfolio Decision Analysis[J]. Chinese Journal of Management Science, 2017
, 25(2)
: 30
-39
.
DOI: 10.16381/j.cnki.issn1003-207x.2017.02.004
[1] Smidt S. A Bayesian analysis of project selection and of post audit evaluations[J].Journal of Finance,1979,34(3):675-688.
[2] Fliedner T,Liesi J. Adjustable robustness for multi-attribute project portfolio selection[J]. European Journal of Operational Research,2016,252(3):931-946.
[3] Smith J E,Winkler R L. The Optimizer's Curse:Skepticism and postdecision surprise in decision analysis[J]. Management Science,2006,52(3):311-322.
[4] Chen M, Dyer J. Inevitable disappointment in projects selected on the basis of forecasts[J]. SPE Journal,2009, 14(2):216-221.
[5] Zhang Qun,Huang Xiaoxia,Zhang Chao. A mean-risk index model for uncertain capital budgeting[J]. Journal of the Operati-onal Research Society,2015,66(5):761-770.
[6] Hall N G, Long Zhuoyu, Qi Jin, et al. Managing underperformance risk in project portfolio selection[J]. Operati-ons Research,2015,63(3):660-675.
[7] 徐维军,罗伟强,张卫国.考虑破产风险约束的多项投资组合决策模型[J].运筹与管理,2013,22(6):92-98.
[8] 于超,樊治平. 考虑决策者后悔规避的风险投资项目选择方法[J].中国管理科学,2016,24(6):29-37.
[9] Jacquier E,Polson N. Bayesian methods in finance[M]//Geweke J, Koop G, Van Dijk H. The Oxford handbook of Bayesian econometrics,Oxford, England:oxford university press, 2011.
[10] Diris B,Palm F,Schotman P. Long-term strategic asset allocation:An out-of-sample evaluation[J]. Management Science. 2015,61(9):2185-2202.
[11] 杨雷, 赵九茹.选择与评价新产品开发的风险决策研究[J]. 运筹与管理,2015,24(3):127-133.
[12] Vilkkumaa E,Liesi J,Salo A. Optimal strategies for selecting project portfolios using uncertain value estima-tes[J]. European Journal of Operational Research, 2014,233(3):772-783.
[13] Kahneman D,Tversky A. Prospect theory:An analysis of decision under risk[J].Econometrica, 1979, 47(2):263-291.
[14] Barberis N,Huang Ming,Santos T. Prospect theory and asset prices[J].The Quarterly Journal of Economics,2001,116(1):1-53.
[15] Fulga C. Portfolio optimization under loss aversion[J]. European Journal of Operational Research,2016,251(1):310-322.
[16] 金秀,王佳,高莹. 基于动态损失厌恶投资组合模型的最优资产配置与实证研究[J]. 中国管理科学,2014,22(5):16-23.
[17] 王佳,金秀,苑莹,等. 基于损失厌恶与模糊厌恶的分布鲁棒投资组合模型[J].系统工程理论与实践,2016,36(2):288-296.
[18] 张茂军,南江霞,袁功林,等.基于损失厌恶的基金管理者的投资决策模型[J]. 管理工程学报,2014,28(4):118-124.
[19] 韦来生,张伟平贝叶斯分析[M].合肥中国科技大学出版社,2013.
[20] William T S, Thomas A P, Douglas N F. The triangular density to approximate the normal density:Decision rules-of-thumb[J]. Reliability Engineering and System Safety,2003,82(3):331-341.
[21] Li Qi,Raccine J. 非参数计量经济学:理论与实践[M].叶阿忠,吴相波,译,北京:北京大学出版社,2015.
[22] Van den Steen E. Overconfidence by Bayesian-rational agents[J]. Management Science,57(5):884-896.
[23] Ruppert D,Matteson D S. Bayesian data analysis and MCMC[M]//Ruppertp,Matteson D S. Statistics and data analysis for financial engineering,Springer Texts in Statistics, 2015:581-644.
[24] Shapiro A,Dentcheva D,Ruszczyński A. Lectures on stochastic programming:Modeling and theory[M]. 2nd Edition, Philadelphia:SIAM,2014.
[25] Jose V R R. Assessing probability distributions from data[M]//Conhran J J. Wiley encyclopedia of operations research and management science,New York:John Wiley and Sons,2010:183-190.
[26] Price H J,Manson A R. Uninformative priors for Bayes theorem[C]//:21st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering,Battimore, M D, August 4-9,2002.
[27] 张尧,关欣,孙杨,等. 考虑背景风险的项目投资决策[J]. 中国管理科学,2016,24(9):71-80.
