Insead Thesis

Venture Capitalists Investment Process, Criteria, and Performance

Frdric Martel*

Draft: November 2006

Abstract

According to previous studies on venture capital, deal selection skills are critical to financial success. The objective of this study is to examine and validate empirically the venture capitalists decision criteria (1) relevant for their investment decision that are (2) predictive of subsequent deal success, and/or (3) survival outcomes.

Using the information collected by a successful European VC firm while evaluating its entire deal flow of 2219 investment opportunities received between September 1996 and August 2001 (e.g. ex-ante data) plus information gathered from a questionnaire answered in September 2001 by the same VC (e.g. ex-post data), we observe that our analyses results differ significantly depending on whether we use the ex-ante or the ex-post questionnaire answers. This observation is consistent with the findings of recent cognitive psychology studies on ex-post research methodologies, as for instance, interviews and surveys.

Focusing on our ex-ante data, we identify the criteria that are significant for investment decision: Management and Financials. We also determine the criteria that are predictive of deals future financial success: Management and Financials and/or predictive of company survival, such as Resistance to Risk, Barriers to Entry, and Innovation. We note that a structured investment approach may assist VCs to adapt their investment criteria to market conditions; thereby improving their prospects of financial success.

*Frederic Martel, University of Lausannes Hautes Etudes Commerciales (HEC), e-mail: [email protected]

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Content I. Introduction ........................................................................................................................... 3 II. Literature review .................................................................................................................. 5

2.1. Investment process: background................................................................................................................... 5 2.2. Data Sources: background ............................................................................................................................ 5 2.3. Criteria linked to investment decision .......................................................................................................... 6 2.4. Criteria linked to success .............................................................................................................................. 8 2.5. Criteria linked to survival ............................................................................................................................. 8 2.6. Limitations encountered in past research ..................................................................................................... 9

(1) Data accessibility issues.............................................................................................................. 9 (2) Reporting bias issues................................................................................................................. 10 (3) Investment criteria identification issues ................................................................................... 10 (4) Sample selection representativeness issues .............................................................................. 10 (5) Data pairing issues .................................................................................................................... 11 (6) Decision model construction issues.......................................................................................... 11

III. Research Method .............................................................................................................. 11 3.1. Empirical context ........................................................................................................................................ 11 3.2. Data Collection............................................................................................................................................ 12

(1) Interview Data ........................................................................................................................... 12 (2) Deal Flow Data.......................................................................................................................... 12 (3) Ex-ante data............................................................................................................................... 12 (4) Outcome data............................................................................................................................. 13 (5) Market Data............................................................................................................................... 13 (6) Ex-post data............................................................................................................................... 14

3.3. Validity, analysis and reliability ................................................................................................................. 14 (1) Data sample specificity ............................................................................................................. 14 (2) Data sample validation.............................................................................................................. 15 (3) Univariate analysis .................................................................................................................... 18

IV. Results and Discussion ..................................................................................................... 19 4.1. Representativeness Analysis....................................................................................................................... 19 4.2. Deal Flow Analysis ..................................................................................................................................... 19 4.3. Investment Process Analysis....................................................................................................................... 20 4.4. Investment Criteria Analysis....................................................................................................................... 20

(1) Interview Analysis..................................................................................................................... 21 Criteria analysis - Differences between 3 criteria types ................................................................ 21 (2) Enacted Criteria Analysis.......................................................................................................... 23 (3) What other VCs seem to do differently .................................................................................... 26

4.5. Decision models comparison ...................................................................................................................... 27 V. Conclusion and Future Research........................................................................................ 28 Acknowledgements................................................................................................................. 30 References............................................................................................................................... 31

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I. Introduction

Venture capitalists (VCs) typically set up specially designed legal structures such as limited

partnerships, commonly called funds, to raise money and then to invest in a diversified portfolio

of companies via multiple rounds of financings. Only the most promising ventures receive follow-

on investments. According to Venture Economics (1988), over a 16-year period, more than one-

third of 383 investments made by a group of VCs resulted in an absolute loss, and about two-thirds

resulted in capital returns of less than double the original amount invested, with only a select

number of deals generating outstanding returns. Looking at the industrys reported returns, wide

gaps exist among the impressive returns generated by a few top-performing VCs, the industry

average returns, and the industrys worst performers.

Consistently delivering above-average returns while mitigating risks is crucial for VCs. It is

also a key challenge because, according to recent studies such as Kaplan and Schoar (2003) and

Cochrane (2005), on average, VC investing is not more profitable than investing in the stock

market, but is much riskier1 due to the high stochasticity of returns (returns come from few

financial successes), low liquidity, lack of transparency, and diversity (jurisdictions, industries,

market cycles, and financing stages differ). Yet the most successful VCs manage to deliver year-on-

year and fund-after-fund consistently high above-average performance, typically thanks to

proprietary skills and/or experience (Kaplan and Schoar, 2003). While published research on the

possible nature of these proprietary skills is extensive, it is far from conclusive.

Research on performance generating skills2 is far from comprehensive because, in part, of

the complexity of the VCs tasks, the opacity of their operating procedures, and the lack of reliable

research data. To date, there has been practically no study published on VCs investment screening

capabilities that is based on unbiased data recorded live at the time of the deals evaluation (e.g.

ex-ante) on real VC deals over an extended period of time that are paired with their subsequent

financial performance. So far, most studies have used either experimental designs or ex-post

evaluations, where possible biases cannot be controlled or quantified.

The purpose of this study is to identify and validate empirically criteria (1) relevant for

VCs investment decision, (2) predictive of subsequent deal success, and/or (3) survival3 outcomes,

using principally ex-ante data, where identified biases are controlled for. In addition, our

investigations aim to identify and segregate some of the assumptions flaws underlying published

studies that rely solely on ex-post design and expose some of the potential biases in deal selection

(by showing that different criteria predict VC investment and success).

1 Shepherd (1999) defines a ventures risk as the potential for the venture failure or bankruptcy 2 e.g. individual and organizational skills, know-how, good practices, or processes. 3 e.g. Shepherd (1999) defines survival as the probability that this venture will continue to participate in the market.

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To our knowledge, this study is unique because it is the first that combines (1) interview

data, (2) ex-ante data gathered on a single representative VCs complete deal flow of 2219 deals,

(e.g. this VC), (3) ex-post data on a subset of no less than 198 deals, and (4) performance data on

all 2219 deals present in the database. All this information was collected to analyze one VCs

investment behavior over a 4.5 years period of turbulent financial markets in the

Telecommunication, Information Technology, Media, Entertainment Industry, (e.g. TIME) industry

sectors.

Our data on deal flow and investment criteria is analyzed on 5 main axes. First, we use

interviews to review the VC investment process and identify the investment criteria this VC says it

used, or its espoused criteria. These are: (1) Management, (2) Scalability, (3) Barriers to Entry,

(4) Exit Opportunities. Second, we regress its investment decisions to the ex-ante information it

collected on deals in order to derive its enacted or actual investment criteria, which are more

related to the Management and the Financials of the venture. Third, also via a regression analysis

using information on other VCs decisions and our ex-ante data, we find predictors that can explain

other VCs investment decisions, which seem to be based also on Management and Financials plus

Product Development, Potential Partnerships, and Existing Customer base.

Fourth, we pair venture criteria to three possible deal outcomes: (1) financial success, (2)

low growth maturity (examined within this paper under survival), or (3) bankruptcy or venture

failure to identify the predictors linked to company survival (e.g. Barriers to Entry, Resistance to

Risk, and Management) and the predictors linked to financial success (e.g. Management and

Financials). Should these predictors be different from the investment criteria, it would imply that

VCs are not always using the right investment decision criteria needed to optimize their financial

success or the investments survival.

We note that this VCs significant investment criteria are highly correlated to the predictors

of venture success. This would lead us to conclude that this VC was following a strategy focused on

investing in deals with a higher probability of financial success where one or two deals in a

portfolio compensate for a high level of total write-offs with few just surviving firms in the

portfolio. This conclusion is further corroborated by the actual performance this VC published in

December 2005.

Also, expanding on Mainprize et al. (2002), we derive a decision model based on our

ex-ante data and evaluate its effectiveness as a decision aid in comparison to the performances of

other published investment models. We find that our model may be a better decision aid than some

of the models presented in other studies.

We also note that a structured approach to venture investing may assist VCs to stay flexible

and in tune with their constantly evolving market conditions when armed with the relevant

investment criteria.

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II. Literature review

2.1. Investment process: background

The importance of the selection criteria can be best understood by first reviewing the

published research on the wider framework of which they are part of: the Investment Process.

In Table 1, the findings of 6 studies on the multiple-stage investment process are presented.

These investigations all point out that process efficiency combined with deal selection skills and the

relevant investment criteria are crucial for financial success. Especially since, according to Fried

and Hisrich (1994), the VC investment decision-making process is also designed to reduce the risk

of adverse selection.

However, two factors make previous research inconclusive. First, according to Hisrich and

Peters (2002), the evaluation process combines objective information gathering and analysis with

the VCs intuition, gut feeling and creative thinking that is difficult to capture via traditional

research methods. Second, recent research suggests that VCs lack a strong understanding of how

they make investment and divestment decisions (Zacharakis and Shepherd, 2001).

Nonetheless, past studies have made a number of important assertions on the links between

investment process and investment criteria that could be also observed in our data. Firstly, Hall and

Hofer (1993) presents different criteria used at each of the screening and due diligence stages,

thereby supporting our observation that VCs tend to adapt their criteria at each stage of the

investment process. Secondly, since Shepherd et al. (2003) states that most VCs also adapt their

decision process to include experience gathered, in support of Mainprize et al. (2002), VCs may

benefit from structuring their investment processes so as to limit certain inherent potential biases

discussed in Section 2.6, which hamper retrospection analysis and experience building.

2.2. Data Sources: background

Previous studies have relied on data gathered via multiple investigations methods. Table 2a

presents a taxonomy compiled from findings of past studies on the investment criteria and their

investigation methods. Almost all studies rely on data collected via ex-post information gathering

methods reporting only espoused4 selection criteria, while only Kaplan et al. (2005a) is based on ex-

ante (e.g. Enacted) data.

In line with Sandberg et al. (1988), Bar et al. (1992), Muzyka et al. (1996), and Shepherd

(1997), which all question the validity of ex-post questionnaire based research, we develop the

following proposal:

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Hypothesis 1: Espoused4 criteria data are different from enacted5 criteria data and

a-posteriori reported6 criteria data, which leads to significant differences in

analyses results depending on the data source used.

Support for hypothesis 1 would also credit research presented in Zacharakis (1995) and

Zacharakis and Shepherd (2001), which asserts that VCs, like many experts, appear to be poor at

ex-post introspection. Indeed, a-posteriori, VCs seem to overestimate their ability to predict the

future financial performance of potential deals.

2.3. Criteria linked to investment decision

According to Fried and Hisrich (1994), the VCs investment decision is crucial and bears

serious adverse selection risk due to the investments illiquidity nature. While Table 2a provides an

overview of a number of studies that investigated the VC investment criteria and their method of

investigations, Table 2b summarises the investment decision criteria identified in some of these

studies. Past research defines mainly four types of selection criteria in order of importance: the

competency of the management team, the attractiveness of the market, the attractiveness of the

opportunity (service or product), and the deals terms.

From the criteria presented in Table 2b, we select the 14 criteria that are present in our

database to develop the following hypothesis:

Hypothesis 2a: The 14 selection criteria Management, Innovation Potential, Stage of

Product Development, Resistance to Risks, Scalability, Barriers to

Entry, Existing Customer Base, Customer Potential, Potential for

Partnerships, Market Potential, Competition, Financials, Exit

Potential, and Valuation Attractiveness are statistically significant

predictors of investment.

Supporting evidence for hypothesis 2a would further credit the research methods used by

past researchers presented in Table 2a. It would also support the findings of the studies presented in

Table 2b. However, only some of these 14 criteria might be significant predictors of VC

investment. This may support Clarysse et al. (2005) that states that financial investment criteria are

more important than human resource criteria.

In addition, Burch (1986) comes to the conclusion that VCs would prefer an average

product/service idea with a top management team rather than a good product/service idea with a

4 As per Shepherd (1999), espoused criteria are the criteria VCs report or state they use when evaluating new venture proposals 5 As per Shepherd (1999), enacted or in-use can be defined as actual or truly used 6 As per Shepherd (1999), a-posteriori reported can be defined as reported via questionnaire or interview after the event

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bad management team. Kaplan et al. (2006) contradicts this finding by implying that this is a bad

strategy.

Rah et al. (1994) finds that Market Attractiveness is by far the most predictive investment

criteria (30% more predictive then management capabilities).

Wright and Robbie (1998) claims that VCs place considerable emphasis and scrutinize in

detail of all aspects of a business. This normally includes sensitivity analysis of financial

information, discussions with personnel, and assessment of a great deal of intangible and subjective

information.

Interestingly, two studies report that the criteria used in different stages of the due diligence

process vary. Wells (1974) found that different criteria were applied at the screening and the

evaluation phases, moving from broad questions, such as portfolio fit, etc., to more deal specific

ones. Whereas, Hall and Hofer (1993) also identifies the two key criteria most used for the initial

screening stage: a) fit of the venture seeking financing with the VC firm's investment guidelines,

and b) long-term growth and profitability of the industry in which the business will operate.

According to them, however, in the proposal assessment stages, the key criteria change to: 1)

source of the business proposal that played a role in the VCs interest in the plan, and 2) proposal

previously reviewed by persons known and trusted by the VC.

To verify these two studies findings, we formulate the following hypothesis:

Hypothesis 2b: Selection criteria importance change at each stage of the due diligence

process.

Supporting evidence for Hypothesis 2b would credit research that link the use of different

criteria to different stages within the due diligence process. The supporting evidence would also

cast some questions on the more encompassing studies that do not take this observation under

considerations when making their analyses.

More generally, although all these studies determine certain investment criteria, contrary to

this study, none actually validate whether the VCs are right to use these criteria to optimize their

success rate.

Furthermore, just thinking that only these selection criteria are the most significant for VC

investment may be too limitative. Other factors, such as changes in the environment or market

conditions, may influence the venture (Abell 1978, Aaker and Day 1986). Also, requirements for

market success are likely to change with market evolution (Abell 1978, Shepherd et al. 2000).

Besides, Guidici and Paleari (2000), Inderst and Mller (2002), and Rider (2005) all report that

Market Conditions influence all aspects of the relationship between the VCs and the venture, and

specifically Valuation, Investment Terms, and Risk/Return expectations. Further empirical research

is certainly necessary in this area.

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2.4. Criteria linked to success

Studies based on a-posteriori collected information demonstrate that VCs are successful at

predicting new ventures future successes (Dorsey, 1979; Sandberg et al., 1988; Kahn, 1987).

Riquelme and Watson (2000) presents a taxonomy of criteria associated with small and medium

sized company success. These analyses tend to indicate that emphasizing the qualifications of the

management, intensifying cooperation between the VC and portfolio companies, and ensuring a

strong (minority) shareholder position of VCs coincide with above average success (Schefczyk,

2001).

In Table 2c, we summarize the success criteria identified in a number of studies on venture

investing. From the criteria presented, we select the 14 criteria that are present in our database and

formulate the following hypothesis:

Hypothesis 3: The 14 selection criteria Management, Innovation Potential, Stage of





predictors of venture success.

Support for Hypothesis 3 would credit research presented in Table 2c. It would also support

the only correlational study, from Schefczyk (2001), that points out a possible correlation of one

selection criterion, emphasizing portfolio companies' managers' qualifications, to success.

While most VCs try to invest in ventures with both strong businesses and strong

management, some VCs claim to weigh one or the other more heavily (Kaplan et al., 2006).

Although it is possible for founders to adapt their style and become successful at running a

larger business, these founders often have neither the interest nor the skills necessary to do so

(Jayaraman et al., 2000). Stevenson and Jarillo (1990) argue that different skills are needed to

effectively manage the entrepreneurial challenges of a start-up versus the later administrative

challenges of an established firm. Hambrick and Crozier (1985) finds that successful start-ups are

more proactive in adding managers with more experience.

2.5. Criteria linked to survival

Table 2d offers a summary of the research findings on the investment criteria predictors of

company survival or non failure. Taking from this table the criteria for which we have data, we

formulate the following proposition:

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Hypothesis 4: The 14 selection criteria Management, Innovation Potential, Stage of





predictors of venture survival.

Support of Hypothesis 4 would further credit the research made by Shepherd (1999) which,

among others state in Table 2d, identifies survival predictors for ventures and ranks them in order

of importance: (1) industry related competence, (2) high educational capability, (3) competitive

rivalry, (4) key success factor stability (i.e. Barriers to entry), and (5) lead time as innovators.

Furthermore, in line with some of the criteria mentioned above, according to Brderl et al. (1992)

and Lee and Lee (2004), failure of technology based ventures could be categorized into three

groups: (1) personal traits of the entrepreneur; (2) strategies and resource capabilities of the

venture; and (3) environmental conditions of a new venture.

2.6. Limitations encountered in past research

A number of publications raise suspicions on the VCs conspicuously accurate predictive

skills captured via a-posteriori research (Hofer and Sandberg, 1987; Hall and Hofer, 1993). Recent

studies even exist on the research limitations and possible biases in the field of VC investment

criteria ex-post analyses (i.e. Muzyka et al., 1996; Shepherd, 1997; Rider and Tetlock, 2005). These

studies highlight 6 key potential research limitations:

(1) Data accessibility issues

Conducting large-scale studies by gathering data about the investment process, criteria, and

investment financial performance of many VC firms is challenging due to the scarcity of reliable

unbiased data.

This lack of information is not surprising since VCs have several incentives to keep their

data opaque, including, amongst other things, to protect their competitive edge, to mitigate greater

potential fiscal scrutiny from their local tax authorities, and to avoid easy benchmarking to their

competitors, not to forget the well documented survivorship bias resulting from unsuccessful funds

going into liquidation without ever having published performance data. Also to be mentioned is the

issue of the time required to fill-out questionnaires when much research agrees that the scarcest

commodity a VC has is time, not capital (see e.g. Gladstone, 1988 and Quindlen, 2000).

Furthermore, as pointed out by Muzyka et al. (1996) and Birley et al. (1994), in sharp

contrast to the United States where a tradition of answering candidly to detailed questionnaires is

well established, European VCs can be much more restrictive with respect to cooperating with

researchers. In addition, European VCs may be somewhat annoyed by the abundance in recent

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years of questionnaires sent indiscriminately by researchers attempting to bring clarity and

transparency to what some VCs often view as trade secrets (Muzyka et al., 1996).

(2) Reporting bias issues

Most past studies are based on VCs retrospective self-reporting (e.g. Tyebjee and Bruno,

1984), interviews and verbal protocols, and statistical analyses (see Mainprize et al., 2002, for a

review), or questionnaire responses rather than actual evaluations (for example, MacMillan et al.,

1987 and Robinson, 1987), and observations in laboratory setting, where typically VCs are asked

by researchers to evaluate deals and decide if they would invest, (Beim and Lavesque, 2004;

Mainprize et al., 2002). However, Hofer and Sandberg (1987), and later Shepherd (1997) and Rider

and Tetlock (2005), have shown that results obtained using these methodologies may diverge from

reality as VCs may not report accurately what they do and how they think. Even fewer studies

address whether VCs are right to use the investment criteria they say they use.

Indeed, according to Shepherd et al. (2003), decision-makers, especially experienced ones,

tend to overlook established objectives and instead rely on intuition and various heuristics when

deciding. Cognitive biases that may affect the way VCs address decisions include overconfidence

and anchoring, one form of which is to follow past practice and shun innovative alternatives

(Keeney, 1992).

Self-reporting has been shown to overstate the number of criteria actually used and to

understate the weighting of the most important criteria when compared to more sophisticated

decision-making techniques (see Stahl and Zimmerer, 1984; Riquelme and Rickards, 1992).

Ex-post methodologies (e.g. interviews, questionnaires, or surveys) assume that VCs can

accurately recall and explain without biases their own decision process. Zacharakis and Meyer

(1998) propose that methods that use surveys to ask VCs to revisit previous choices and use those

choices as a base to assess their decision process are biased.

(3) Investment criteria identification issues

Depending on the scope of the studies7 the most salient investment criteria vary (see

Riquelme and Watson, 2000). In addition, investment criteria may vary with market conditions (see

hypothesis 4 presented in section 2.4), VC investment agenda, VC style, etc. Therefore, to identify

the relevant investment criteria is challenging.

(4) Sample selection representativeness issues

Another important issue is the potential for sample selectivity (or non-random) bias in the

data. Since we are using sub-samples of the full database, it is important to show that these

7 Including or not deal sourcing, initial screening, ratings of business plans, due diligence, etc.

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subs-samples are representative of the average deals applying for finance in our data set. The

Heckman procedure tests if such potential bias is present.

Unfortunately, there is a large number of other potential selection biases (e.g. is the deal

flow we analyze representative of all VCs deal flows, are the deal rankings representative of the

way other VCs rank deals, etc.) that can not be controlled for within our data sample. These biases

have to do with heterogeneity, i.e. VCs are not all alike and do not all follow the same objectives

with the same risk/reward investment profiles. Such biases would also influence more general

studies. We only have data from one of them so we can not generalize our findings to the industry

as a whole.

(5) Data pairing issues

Researchers lack paired ex-ante data on VC deal selection skills and the resulting

investment performance (Schefczyk, 2001). Therefore, so far, no meaningful comparison between

relevant investment criteria and subsequent performance can be found in literature.

(6) Decision model construction issues

Past research on the modeling of investment decision suggests that VCs do not necessarily

have a strong understanding of their decision criteria (e.g. Shepherd, 1997; Zacharakis and Meyer,

1998) and, instead, rely heavily on intuition (e.g. Hall and Hofer, 1993; Zacharakis and Meyer,

1998); face environments that are not conducive to learning and/or improving judgment (e.g.

Shepherd and Zacharakis, 2002); and may need to use decision aids to significantly improve their

judgment and decision making (e.g. Zacharakis and Meyer, 2000; Shepherd and Zacharakis, 2002).

However, decision models found in previous studies, such as Mainprize et al. (2002), are

limitative in their capacity to encompass all elements affecting VC decisions. Also, to present the

diversity of VCs individual and organizational deal-selection practices with a common model is an

issue.

III. Research Method

3.1. Empirical context

Our source

Most of our unique data comes from one VC company that was established in September

1996 in Switzerland. In 1998, it employed about 10 professionals, including staff. By August 2001,

its team was composed of 5 partners, 8 investment managers, plus assistants, accountants and

secretaries; a total of 25 professionals. Section 4.1 should demonstrate the representativeness of this

VC.

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3.2. Data Collection

This study relies on the combination of analyses done on the following six data sources:

(1) Interview Data

Our first source of data consists of interview data on how the VC structured its investment

process and on its modus operandi. This data was collected via multiple interviews with its

investment managers and partners between 1999 and 2001. These interviews were intended as very

open discussions in order to capture a maximum of information. Subsequent interviews helped

obtain any necessary clarifications.

These interviews serve as the basis for Section 4.2 that describes a VCs investment process

and which is presented in Table 3. The VCs feedback to our studys preliminary findings also

helped us to further develop our understanding of investment process and investment criteria by

providing some reality checks and an opportunity to collect additional information and

perspectives.

(2) Deal Flow Data

The second source consists of the empirical data collected at the time of the due diligence

on the 2219 deals it screened between September 1996 and August 2001; all in all about 20

columns of potential information for each deal. Much information on the deal flow specifications

can be found in Table 4.

The quantity of deal flow information available for each 2219 observations varies

somewhat, as the investment opportunities that were more carefully evaluated have more complete

information (see Table 5). To improve the completeness of our database, in some cases, additional

information was hand-collected from the ventures websites, public and private databases, etc.

(3) Ex-ante data

The third data source consists of the ratings on 14 evaluation criteria selected and defined

by the VC in 1999, gathered on the deals at the time of their individual due diligence. The criteria

can be best illustrated by the 14 main questions the VC asked itself for each investment

opportunity:

Investment Criteria and Description Questions

Criteria Description question Management 1 Management How capable is the management to execute its business plan?

Product / Service 2 Innovation Potential How innovative is the product or service? 3 Stage of Product Development What is the current stage of the product development? 4 Resistance to Risks How risky is the project and what is the worst case scenario? 5 Scalability How scalable is the business? 6 Barriers to Entry How much is the product or company protected from copiers?

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7 Existing Customer Base What is the quality of the current customer base? 8 Customer Potential How attractive/large is the potential customer base? 9 Potential for Partnerships What is the potential for this VC to create value added for the company

via partnerships with vendors, other portfolio companies, etc.? Market 10 Market Potential What is the market potential for the product or service? 11 Competition What is the status of the competitive landscape? Financials 12 Financials Are the financial plans reasonable and attractive? 13 Exit Potential What is the likelihood of having a successful exit via trade sale or IPO? 14 Valuation Attractiveness How attractive is the investment proposition for the VC?

Source: our reference VC

In our database, these ratings are available for a sub-sample of 198 deals.

The VC ascertained that the grades recorded for each project are the grades attributed to it

at the last stage reached within the due diligence process. Hence, if a project was defined as having

a management team with a grade of 2 (not very good) at the first evaluation stage, but then, during

the due diligence process, this VC revised its opinion on the management (for example because it

was strengthened by the arrival of an experienced CFO), the deal may have ended with a grade of 5

for Management (which would be captured in the database).

This VC also allocated to each criterion an investment decision weight. All criteria

multiplied by their respective weight could be added to compute a deals overall attractiveness

score. Unfortunately, as this VC was still experimenting with its evaluation model, it did not

actively modify the weights from deal to deal so that the variation of the weights in the ex-ante

database can not be properly analyzed. During our interviews, this VC informed us that it intended

to analyze these weights at some later date to refine and improve its decision process, provided

enough quality data was then available.

(4) Outcome data

The fourth data source consists of information collected on the performance of the deals,

the number of successive financing rounds, and financial outcomes (i.e. trade sale, IPO, still

private, or bankruptcy) on the 2219 deals between September 2001 and December 2004. The

outcome data was collected by consulting a number of VC specific databases (e.g. Venture One,

Venture Economics, Capital IQ, etc), company websites or web page archives (such as

web.archive.org) in order to establish whether the firm became a successful investment, is alive but

not a successful VC investment, or was bankrupt by December 2004. The financial outcomes of

the ventures are used in Section IV to evaluate whether certain investment criteria could be

predictive of performance.

(5) Market Data

Because stock market indices and deal flow are highly correlated, our data set does not

allow us to discriminate among (1) quarterly variations in market conditions measured by European

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IT stock index, deal flow (as a proxy for demand for VC money), other VCs investment rate (as a

proxy for offer of VC money, survival, and success rates), (2) variations in this VCs activity

measured by the number of deals encountered every quarter, and (3) variations in the VCs

experience (as a function of time, the cumulated number of investments realized or exited).

As shown below, four time periods have been defined. Between September 1996 and

August 2001, the TIME industry, this VCs industry focus, underwent first a major expansion until

end of 1st Quarter 1999. A boom period followed from the 2nd Quarter 1999 to the 2nd Quarter 2000.

The boom period lead to a correction period between the 2nd Quarter 2000 and the 4th Quarter 2000.

Finally, the bust period started in the 1st Quarter of 2001, which is the last quarter for which we

have deal flow and/or ex-ante data.

EXPANSIONBOOM

FALLBUST

1996 1997 1998 1999 2000 2001

our VC's TIME industry focus

Figure 1: TIME Industry Chronology

(6) Ex-post data

The sixth data source consists of 98 answers to an ex-post multiple-choice survey 8 with 32

questions completed in August 2001 by the VCs investment managers. These 98 deals, 79 rejected

projects plus 19 investments, are all part of the 198 deals in the deal flow for which ex-ante

investment criteria ratings are available. The questions include the 14 investment criteria answered

ex-ante at the time of due diligence. The 98 deals were initially screened between the first quarter

of 1999 and the second quarter of 2001; e.g. 1 to 5 years before the questionnaire was answered.

The 19 ventures in which the VC invested involved in some cases multiple rounds of financings.

3.3. Validity, analysis and reliability

(1) Data sample specificity

As data from only one VC is available to us, our findings can only be specific to this VC

and no generalization is possible. The two main questions that can not be answered are: (1) Is the

VC representative of successful VCs in general and therefore can this studys findings be

8 For more information on our ex-post questionnaires design and an initial analysis, please review Birrer (2002). Alexander Birrer is the

author of the questionnaire also used in this study.

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generalized to successful VCs?, and (2) Are the subset of VCs deal flow for which we have data

representative of the entire deal flow of this VC?

(2) Data sample validation

To support our analyses and validate our findings, of the following tests are done.

First, a correlation analysis between the ex-ante and ex-post data is made to determine

whether any salient difference exists. Ideally the analysis of the ex-ante and ex-post data should

lead to the same conclusions. This can however only be the case if the correlations between the two

data samples are high. If the correlations are low, we need to concentrate our analyses on the ex-

ante data, which we deem less prone to some of the biases presented in Section 2.6.

Second, to address the possibility of selectivity bias, we use the Heckman two-step

estimation procedure, or Heckit model9, on our ex-ante data. This analysis enables us to eliminate

biases in regression weight calculation due to censorship.

As describe by ., the following conditions need to be fulfilled to built the Heckman

model: 1) create a probit model for the propensity u, 2) compute the Mills ratio (lambda ) from u, 3) use the Mills ratio and the model variables x to build a regression model using only the

uncensored records, record with non-zero v, and iv) apply the model to the whole dataset

suppressing the dependence on x.

The Heckman model posits i) a value w for participating in an activity (working, stealing,

etc.), ii) a propensity u for engaging in this activity, and iii) a latent correlation between

participation and value. If the correlation between participation and value is model by explicit

variables, the correction is not needed and conveniently disappears. Nonetheless, censoring does

not always hurt a model. It is sometime possible to fit an accurate model using only the uncensored

records. Censoring hurts when it is biased in the sense that it is dependant on the independent

variable w. As previously said, the Heckman model eliminates bias caused by censoring on a

variable correlated to w and the scheme is defined as follows:

V = s(u) w

where:

V: censored value. It is a directly observable value censored by a biased selection process.

w: Value. Latent Gaussian r.v.

s( ): Step function. Equal to 1 for positive u; 0 otherwise. s(u) is observable

u: selection propensity. Latent Gaussian r.v.

Having described the scheme, the Heckman model is the following:

w = xt +

9 Heckman developed the Heckit model to investigate the value of womens wage (J. Heckman, 1974, 1979). It uses a binary probit model, a kind of single-neuron neural Network.

16

u = xt + where

X: vector of observable model variables possibly governing w and u.

(beta) and (gamma): vectors model of coefficients governing "w" and "u". (epsilon) and (eta): latent variables governing w and u. Having described the Heckit model, we can apply this rational to our data. Here, this

procedure allows the identification and isolation of any sample specific bias due to the potential

non-representativeness of our datas sub-samples on the total data sample. For this test, it is

necessary to segregate our data by dependent variables (i.e. invested/not invested, successful/not

successful, alive/bankrupt, etc.) and then compare each cluster groups so as to determine if

significant differences exist. The influence of the unbiased criteria (e.g. Predictors) on the possible

outcomes (e.g. Dependent Variables) is then highlighted as the inverse Mill's ratio.

So, in the first step, a probit regression selection model is estimated to calculate the inverse

Mill's ratio (because the error term of this model is normally distributed, which is one of the main

assumptions underlying the Heckman model) with a dummy variable (i.e. 1 if this VC invested; 0,

otherwise) as a dependent variable (Equation 1).

Our selection equation is as follows:

Selection equation 1: Dependent variables (DVi)i = a0 + 1 n (exa1) + 2 n (exa2) + 3 n (exa3) + ei ... Description of the variables Control variables a1 Source a2 Age a3 Country of Origin a4 Industry a5 Develop. Stage a6 Raising first round a7 Known Founder a8 Serial Founder a9 Successful Founder a10 Financed a11 OtherVcLeads_AK a12 OtherInvestorCommitted_AD

Predictors (exai) exa1 Financials exa2 ExitStrategy exa3 ValueAttractiveness exa4 RiskResistance exa5 MarketPotential exa6 Competition exa7 Innovation exa8 Management exa9 Scalability exa10 PotentialCustomers exa11 BarriersToEntry exa12 Partnership exa13 ProductDevelopment exa14 ExistingCustomers

Dependent variables (DVi) slctd Invested by this VC vcsbsq Financed by other VCs sccss Successful investment srvl Live company in Dec.04

n = Lambda n

Note that in the Heckman procedure, the residuals of the selection equation are used to

construct a selection bias control factor, which is called Lambda (), which is equivalent to the Inverse Mill's Ratio. is a summarizing measure that reflects the effects of all unmeasured

18

characteristics, which are related to observed selectivity biases to be isolated from our investment

criteria into and additional variable, Lambda..

In a second step, the estimated probabilities (Lambda n (n)) (including the inverse Mill's ratio to account for selection bias) are used as regressor in an Ordinary Least Square (OLS)

regression model to estimate the probability that a venture will receive VC backing. This model

employs the White (1980) robust standard error procedure that accounts the sample selectivity

separately via a heteroskedasticity10-consistent covariance matrix estimator (Equations 2)11.

Equation 2 - regression or observation equation: y* (unobserved)='w+u u~N(0,1 )

where y is observed (equals 1) if and only if y* is greater than 0; and y is not observed if

smaller or equal to 0. The variance of u is normalized to 1 only because, not z*, is observed. The

error terms, u and e, are assumed to be bivariate, normally distributed with correlation coefficient,

, and and are the parameter vectors.

The Heckman 2-step procedure should indicate if any selectivity bias due to sub-sampling

is present.

(3) Univariate analysis

Using our ex-ante data, we derive the significance of each selection criteria used via linear

regression for 4 situations. This gives us a preliminary description or summarization of individual

criteria. Thanks to our univariate analysis of multivariate outcomes, we can explore each variable

separately. We look at the range of values, as well as the central tendency of the values. We also

describe the pattern of response to the variable. We are conscious that linear models are very coarse

approximations of the Boolean logic underlying the mental processes. However, they are very

parsimonious, which is a key advantage for small data sets.

Yet, because deal characteristics may be inter-correlated, these univariate analyses do not

say a lot about the independent contribution of each criterion to the decision. For instance, this

VCs preference for network deals may actually be a preference for deals with good management or

conversely.

When evaluating possible limitations of our analysis, we realize that univariate analysis

alone may not be sufficient, especially for our complex yet in some case limited data sets.

Additional, and sometimes even contradictory, results may be found using multivariate analysis.

During the course of data analysis, a common practice is to include in multivariate analysis only

those variables that are statistically significant in univariate analysis. Such a habit is risky as some

10 A sequence or a vector of random variables is heteroskedastic if the random variables in the sequence or vector may have different

variances. The complement is called homoskedasticity. Source: Wikipedia. 11 The results are reported with and without allowing for sample selectivity.

19

variables not significant in univariate analysis may become significant in multivariate analysis. In

this study, we identify, with examples, four possible scenarios in which the above situation could

occur: (1) the effect of unbalanced sample size; (2) the influence of missing data; (3) an extremely

large within group variation, relative to between group variation; and (4) the presence of

interaction.

Further analysis could include principal component analysis with multivariate regression

analysis. It is highly probable that these analyses would yield entirely different findings. However

such analysis will not be covered in this version of the paper but could be part of future research.

IV. Results and Discussion

Our data can be analyzed from four different perspectives: (1) Deal Flow analysis, (2)

Investment Process Analysis, (3) Investment Criteria Analysis, and (4) Decision Model

Comparison.

4.1. Representativeness Analysis

This VC invested about Euro 140 million via three funds and created more than Euro 342

million in capital gains for its investors, generating an audited internal rate of return (IRR) of more

than 30% with an average exit multiple of 3.4. It managed to raise a new fund in 2000-2001 when

fundraising conditions were not favorable, and is still a very active investor that has made a number

of successful exits between 2001 and end of 2004.

The deal flow followed the variations of the stock market with a time lag of about one

quarter. It ranged from about 25-50 deals per quarter to a peak at 250-350 mostly early stage deals.

By the end of August 2001, this VC had invested in 62 individual ventures and made 17

follow-on investments selected from within its deal flow of 2219 deals.

The 79 investments resulted in 16 IPOs and 15 trade sales. From the remaining

investments, by the end of August 2001, only 17 firms had ceased activity, while 14 were still

active portfolio companies that in some cases had received additional financing by December 2004.

4.2. Deal Flow Analysis

Table 5 divides the information collected by the VC on each potential investment according

to a) information about deal focus in terms of Country of Origin and Industry, b) information about

the companys development stage, such as its Financing Stage, if it was Raising 1st Round

financing or if it was a Follow-up Deal, its Age, and if it already Has Revenues; c) information

about the origin of the deal, its Source, and d) information about what other VCs did, if Other VCs

lead the deal and whether Other Investors Committed; and e) information about the entrepreneur,

20

whether he was a Known Entrepreneur to the VC, a Serial Entrepreneur, and/or a Successful

Entrepreneur with a positive track record.

Table 6a and Table 6b compare the survival and the financial outcomes of the deals

received by this VC, and whether they have been financed by this VC, other VCs, or not financed at

all. Overall, it shows that this VC, on average, made 52% of Good Investment Decisions (GID),

significantly better than other VCs (29%) active during the same period (Table 6c), which confirm

this VCs above average selection skills.

In addition, after formally implementing its structured investment process in August 1999,

this VC showed a relative resistance to worsening market conditions: its success rate decreased to

36% (14/35) from 69% (18/26) while, in parallel, that of the average VC decreased steeper to 3%

from 54% (35/65) (see Table 6a).

The potential success rate of other VCs, had they followed this VC investment schedule,

would have been between 31% and 36% depending on the method, leaving an over-performance for

this VC of 16% to 21% (52% minus 31% to 36%) (see Table 6b), whether we take into account all

deals or only deals that we know became successes. Besides, this VCs over-performance also

exists within the Boom (fraction of deals invested: 9/16 = 56% vs. 13/61 = 21%) and the Fall

(fraction of deals invested 4/11 = 36% vs. 1/32 = 3%) of the financial markets (Table 6a). This

would tend to show that this VC was more successful than other VCs in selecting future profitable

deals.

Overall, as demonstrated in Table 6b, this VCs performance should qualify it as a

successful VC worthy of being considered for an insightful academic study.

4.3. Investment Process Analysis

Within its investment process, this VC defined itself 7 due diligence stages. The most

selective stages were the Manager (only 27% of the deals passed through), then the Team (32%),

then the Screening (61%) and finally the Investment Committee (IC) (80%). Table 5d shows that all

due diligence stages except the IC were good at identifying future successes from future failures,

thereby making good investment decisions. It seems that the Investment Committee (IC) actually

made, proportionally, the worst rejection decisions.

4.4. Investment Criteria Analysis

Background

Researchers generally agree that the VCs espoused criteria are not the sole basis for the

VCs investment decisions (Pries and Guild, 2002). A priori, VCs decision to invest in a specific

deal, or to promote it to the next level of due diligence, is an obscure, implicit mental and social

process integrating information about deals, market conditions, and VCs fund requirements. Many

factors may enter into consideration when a VC makes an investment. The ultimate objective of

21

decision analysis is to translate a decision into a structured set of explicit rules. If we suppose that

financial success is a major objective of VCs, we can expect these rules to be predictive for success.

For VC investors, a great differentiation can be made between survival goals and success

goals, as companies pursuing survival goals may require different strategies and resources than

those aiming for a rapid financial success. Accordingly, VCs focused on the maximization of

financial profits for their investors and themselves may pursue different investment strategies as

they get relatively little rewards for creating strong stable businesses that can not be sold quickly

with a large profit

As discussed above, the investment criteria can be examined from at least 5 angles: (1)

What the VC, during our interviews, says it does its Espoused Criteria; (2) what it does its

Enacted Criteria, and (3) what other VCs do differently, (4) what it reports ex-post to have done via

an ex-post questionnaire, and (5) what this VC should do.

(1) Interview Analysis

From our interviews we obtain this VCs espoused investment decision rankings for its

14 criteria by order of importance, as follows:

High Importance: (1) Management, (2) Scalability, (3) Barriers to Entry, (4) Exit

Potential.

Medium Importance: (1) Financials, (2) Valuation (Attractiveness of Valuation),

(3) Innovation, (4) Potential for Partnerships, (5) Market potential.

Low Importance: (1) Risk, (2) Competition, (3) Potential Customers, (4) Product

development, (5) Existing Customer Base.

We also learn that this VC explicitly considers as key investment criteria: a) how, at the

pre-screen/sourcing stage, the deal fits with its investment focus in terms of Country/Location,

Industry, and Financing Stage; and then b) how the deal scored on its 14 quantitative evaluation

scales, at all stages. Other information recorded in the database such as the age of the firm, the

source of the deal, the entrepreneur profile, and the investment round were not spontaneously cited

as investment criteria. We observe however that this VCs 14 investment criteria fall within 4 main

categories already identified in many previous publications (i.e. Vinig and de Haan 2001,

Zacharakis 1995).

Criteria analysis - Differences between 3 criteria types

Hypothesis 1: Espoused criteria data are different from enacted criteria data and a-

posteriori reported criteria data, which leads to significant differences in analyses results

depending on the data sample type used.

As described in Section 3.3, to test Hypothesis 1, we perform a comparative analysis of the

ex-ante/ex-post correspondences in order to evaluate the extent of potential biases. As presented in

22

Table 7, the low correlation between ex-ante and ex-post evaluation criteria based on their ability to

predict the VCs investment, deal survival and/or success shows that the data contained in the

ex-post questionnaire answers does not accurately reflect the VCs written deal evaluation at the

time of due diligence.

The scales that deviate the most between the ex-ante and the ex-post data are for the more

subjective criteria: Management, Financials, Exit Potential, Valuation, Potential Customers, and

Existing Customers. This observation is consistent with Dubini (1989), Goslin and Barge (1986),

and Gorman and Sahlman (1989) that assert that the evaluation of subjective criteria such as

Managerial Capabilities is challenging (see also Rah et al., 1994).

Looking at the potential causes for these deviations, from Table 6b, we observe that ex-post

knowledge does not help this VC to predict other VCs investment decision, suggesting that the

bias is specific to this VCs ex-post knowledge of its own decision, not of other VCs decisions. In

Table 7b, we test two potential reasons for this bias:

(1) Ex-post knowledge

Ex-post evaluation could be influenced by additional knowledge about the financial

outcomes of the deals. If so, lasting interaction with financed ventures should result in higher extra-

knowledge, and thus in more bias. Accordingly, Table 7a shows that the discrepancy between

ex-post and ex-ante ratings significantly12 decreases when only considering the rejected deals. This

tends to confirm that ex-post knowledge did bias retrospective questionnaire responses.

(2) Forgetting

Ex-post data could explain an increasing fraction of investment decision variance at later

due diligence stages. This suggests that ex-post information underestimates what happens at earlier

stages, probably because this VC better remembers deals it evaluated more in detail. Also, this VC

might be inclined to forget information about older deals and better recollect information on more

recent investment opportunities. Indeed, a majority of correlations get significantly13 better when

only recent deals are considered (Table 7a). In addition, the ex-post knowledge bias mentioned

above may not have taken affect yet since the outcome is still unknown for these younger deals.

Overall, the discrepancies between VCs ex-ante and ex-post evaluations seem to have

several causes, including forgetting and a posteriori knowledge. Although conclusions have to be

drawn carefully because our sample size is modest, it tends to support as Zacharakis and Meyer

(2001), which observes that when more or newer information becomes available to a particular

decision, the VC's ability to introspect about that decision process diminishes.

As Hypothesis 1 is supported, we decide arbitrarily to pursue our data analyses using

primarily our ex-ante data to reduce ex-post knowledge related biases.

12 p

23

(2) Enacted Criteria Analysis

Predictors of Investment

Hypothesis 2: The 14 selection criteria Management, Innovation Potential, Stage

of Product Development, Resistance to Risks, Scalability, Barriers to Entry,

Existing Customer Base, Customer Potential, Potential for Partnerships, Market

Potential, Competition, Financials, Exit Potential, and Valuation

Attractiveness are statistically significant predictors of investment.

Before presenting our data analyses results a word of caution is necessary. Indeed, our

results could be also biased by the VC specific investment motivation as it may not care about

ventures survival, but only about spectacular successes. In this case, for the VC, it may be optimal

to invest in deals with lower survival probability if the return (e.g. Internal Rate of Return or IRR)

probability is high. However, during our interviews, this VC did not specifically express this as a

strategic objective, although it was aware of its effect. The VC explained that at the time of

investment its return expectations were high for all deals and it was equally enthusiastic about all

initial investments without knowing which ones would be spectacularly successful.

As described in Section 3.3, via univariate analysis using our ex-ante data, we determine

the criteria that were both decisive (e.g. used by this VC for its investment decision), predictive of

survival, and/or of success, and encounter the following four situations:

Decisive and Predictive

As shown in Table 8, both the quality of the Management and the Financials of the

company are Decisive and Predictive since they are significantly correlated to this VCs decision to

invest, the subsequent investment by any VC, and the deals survival. In addition, they are also

decisive for other VCs, which suggests their universal value, in line with published literature (i.e.

Kaplan and Strmberg, 2004; Kaplan et al., 2005a). Finally, they correlate positively with our ex-

post data, suggesting that this VC also perceives them as a decisive investment criteria ex-post.

Accordingly, it is cited as a moderately important espoused factor by this VC, congruent with the

fact that they are decisive factors also for others (ex-ante and ex-post), in line with studies on VCs

espoused criteria (i.e. Hall, 1989, and Fried and Hisrich, 1994).

Management is decisive, cited as highly-important by this VC, and almost all

espoused criteria studies (i.e. Tyebjee and Bruno, 1984; Hall, 1989; Fried and Hisrich, 1994; and

Boocock and Woods, 1987). This would tend to show that VCs may attribute deal

successes/failures to management rather than to other deal characteristics. If so, one would expect

the VC to upgrade ex-post its ratings of the management of successful ventures. This assumption

could not be tested on this VCs deals due to the limited sample size and was not verified on all

deals since ex-post management ratings also do not correlate to survival or success.

24

Another explanation could be that, although financial success is important for VCs, it may

not be their only objective. Highly-rated management would be decisive because it predicts easier

work partners, fewer critics from investors, or even learning from entrepreneurs. To solve this

mystery, we would need more cases of paired ex-ante/ex-post ratings of portfolio companies

(successes and failures) and alternative measures of success, beyond financial success, such as

VCs intellectual and social satisfaction from working with each company.

Decisive, but not predictive

Financials, Potential Customers are decisive criteria but seem not to be highly

predictive of venture survival, especially in comparison to other criteria. However, from this VCs

ex-ante data, it is not predictive at all, whatever the model and the period considered. It is even

negatively correlated with success, which is consistent with Kaplan et al. (2006).

Predictive, but not decisive

Although it was cited as a highly-important criterion by this VC in our interviews and is

also often cited by other VCs, Barriers-to-Entry was not significantly used by this VC for its

investment decision, although it is predictive for venture survival.

How could this VC know it is a highly important criterion and not apply it? One

explanation could be that this VC only realized the importance of Barriers-to-Entry later on. This

VC might have used this information to update its espoused criteria only at the time of interview.

Such an update would be possible since Barriers-to-Entry correlates to survival and success ex-

post (see Table 7a).

Not predictive, not decisive

The criterion Scalability seems neither predictive nor decisive; but considered as a

highly important espoused criterion by this VC (see Table 9). Indeed, this VC does not use it

significantly for its decision, it is not useful as it is not predictive, but initially in our interviews the

VC claims that it is! When only looking at the ex-post ratings, Scalability appears predictive of

survival and success. Worse, ex-post, this VC has the right impression that it does not use this

criterion. During our feedback interviews, this VC could not give any explanation to this finding.

Therefore for Hypothesis 2, criteria Deal Value, Deal Source, and Follow-up

Financing are statistically significant predictors of VC investment, whereas Scalability is not

(even though this VC claims ex-post that it is).

Table 8 shows that the most significant predictors of the VCs investment are the quality of

the Management Team and the Financials outlook of the company. Next in line are the companys

Market Potential, Potential Customers, and Resistance to Risk. Therefore Hypothesis 2a could only

be partially supported since not all criteria were predictive of this VCs investments.

Not surprisingly, this VC preferentially selected deals that were: not from Eastern Europe;

in Financial Services and Telecom, but not in Telecom Services and Consulting; at a Public/Spin-

25

Off financing stage, but not at seed/start-up (even though the former were much rarer in the deal

flow); not raising their 1st round; sourced actively or from its network, not passively sourced; lead

by other VCs; in which other investors already had committed to invest; with a known, serial or

successful entrepreneur; and well rated on almost all 14 evaluation scales and factors.

Predictors of Success

Hypothesis 3a: The 14 selection criteria Management, Innovation Potential, Stage




Attractiveness are statistically significant predictors of venture success.

The univariate analysis in Table 5a shows some correlation between some deals

characteristics and success: Media industry; Pre-IPO stage; ratings of Risk, Competition (scale and

factor), and Partnerships. Among this VCs investments, we could also correlate Return on

Investments (e.g. ROIs) to deals; not from USA; not in Retail industry; Pre-IPO stage; not 2-3

years of age; and not with successful entrepreneurs. ROI was also correlated with ratings on

Financials, Competition, and Barriers scales, but only within the 80% confidence interval.

Therefore, Hypothesis 3a is supported, but only for a few criteria and with a lot of caution

due to the low sample size.

Although we suspect that Market Conditions may be a significant predictor of venture

success, we could not fully clarify our suspicion. Unfortunately, our 198 ex-ante rated deals only

contain 7 successes so the statistical significance is low, especially across periods.

While this VC decreased its investment size per deal with time, it kept its investment rate

per quarter almost constant. This was beneficial, since Market Condition was the best predictor of

success. For this VC, we cannot assess however if its good investment timing was the result of an

intelligent investment strategy or the automatic consequence of its funds inception dates.

First, VC-backed ventures achieve a higher survival rate than non-VC backed businesses

(Kunkel an Hofer, 1990, Sandberg, 1986, Timmons, 1994). Tyebjee and Bruno (1984) asks VCs to

evaluate previously examined plans on 23 criteria using a four-point scale and reduces these criteria

via factor analysis to five underlying dimensions namely (1) Market Attractiveness (size, growth,

and access to customers), (2) Product Differentiation (uniqueness, patents, technical edge, profit

margin), (3) Managerial Capabilities (skills in marketing, management, finance and the references

of the entrepreneur), (4) Environmental Threat Resistance (technology life cycle, barriers to

competitive entry, insensitivity to business cycles and down-side risk protection), (5) Cash-Out

Potential (future opportunities to realize capital gains by merger, acquisition or public offering). It

finds that investment decisions can be predicted from the perceptions of risk (e.g. new venture

failure) and return (e.g. financial performance).

26

Predictors of Survival

Hypothesis 4: The 14 selection criteria Management, Innovation Potential, Stage




Attractiveness are statistically significant predictors of venture survival.

Table 8 shows that Barriers-to-Entry, Resistance to Risk, Management, and Innovation

were the most predictive criteria, followed by Financials. Most of these criteria are related to risk

management so it could be logically expected to find them as greater determinants of venture

survival. Investors concentrating mostly on Resistance to Risk and Barriers- to-Entry

Company survival does not mean financial success, although they correlate across quarters

(coefficient .58, p

27

Therefore, we note that this VC seems to have different investment patterns than the

average other VCs and may have been more successful at adapting early to changes in market

conditions, a significant predictor of investment success. More research could be undertaken in this

area as well.

4.5. Decision models comparison

Zacharakis and Meyer (2000) focus on the task of screening venture proposals without

unduly rejecting high potential investments. Participants capacity to select the right investments is

compared to the predictions of (1) a bootstrap actuarial mode, structured to capture the cognitive

system of a decision-maker, which uses information factors previously identified by VCs as being

most important to making good investments decisions and (2) a second actuarial model based on

the findings of Roure and Keeley (1990), who identified predictors of success for technology

ventures. On predictions of success and failure, the bootstrap model outperformed all but one

venture VC (who achieved the same hit rate as the bootstrap model), and the Roure and Keeley

model outperformed over half of the VCs.

Zopounidis (1994) presents an overview of published VC investment decision models and

their relative performances. Mainprize et al. (2002) also presents an overview of the performance a

few models developed via conjoint analysis or plain bootstrapping. These two studies allow us to

compare the performance of this VCs actual decision model to other published models. The

outcome of this analysis is presented Table 9. It shows that this papers decision model fends

favorably to the decision models developed by researchers in laboratory contexts via various forms

of a-posteriori data collections and analyses.

Table 9 also shows that this VC, on average, made 52% of Good Investment Decisions

(GID), significantly better than other VCs (29%) active during the same period, which confirms its

selection skills. In addition, after formally implementing its structured investment process in

August 1999, it showed a relative resistance to worsening market conditions: its success rate

decreased to 36% (14/35) from 69% (18/26) while, in parallel, that of the average VC decreased

steeper to 3% from 54% (35/65).

A number of studies across a variety of decision contexts have found that such models often

outperform actual decision makers (see Camerer, 1981; Dawes, 1971; Osherson et al., 1997;

Zacharakis and Meyer, 2000). For additional detailed discussions on VC investment decision

modeling, please refer to Riquelme and Rickards (1994), Zacharakis and Meyer (2000), Shepherd

and Zacharakis (2003), Rider (2005), etc.

28

V. Conclusion and Future Research

This study analyses the investment process, investment criteria, and investment

performance of a single VC while attempting to limit known biases that have influenced the results

of previous studies. This study also attempts to tie its findings to the published theoretical

framework on VC investment criteria.

We show that, with access to ex-ante data, it is possible to explore and compare empirically

what a VC says it does, what it effectively does, what it should have done, and how it may differ

from what other VCs do.

Using our ex-ante data, we identify investment criteria that are both decisive for VC

investment, and/or predictive of deal future survival and/or venture subsequent success.

The comparison of the ex-ante data and the ex-post data highlights significant differences,

especially on the more subjective criteria. The differences confirm that our studys findings would

be substantially altered if they were solely based on the analysis of ex-post questionnaire answers.

This supports recent studies that highlight the poor introspection skills of VCs. Also, in support of

Zacharakis and Shepherd (2001), our findings confirm that in its ex-post questionnaire responses,

the VC displays overconfidence in its decision process and its predictive capabilities.

With regards to predictors of deal success, the criteria Business Potential and Barriers to

Entry were both significantly predictive. However, a deals financial success could have been

mostly determined by market conditions and/or country and industry, while the unique contribution

of the Deal Value factor to success prediction is nil. However, we feel that to support these findings

more research is needed with a larger dataset possibly from multiple VCs.

We note that most significant selection criteria of the VC were Management and

Financials and that the two criteria determining success were exactly the same. It proves that the

VC had correctly identified the right criteria to invest if it wanted to follow a strategy for success.

Although it did not directly admit it, it was obviously following such a strategy of success. This is

evidenced by its track record where few deals were outstanding success that more than

compensated for the numerous total write-offs in the portfolio.

With respect to predictors of deal survival, this study shows that a companys

characteristics could predict in part its survival 3 to 5 years later, irrespective of it receiving further

financing rounds and market conditions. Furthermore, Barriers-to-Entry was the most predictive

factor of company survival, followed by Business Potential (Market, Competition and

Innovation) and Deal Value.

The comparison of our investment decision model with those in published studies shows

that our model rates favorably with respect to investment successes, the main driver of a VCs

financial performance.

29

The use of checklists, scorecards, and templates for rating potential investments could be an

important initial step into formalizing an investment process. This area would benefit from further

definition of robust scales based on empirical results, as suggested by our findings in Section IV.

According to our results, scales should be constructed from ex-ante findings, since ex-post findings

do not give similar information structures. While ex-post introspection may help VCs improve in

the long term their performance, systematic recording of investment decision justifications live

(ex-ante) may bring more accurate results.

Our analyses also confirm that VCs could probably benefit from being able to

retrospectively analyze its deal flow to refine its investment criteria, as we have done, thereby

improving its effectiveness at selecting successful investments more systematically. However, a

cross-sectional study on many VCs would be necessary to get more information on the potential

added value of each element. Unfortunately, access to similar ex-ante cross-referenced data from

multiple VCs is difficult to obtain.

Indeed, it is logical that VCs update their criteria on the basis of the information they can

remember on deals at the time they learn about the outcome, typically 2 to 5 years after investing. If

this ex-post information differed from the ex-ante information used at the time of the investment

decision, VCs would not be able to improve their criteria. If we suppose that VCs can learn from

the outcomes of their decisions, we also expect that VCs increase their predictive power with

experience. Finally, if we suppose that VCs have strategic intelligence, they should adapt their

investment criteria to changing market conditions. However, until now, little research evidence

exists that demonstrates that specific deal selection skills impact performance.

Further research is also needed on VCs investment strategies with respect to the fact that a

fund has a defined number of years within its lifespan when it can make investments. The time

window of a funds investment opportunity should influence investment managers investment

appetite and investment strategy. Intuitively, one could expect that VCs invest in earlier stage

ventures at the beginning of a funds life and then gradually invest more in later stage transactions

(including follow-on investments for early deals) as the investment period ends. VCs would be

motivated to do this because during the second part of their funds life, the harvesting period, they

would want to exit all their investments. If they had invested at the end of their investment phase in

early stage deals, VCs would probably not be able to do follow-on investments with the same fund,

due to investment guideline restrictions. Unfortunately, the data set used for this study did not allow

for extensive or conclusive analysis of this phenomenon.

Unfortunately, this study does not show the interactions between selection criteria and

market conditions. Future research using a large enough sample size per period and covering a

whole investment cycle could be very revealing.

It is important to mention that while the actual criteria included the criteria that were

significant for the survival and/or the success of venture, the selection of the investment was only

30

part of the beginning of the value creation process aimed at generating outstanding returns for its

investors. This value creation, while crucial, falls outside the scope of this study, but could certainly

benefit from further in-depth research.

Although many questions still remain open for future study, researchers are making

progress towards unraveling the VC investment process and investment success/survival predictors.

These efforts should assist investors in avoiding poor investment decisions in the future; especially

in booming markets when irrational behavior may occurs (see Kaplan et al. 2005a).

Acknowledgements

No paper is solely the effort of its author. For their valuable inputs and support, I am also

grateful to all VCs who participated in the study and provided essential insights. Finally, I want to

thank Professor Didier Cossin, Professor Autio Erkko, Professor Benoit Leleux, Professor Per

Stromberg, and Professor Steve Kaplan for their academic support.

31

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