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23 Seiten, Note: 2,0
2. Literature Review
2.1 Measuring the financial performance of VCFs
2.2 MCDM methodologies and their application on the Venture Capital System
2.2.1 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
2.2.2 Modified TOPSIS with objective weights
3.1 Modified TOPSIS with objective weights
3.2 Choice of Financial Performance Indicators (FPIs)
3.2.2 Asset management
3.2.4 Debt management
4. Testing the methodology
Venture Capital Firms (VCFs) are agents boosting the process of technology commercialization. They provide young and technology-oriented companies with private equity for a long term. Furthermore, they manage the Venture Capital funds and provide portfolio companies with managerial support. To date, there has not been utilized an adequate analytical model that simply, effectively and objectively measures the VCF's corporate financial performance in comparison to their competitors. In this analytical study we make use of the modified TOPSIS with objective weights (mTOPSISow) (Deng et al., 2000) for inter-company comparison of VCFs to evaluate their relative corporate financial performance and to identify influential Financial Performance Indicators (FPIs). Furthermore, we test and apply mTOPSISow on a sample of five large German VCFs. We come to the result that there is a high discrepancy regarding to the performances of the examined VCFs. Every VCF is geometrically more or less far away from a potential top achievement. Three out of four FPIs are nearly equally important for the final result, whereas one FPI is not influential. Consequently, this study is useful for individual VCFs to identify their relative financial performance and areas of improvement in an objective and simple way.
Venture Capital Firm,
Financial Performance Indicator,
Multi-Criteria Decision Making (MCDM),
Recent decades have shown that innovation and entrepreneurship are essential elements for economic growth, wealth and well-fed labor markets. Furthermore, increased globalization imposes requirements in the competitiveness of emergent and technology-oriented companies worldwide (Greenhalgh and Rogers, 2010). The process of technology commercialization is a challenging area when it comes to the common issue of how to maximize the value of business ideas and intellectual property (Sullivan, 1995). Venture Capital Firms (VCFs) play a key role in Technology Transfer (TT), because they provide ventures with private equity and managerial support. Hence, they have significant impact on the success and growth rate of the TT system, including researchers, entrepreneurs and investors (Croce et al., 2013; Samila and Sorenson, 2010). The Venture Capital (VC) system is a part of the TT system and contains VCFs, researchers, entrepreneurs, investors and policy makers. The agents contribute with equity, other financial flows, consultancy services and regulations to the VC system. The level of involvement depends on the current investment stages (seed, early stage, growth etc.) of the portfolio firms (Tarrade, 2012).
Simple, effective and flexible measurement tools are necessary to evaluate the VCF’s corporate financial performance. Few researchers have addressed the problem of multi-criteria financial evaluation of Venture Capital Firms (Gregoriou et al., 2007; Kung and Wen, 2007), because traditional methods of evaluation are not appropriate in every case of assessment (Da Rin et al., 2013). We criticize these recent attempts because they do not make use of a simple and objective Multi-Criteria Decision Making approach. Such a methodology is necessary for individual VCFs to evaluate their relative corporate financial performance and to identify areas of improvement.
We contribute to the current literature in terms of conveying a simple, effective and objective multi-criteria measurement tool (mTOPSISow) to the measurement of the relative corporate financial performance of Venture Capital Firms. Thus, we obtain a detailed performance ranking of VCFs. The methodology, as introduced by Deng et al. (2000), can be combined with Spearman’s rank correlation. As a consequence, the approach is also able to identify the amount of influence of performance indicators (criteria) which cause the ranking. We adapt the methodology to the integration of ratios from different financial areas which seem to be important for the VCF’s performance. Furthermore, we test and apply the methodology on a sample of real VCFs. Radar charts are used to interpret mid-results which cause the overall performance too.
We find out that the methodology is very suitable to measure the relative corporate financial performance of a sample of five German VCFs easily, objectively and effectively. We are able to calculate objective weights, a performance ranking of VCFs and the impacts of Financial Performance Indicators (FPIs) on the overall performance. Generally speaking, only one Venture Capital Firm is top-performing in relation to its competitors. Three out of four financial ratios have nearly equal influence on the overall performance. One financial ratio has no impact on the evaluation results. The approach can easily be applied on other i.e. larger groups of VCFs. The flexibility of this method allows the integration of various other performance measures.
This paper is organized as follows: In the second section we present generally accepted methods for the performance evaluation of VCFs, Multi-Criteria Decision Making (MCDM) techniques and their applications on the Venture Capital system. Furthermore, we identify the gap in the literature we concern about and justify the choice for the later presented methodology. The third section presents the methodology, namely "modified TOPSIS with objective weights" (mTOPSISow) (Deng et al., 2000) and the Spearman's rank correlation. In addition, we explain the choice for the Financial Performance Indicators which will be integrated into the measurement approach. In the fourth section we test and apply the proposed methodology to a data set of five large-sized independent German VCFs. Results and a discussion follow. The fifth section concludes.
In the following section we present general literature about the Venture Capital Firm (VCF) research. In detail, we discuss common and well-accepted procedures which measure the financial performance of VCFs. Moreover, we show numerous investigations of Multi-Criteria Decision Making (MCDM) methodologies and their usage in the field of the VC system. Finally, we introduce the TOPSIS methodology and the TOPSIS extension: modified TOPSIS with objective weights.
Studies focusing on how VCFs operate have attracted much attention in recent years. More specifically, it is already presented how VCFs design their structural shape (Sahlman, 1990), choose investments (Gompers, 1995) and cooperate with other VCFs (Bygrave, 1987). However, less is known about the performance measurement of VCFs, especially key tools for policy makers, investors, entrepreneurs and particularly VCFs (Da Rin et al., 2013). Although attempts have been undertaken considering a combination of strategic and financial performance measures (Bassen et al., 2006), the greater importance of financial objectives (for the German VC market) has already been proven (Weber and Weber, 2005).
A common value for measuring the financial performance of a VCF is via time-adjusted profit returns of the funds they manage. Indicators such as Internal Rate of Return (IRR), Total Value to paid-in ratio (TVPI) and Distribution to paid-in ratio (DPI) are often used (Lindström, 2006). However, a weakness of these measurement tools is that they directly evaluate the performance of the portfolio firms, i.e. if some succeeded an exit through IPO (Initial Public Offering). Therefore former investments turn into returns after some time of managerial support by the VCF.
As illustrated by Figure 1 the VC fund connects financial flows among investors, VCFs and portfolio firms. VCFs receive management fees and carries from the VC fund or from the investors. Furthermore, they usually invest small equity in the VC fund or directly in the portfolio firms and receive returns. These are the only cash flows which should be considered for the determination of the corporate financial performance of VCFs. On the contrary, there are cash flows between some institutional investors and the VC fund such as disbursements and distributions. These have no direct influence on the financial performance of VCFs. Here, strategic relationships between VCFs and investors are driving the performance too. Moreover, investments in and exit proceeds from the portfolio companies don’t relate to the VCF’s financial performance directly. Contract designs among the agents related to the VC fund specify the proportions (Da Rin et al., 2013; Tarrade, 2012). Thus, measuring the performance of the VC fund and relating this to the corporate financial performance of the VCF is not appropriate. Additionally, these measures only consider profitability. An alternative approach is necessary to measure the corporate financial performance of VCFs more correctly.
Figure 1: financial flows and service flows between the agents in the VC system (Da Rin et al. 2013; Tarrade, 2012)
Abbildung in dieser Leseprobe nicht enthalten
To avoid this deficiency a few authors have investigated the corporate financial performance of VCFs. They use Financial Performance Indicators (FPIs) just like they are declared in financial statements. They more or less include ratios from different financial areas like profitability, solvency, growth and liquidity (Rakhman, 2011; Smith, 2005). They do not consider that measuring the corporate financial performance is not only orientated on standalone ratios. Instead it is a problem of multiple attributes, involving and combining different areas of financial performance.
Steuer and Na (2003) show that several authors consider the use of MCDM approaches and Multi-Criteria Decision Analysis (MCDA) methods in a combination with finance. Furthermore, there are a number of methodologies and applications proposed using MCDM approaches for studies concerning the VC system (Babalos et al., 2012; Dos Santos et al., 2011; Serrano-Cinca and Gutiérrez-Nieto, 2013; Siskos and Zopounidis, 1987).
A common disadvantage of MCDM techniques is that the weighting of criteria has a certain amount of subjective influence. Subjective influence could lead to wrong results especially when decision makers are not equipped with all expert knowledge. Moreover, some methods are mathematically ambitious. Consequently, they cannot be suggested as daily tools for decision makers. Nevertheless, MCDM methods are effective and one cannot imagine the world of finance without them (Ginevicius and Podvezko, 2007).
Finally, some scientists identify the importance, usefulness and originality of this merged topic: Developing a multi-criteria measurement tool for evaluating the (corporate) financial performance of VCFs on the basis of several FPIs (Gregoriou et al., 2007; Kung and Wen, 2007).
In previous studies less attention is paid to use effective, objective and simple methodologies: Kung and Wen (2007) combine a series of well-known MCDM techniques - Grey Relational Analysis (GRA), Grey Decision-Making, GM(0, N) - in order to rank a set of VCFs according to their overall financial performance and to identify influential FPIs. Although they obtain realistic results their research exhibits several drawbacks: (1) the methodology is not user-friendly (mathematical challenging), (2) utilization without advanced numerical computing environments is not possible. And (3) the application is not replicable. Gregoriou et al. (2007) analyze the efficiency of European VCFs with Data Envelopment Analysis (DEA) on the basis of the funds they manage. They use DEA as a multiple dimension tool dividing outputs (IRR, Multiple of cost) by inputs (fund size, committed capital, etc.), thus it generates an efficiency score for each VCF. Calculating the weights is computationally demanding, because each VCF has individual weights for all criteria, based on the solution of an agent-specific optimization problem. The authors do not take the varying impact of criteria into account. In both studies subjectivity is acknowledged due to a predetermined weighting of criteria.
Hence, we have to search for a simple, objective and effective approach. In the following we state several of the most common used MCDM techniques and justify the choice for mTOPSISow. As mentioned before DEA (Charnes et al., 1987) and GRA (Deng, 1982) are very effective approaches but weaken substantially in terms of simplicity. Other approaches have similar strengths and disadvantages i.e. ELECTRE (Roy, 1991) and PROMETHEE (Brans and Vincke, 1985). Moreover, it is not feasible to identify the impacts of financial ratios on the overall results with the use of DEA because inputs and outputs are separately aggregated. Although the Analytic Hierarchy Process (AHP) (Saaty, 1990) is very simple and effective, it is important to mention that numerous pairwise comparisons of alternatives lead to high subjectivity. Al-Aomar (2010) develops an AHP-Entropy method to remove this deficiency. Diakoulaki et al. (1995) developed the CRITIC method which is suitable for the objective ranking of alternatives based on several criteria. However, with these two methods it is not possible to interpret mid-results which cause the final results effectively. TOPSIS (Hwang and Yoon, 1981) is a simple and effective approach to determine performance ranks of alternatives. These are based on geometric distances to best and worst performance entries of the alternatives. Therefore, causes of the final results are easy to interpret. Weights are given to each criterion. Hence, the decision maker’s expertise is highly influential to the final ranking of alternatives. Deng et al. (2000) convey an entropy concept to the TOPSIS methodology and develop a modified TOPSIS to determine objective weights for the alternatives. Thus, we choose modified TOPSIS with objective weights as the most appropriate methodology for evaluating the relative corporate financial performance of VCFs.
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a technique for MCDM and performance analysis of alternatives. Given a set of criteria and a set of alternatives a decision matrix can be generated. Furthermore, the decision maker has to determine the importance of each criterion (weights). However, after a few calculations the Euclidean distances from a Positive Ideal Solution and Negative Ideal Solution are aggregated to form a performance indicator (Similarity to Best Condition). For each alternative this measure is between 0 and 1. A high Similarity to Best Condition (SBC) is preferable. We equate the word “solution” with the word “performance” because every set of solutions is a set of performance entries.
Again, the advantage of this method lies in its simplicity, effectiveness and flexibility concerning the design of problems (i.e. criteria for performance and/or efficiency) (Hwang and Yoon, 1981). Moreover, the decision maker can simply and effectively interpret and visualize performance origins (e.g. with radar charts).
Various advancements and adaptions of TOPSIS methodology have been developed during recent years (Abo-Sinna and Amer; 2005; Chen, 2000; Deng et al., 2000). An application in the field of finance is common (Arslan and Cunkas; 2012; Aydogan, 2011; Zhang et al., 2011). Moreover, Behzadian et al. (2012) show that the approach is widely used in business-related areas. Still, some problems arise, for example weights for criteria are given with subjective influence by the decision maker. Even with AHP (Analytic Hierarchy Process) pairwise comparisons of criteria do not diminish the subjective influence to the utmost satisfaction (Saaty, 1990). Olsen (2004) discusses various attempts that have been undertaken to determine objective weights for TOPSIS.
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