How to select a promising enterprise for venture capitalists with prospect theory under intuitionistic fuzzy circumstance?
Graphical abstract
Introduction
Venture capital (VC) is a kind of capital that helps New High-tech Enterprises (NHEs) get rid of strategic or capital dilemma via providing professional resources such as additional financial capital, effective management and monitoring system, or powerful social network and so on. While NHEs play a great important role in activating economy with new scientific research results and with the application of new technologies. Moreover, those NHEs often accompany with high risk, potential high profit and many uncertain factors [1] so that only VC is willing to finance them for the sake of gaining high revenue with small probability of success.
Since the market of VC began to take shape in the 1980s in China, the government takes many effective measures to promote the development of VC market, including setting up guiding funds, making rules and regulations. Benefited by those effective measures, some achievements have already been made in recent years, as shown in Table 1.1
As we can see from Table 1 that the VC market keeps good and continuous development in China. But it is still unsatisfactory by comparing it with USA in the internal rate of return (IRR), an important index for the efficiency of VC. The IRRs of VC in USA were 9.09% in 2013 and 30.5% in 20122 according to the criterion of NVCA (the National Venture Capital Association). Due to the immaturity of VC market in China and the incompleteness of data collection, there is no precise IRR. But fortunately, we have interviewed some senior venture capitalists (VCs), and all of them agree that the IRR of VC in China is far less than in USA.
Why this tremendous gap between USA and China has been widened? A very important reason is that there is lack of a complete and effective approach used by VCs to aid them to select a promising enterprise which will affect their performances. It is particularly important for VCs to select the promising enterprise before spending their limited capital to enterprise. Sometimes, they would rather consider the promising enterprise than fund an unsatisfactory one. Therefore, as the development and expansion of VC market, the VCs are always devoting themselves to selecting the promising one among the thousands of enterprises under uncertain investment circumstance. The decision-making process of VCs can be shown in Fig. 1.
Uncertainty not only includes the fundamental nature of VC circumstance but also contains the bounded perception of VC circumstance by the VCs. However, VCs are individuals who differ in educational background, gender, age, experience, belief, risk preference and so on, and they couldn’t access all the information or enterprises, or predict the tendency of future exactly. Those conditions lead VCs to conceptualize and understand uncertainty in a different way which will impact their encoding processes of decision-making information. Moreover, in most decision-making processes, the VCs express cognitive bias caused by psychological state such as overconfidence, representativeness, availability, under and over-reaction, and herding. Those psychological states of VCs are hard to be quantitative as the decision-making information in the existing decision-making approach. Based on the uncertain circumstance of VC investment and the different perception of decision-making information produced by cognitive biases of VCs and so on, an approach is necessary to exhibit it.
The primary work of this paper is to construct the steps of how to select a promising enterprise by considering both the psychological state of VCs and vague information under VC circumstance. First, we begin with the development of VC market in China. Next, the literature review about how the VCs make their decisions including the decision-making approaches and frameworks used by VCs is presented, and we find that there is no appropriate approach that can be used to demonstrate the complex process of decision-making for VCs, especially the psychological state of VCs can’t be reflected in the existing approaches. After that, a suitable approach based on Intuitionistic Fuzzy Prospect Theory (IFPT) [2] is constructed to improve the quality of the VCs’ decisions, and the specific steps of selecting a promising enterprise are proposed. Finally, a practical example is provided to exhibit the application of this approach for the VCs in Ali Capital, and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) is used to compare with this approach to exemplify its superiority.
At least two main contributions are made in this paper: firstly, we construct a selecting approach based on IFPT to simulate the VCs’ decision-making. It not only includes the vague information under uncertain investment circumstance but contains the psychological states of the VCs. Secondly, the proposed approach is the application of IFPT in the field of VC, which plays a demonstrated role for other fields.
Section snippets
Literature review: researches about the venture capitalists’ decision-making
To understand the decision-making process of VCs is not only conductive for them to make better decisions in a more rational way, but also beneficial for substantial entrepreneurs to gain the desired capital to realize their business dreams. However, to choose a proper decision-making approach is the most important issue for VCs in their decision-making processes. In order to demonstrate the superiority of the decision-making approach constructed by us, in the following, we review the
The process of selecting the promising enterprise based on the IFPT
The decision-making approaches under the PT with fuzzy information, such as interval-valued intuitionistic fuzzy information [43], triangular intuitionistic fuzzy information [44], trapezoidal fuzzy information [45], trapezoidal intuitionistic fuzzy information [46], intuitionistic random information [47] and so on, have already been studied. In addition, the TODIM is constructed based on the PT to measure the relative dominance of the alternatives [48]. But none of them takes the uncertain
Case study
In this section, an example of Ali Capital has been discussed to illustrate the advantage of the selecting approach proposed in this paper. Hundreds of enterprises have been provided by entrepreneurs to Ali Capital as alternatives. Ali Capital is the famous Chinese-fund VC institution in China. The total amount of investment of Ali Capital has already been CNY 30.53 billion during 2011–2015, capturing the top two in China and the top one among the Chinese-fund VC institution. The key challenge
Conclusions
The VCs are confronted with the decision-making problems all the time under uncertain circumstances which will produce psychological discomforts. It motivates them to take actions such as decision-making strategy or model to transfer the uncertain situations to the relative certain ones. While, in the process of decision-making, VCs make their decisions with intuition and psychological state involuntarily. So, it is important to simulate the characteristics of VCs’ decision-making. The main
Acknowledgments
This research was funded by the National Natural Science Foundation of China (Nos. 71401116, 71571123), also funded by Sichuan University (No. skqy201655), and funded by the FEDER funds under grant (TIN2013-40658-P, TIN2016-75850-P) as well.
References (59)
- et al.
The evaluation criteria of the venture capital investment activity: an interactive assessment
Eur. J. Oper. Res.
(1987) - et al.
The potential of actuarial decision models: can they improve the venture capital investment decision?
J. Bus. Venturing
(2000) Venture capital investment selection decision-making base on fuzzy theory
Phys. Procedia
(2012)- et al.
Selection of business funding proposals using analytic network process: a case study at a venture capital company
Procedia Manuf.
(2015) - et al.
Selecting start-up businesses in a public venture capital financing using Fuzzy PROMETHEE
Procedia Comput. Sci.
(2015) Organization of innovation and capital markets
North Am. J. Econ. Finance
(2015)- et al.
Should I stay, or should I go? How fund dynamics influence venture capital exit decisions
Rev. Financ. Econ.
(2015) Capital investment decision: corporate governance, and prospect theory
Procedia Soc. Behav. Sci.
(2010)- et al.
Discrete-time behavioral portfolio selection under cumulative prospect theory
J. Econ. Dyn. Control
(2015) - et al.
The nature of information and overconfidence on venture capitalists’ decision making
J. Bus. Venturing
(2001)
How venture capitalists respond to unmet expectations: the role of social environment
J. Bus. Venturing
Trapezoidal intuitionistic fuzzy multiattribute decision making method based on cumulative prospect theory and Dempster-Shafer theory
J. Appl. Math.
A study of TODIM in a intuitionistic fuzzy and random environment
Expert Syst. Appl.
Multiple attribute decision making considering aspiration-levels: a method based on prospect theory
Comput. Ind. Eng.
The measurement of preferences over the distribution of benefits: the importance of the reference point
Eur. Econ. Rev.
On the shape of the probability weighting function
Cognit. Psychol.
Handling multicriteria fuzzy decision-making problems based on vague set theory
Fuzzy Sets Syst.
Multicriteria fuzzy decision-making problems based on vague set theory
Fuzzy Sets Syst.
Integrating experts' weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors
Decis. Support Syst.
Investment risk evaluation of high-tech projects based on random forests model
Decision making framework construction in prospect theory under intuitionistic fuzzy information
Technical Report
A cardinality constrained stochastic goal programming model with satisfaction functions for venture capital investment decision making
Ann. Oper. Res.
On Dynamic Multiple Criteria Decision Making Models: A Goal Programming Approach. Multiple Criteria Decision Making in Finance, Insurance and Investment
Multiple Criteria Decision Making and Goal Programming for Optimal Venture Capital Investments and Portfolio Management. Multiple Criteria Decision Making in Finance, Insurance and Investment
A fuzzy goal programming model for venture capital investment decision making
Inf. Syst. Oper. Res.
A decision making model for selecting start-up businesses in a government venture capital scheme
Manage. Decis.
Evaluation model for venture capital with intuitionistic fuzzy information
Research on appraisal model of venture capital investing project based on high-tech outcome transformation with uncertain linguistic information
Adv. Inf. Sci. Serv. Sci.
Research on evaluation of venture capital fund project based on data envelopment analysis model
J. Comput. Theor. Nanosci.
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