Journal of Investment and Management
Volume 7, Issue 3, June 2018, Pages: 91-101
Received: Aug. 9, 2018;
Published: Aug. 13, 2018
Views 1104 Downloads 96
Liu Xiaobing, College of Management, Shenzhen University, Shenzhen, China; Research Centre on Fictitious Economy and Data Science, University of Chinese Academy of Sciences, Beijing, China
Tian Yingjie, Research Centre on Fictitious Economy and Data Science, University of Chinese Academy of Sciences, Beijing, China
Liu Manhong, Finance School, Renmin University, Beijing, China
The value of risk project is usually uncertain, so venture investor must make investment decision based on prior estimation of future value of risk projects. This paper constructs a portfolio optimization model of risk projects considering the psychological characteristics of venture investors, and proposes a Bayesian method to deal with the uncertainty of value estimation in project portfolio selection, and utilizes Monte Carlo method to simulate the model as a linear integer programming problem. The study finds that, compared with portfolio selection based directly on ex ante value estimation, Bayesian modeling of project estimates of project value uncertainty can provide more accurate value estimates and use the resulting revised estimates to make portfolio decisions can help to select a project portfolio with a higher expected utility, eliminate the expected interval between the expected pre-expected utility and the expected utility of post-implementation, and reduce the degree of disappointment of venture investor's expected decision-making.
Research on Strategic Analysis and Decision Modeling of Venture Portfolio, Journal of Investment and Management.
Vol. 7, No. 3,
2018, pp. 91-101.
Smidt S. A Baysian analysis of project selection and of post audit evalutions [J]. Journal of Finance, 1979, 34 (3):675-689.
Fliedner T, Liesio J. Adjustable robustness for multi-attribute project portfolio selection [J]. European Journal of Operational Research, 2016, 252 (3):931-946.
Smith J E, Winkler R L. The Optimizer’s Curse: Skepticism and post-decision surprise in decision analysis [J]. Management Science, 2006, 52 (3):311-322.
Chen M, Dyer J. Inevitable disappointment in projects selected on the basis of forecasts [J]. SPE Journal, 2009, 14 (2):216-221.
Zhang Qun, Huang Xiaoxia, Zhang Chao. A mean-risk index model for uncertain capital budgeting [J]. Journal of the Operational Research Society, 2015, 66 (5):761-770.
Hall N G, Long Zhuoyu, Qi Jin, et al. Managing underperformance risk in project portfolio selection [J]. Operations Research, 2015, 63 (3):660-675.
Xu Weijun, Luo Weiqiang, Zhang weiguo. Multiple Portfolio Decision Models Considering Bankruptcy Risk Constraints [J]. Operations and Management, 2013, 22 (6):92-98.
Yu Chao, Fan Zhiping. Selection method of capital project under considering the decision-makers regret to avoid the venture [J]. Chinese Management Science, 2016, 24 (6):29-37.
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.
Diris B, Palm F, Schotman P. Long-term strategic asset allocation: An out-of-sample evaluation [J]. Management Science. 2015, 61 (9):2185-2202.
Yang Lei, Zhao Jiuru. Selection and Evaluation of New Product Development Risk Decision Research [J]. Operations and Management, 2015, 24 (3):127-133.
Vilkkumaa E, Liesio J, Salo A. Optimal strategies for selecting project portfolios using uncertain value estimates [J]. European Journal of Operational Research, 2014, 233 (3):772-783.
Kahneman D, Tversky A. Prospect theory: An analysis of decision under risk [J]. Econometrica, 1979, 47 (2):263-291.
Barberis N, Huang Ming, Santos T. Prospect theory and asset prices [J]. The Quarterly Journal of Economics, 2001, 116 (1):1-53.
Fulga C. Portfolio optimization under loss aversion [J]. European Journal of Operational Research, 2016, 251 (1):310-322.
Jin Xiu, Wang Jia, Gao Yin. Optimal Asset Allocation and Empirical Study Based on Dynamic Loss Aversion Portfolio Model [J]. Chinese Management Science, 2014, 22 (5):16-23.
Wang Jia, Jin Xiu, Wan Yin. A Robust Portfolio Model Based on Loss Aversion and Fuzzy Aversion [J]. Systems Engineering Theory and Practice, 2016, 36 (2):288-296.
Zhang Maojun, Nan Jiangxia, Yuan Gonglin, etal. Investment Decision Model Based on Loss Averse Fund Managers [J]. Chinese Journal of Management，2014, 28 (4):118-124.
Wei Laisheng, Zhang Weiping. Bayesian analysis [M]. University of Science and Technology of China Press, 2013.
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.
Li Qi, Raccine J. Nonparametric Econometrics: Theory and Practice [M]. Ye A-Zhong, Wu Xiangbo translation, Beijing: Peking University Press, 2015.
Van den Steen E. Overconfidence by Bayesian-rational agents [J]. Management Science, 57 (5):884-896.
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.
Shapiro A, Dentcheva D, Ruszczynski A. Lectures on stochastic programming: Modeling and theory [M]. 2nd Edition, Philadephia:SIAM, 2014.
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.
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, Augest 4-9, 2002.
Zhang Yao, Guan Xin, Sun Yang, etal. Project Investment Decision Under Considering Background Risk [J]. Chinese Management Science，201, 24 (9):71-80.