Nutopya White Paper Biomedical Investment Decision

Sabin Charles CARME
MBA November 2015
Warwick Business School - The University of Warwick
WHITE PAPER
“Venture capital in biomedical companies:
An evolving environment where
entrepreneurs need to adapt or die”
* The company case study has been removed for confidentiality reasons. We
apologize for the inconvenience and provide empty framewoks helping readings.

Nutopya White Paper – November 2015 - Sabin Carme – MBA - Warwick Business School p 2
Table of content
Chapter 1 – Project context............................................................................................... 4
Venture Capital uncertainty and rationality....................................................................... 4
1.1 Project background ............................................................................................................................5
1.2 Life Science Venture Capital Transformation...........................................................................7
1.3 Research question............................................................................................................................10
1.4 Research plan.....................................................................................................................................11
Chapter 2 – Literature review.......................................................................................... 12
Models and behaviours for investment decision ............................................................. 12
2.1 Rational and behavioural criteria for venture capital investment................................13
2.2 A decision making model for venture capital investments..............................................19
2.3 Overview of the modelling of investment decision.............................................................24
Chapter 3 – Research Methodology................................................................................. 25
Case study for modelling biomedical investment............................................................. 25
3.1 Case study research methodology...........................................................................................26
3.2 Investment decision models ........................................................................................................31
Chapter 4 – Case Study Application................................................................................. 37
Supervised analysis of biomedical investment................................................................. 37
4.1 Strategic advantage .........................................................................................................................38
Chapter 5 – Case Study Discussion .................................................................................. 41
5.1 Discussion on model based investment strategy.................................................................42
5.2 Best practices from the Venture Capital Community.........................................................44
Conclusion...................................................................................................................... 51
References...................................................................................................................... 53
Appendix........................................................................................................................ 65
A.1 Biomedical industry context........................................................................................................65
A.2 Life science start-up fundamentals...........................................................................................75
A.3 Venture Capital context.................................................................................................................81
A.4 Catalogue of Independent Life Science Venture Capital firms .......................................96

Table of Figure
Figure 1 – NVCA data on life science venture capital performance (Booth, 2015) .............................. 6
Figure 2 – Period 2012-2014 in Life Science (PWC, 2015a, page 3;5);..................................................... 6
Figure 3 – Old (a) and new (b) life science investor landscape, (Ford, 2014, page 16) ..................... 7
Figure 4 – (Hugett, 2013, p398) and (Bains, 2014, page 15)........................................................................ 8
Figure 5 – Life Science investment decision framework...............................................................................10
Figure 6 – Investment rational/behaviorial criteria (Waitman - AssentCompliance).......................13
Figure 7 – General criteria and information investors want to know (Redis, 2010)........................14
Figure 8 – Rational and emotional pathway to judgment (Mobus, 2010)...............................................16
Figure 9 – General criteria and information investors wants to know (Mann, 2015) .......................17
Figure 10 – Tag Cloud heuristics arrange words importance by size, font and colour ....................19
Figure 11 – Example of Decision tree in VC investment at NextView (Go, 2015)...............................21
Figure 12 – Example of System Dynamics in VC investment (Yepez, 2004b, p9)...............................22
Figure 13 – Overview of concepts for modelling in investment decision................................................24
Figure 14 – Chain and convergence of evidences in case study (Yin, 2014, page 100).....................27
Figure 16 – Short term value creation business model..................................................................................28
Figure 17 – Microenvironment criteria influencing investment performance.....................................31
Figure 18 – Macroenvironment criteria influencing investment performance....................................32
Figure 19 – Decision tree for Biomedical early-stage investment decision making...........................33
Figure 20 – Example of stock and flow graph derived from the model....................................................35
Figure 21 – System dynamics for Biomedical early-stage investment decision making...................35
Figure 22 – Porter 5 forces company analysis....................................................................................................38
Figure 23 – VRIO analysis for competitive advantage.....................................................................................40
Figure 24 – SWOT Analysis for strategic options ..............................................................................................40
Figure 27 – Venture capital blend maps 2015....................................................................................................44
Figure 28 – Crossover Investors (2013-2014) – SVB data (SVB 2015, page11) ..................................47
Figure 29 – IPO performance with crossover investors (2013-2014) (Booth, 2015c) .....................47
Figure 30 – Biotech stock market bubble burst October 2015 (Yahoo! finance).................................48
Figure 31 – 2014 Venture capital deals per sector - SVB proprietary data (SVB, 2015)..................49
Figure 32 – Roadshow material : time required and estimated cost (Ford, 2013)...........................50
Figure 33 – Life science venture capital companies (detailed in Appendix).........................................50
Figure 34 – Hype Cycle for Life Sciences - Gartner - 2014.............................................................................66
Figure 35 – Pharma value chain new business model (ATKearney, 2013)...........................................67
Figure 36 – Alps Bio Cluster (www.alpsbiocluster.eu)..................................................................................68
Figure 37 – Open innovation in Life science changing paradigm (Bock, 2015)...................................68
Figure 38 – Projects closed (left) with a focus on phase II efficacy criteria (right) (Cook, 2014)69
Figure 39 – Worldbank health expenditure (http://data.worldbank.org/)..........................................70
Figure 40 – Value chain of the biomedical industry (Volery 2007, page 7)............................................76
Figure 41 – Strategic advantage analysis and frameworks...........................................................................78
Figure 42 – Balanced Scorecard................................................................................................................................78
Figure 43 – Biomedical start-up advancement stage framework (Booth, 2011)................................79
Figure 44 – Capital Market Line theory and positioning of private equity and venture capital...81
Figure 45 – General partners and Limited partners involvement (EVCA, 2007)................................82
Figure 46 – VC performance and importance of CVCs (Lerner, 2013)....................................................83
Figure 47 – Top ten VC firms in 2012 (including four CVC) (Huggett, 2013)......................................84
Figure 48 – Different step in venture capital investment process (Clercq, 2006)..............................85
Figure 49 – Entrepreneurship ecosystem (Isenberg, 2015)........................................................................86
Figure 50 – Six months investment partnership process (Mann 2015) .................................................86
Figure 51 – VC evaluation provess (Osman, 2010)...........................................................................................87
Figure 52 –Investors exits through IPO or M&A in 2014 (CB Insights, 2015).......................................88
Figure 53 – GVC filling early stage funding gaps (NESTA, 2009, p26)......................................................93

Chapter 1 – Project context
Venture Capital uncertainty and rationality
Chapter 1 - Abstract
This chapter introduces the project background, the role of investors, and entrepreneurs
in innovative venture investment. The investment landscape has changed since the 2002
start-up bubble burst and the 2008 financial crisis. Those events have obliged non-
specialized investors to leave, whereas the remaining investors have operated carefully
and wisely. The few high quality projects, funded after 2008, have recently led to
successful exits.
The specialized life science Venture Capital investors (VCs) have been outperforming the
sector with amazing growth of 300% of biotechnology Nasdaq index over the last three
years. Those excellent results have convinced former investors to re-enter the market
place but they know that to perform, they will require strong technical expertise in the
field. We investigate how non-specialized VCs can find their niche along larger specialized
VCs and how solid heuristics linked to supervised decision-making can help them to
improve their performance.
The main topics highlighted in this chapter are:
- The performance of the biomedical sector for Venture capital investment
- The balance between investment in early stage and late stage venture
- The investment sector transformation, the new landscape of actors and roles
- The derisking strategy of investors
- The research question on model for systematic investment decision-making
- The importance of the environmental analysis to drive those models

1.1 Project background
The healthcare industry represents a significant part of GDP expenditure in most of
developed and emerging countries where the biomedical companies invent new
medicine, medical devices and services. In a context of economic crisis and looking for
GDP growth and employment, most of the government’s politics have focussed their
efforts on fostering the biomedical innovations and helping their access to the market.
The strategy focuses mainly on increasing corporate R&D incentives, academia
technology transfers to industry and facilitate new ventures creation.
Start-up funding stages. We are particularly interested in the strategies for survival of
biomedical start-ups studied by Tsai et al (2006): “Financing strategy is often the foremost
focus of the start-up biotech company. Funding and balance sheet liquidity, cash for
operations are its lifeblood… “. The “Valley of Death” funding issue is defined as the period
between seed funding and early stage capital investment where start-ups often failed to
bring in investors. At this seed/early stage, the investors are often syndicates of
specialized independent venture capital funds investing between €1-3 million. Later,
having the biomedical company more structured and running allows to target €10-15
million fundraising campaign with more experienced investors such as global
independent venture capital funds or/and large corporate venture capital funds.
Investors performance. According to the PWC survey (2015a), the investment in life
science has been very active with 30% increase in the USA in 2014 for a total amount of
$6 billion going into more than 400 deals and representing almost one fifth of total
venture capital investments. The MoneyTree™ Report1 edited by PWC and the National
Venture Capital Association is enthusiastic for the investor industry (PWC, 2015b). The
year 2015 confirms the trend of transformations that led to successful early investment
strategies in life science. Bruce Booth, venture capital partner at Atlas Venture comments
the biopharma sector outperformance2 with three reasons:
 Scientific advances. The improvement of bioassays, quality of subcontractors as well
as technology sourcing from academia, business development units in the large
corporations and structuration of biotechnology ecosystem in clusters has led to
technological breakthroughs.
 Robust capital markets actors. The number of general investors has shrunk
dramatically, in particular at the early stage, while specialized investors have
increased their commitment, dragging with them other type of investors such as
family offices or corporate venture capital firms. As an example, Janis Naeve from
Amgen Ventures has invested $1.5 million in MiRagen (Flinn, 2011).
 Successful exits. The recent article in the Boston Globe (Weisman, 2015) highlights
the strong performance of the sector: “A record 71 biotech companies went public in
2014, raising more than $5.2 Billion”. The recent number of IPOs and M&A successes
has led to the creation of new funds with strong investment support from limited
partners, as highlighted by Clarus Ventures who announced in June 2015 they closed
$500 million fund.
1 http://www.pwc.com/us/en/technology/moneytree.jhtml
2 http://lifescivc.com/

We are considering internal rate of return (IRR) and number of investments for the period
2011-2015 available from the National Venture Capital Association (Figure 1a-b). On his
blog, Bruce Booth (2011) reports an IRR of 69.9% for the Biopharma sector which as
compared to average 10% in the period 2001-2010.
a- IRR b- number of investments
Figure 1 – NVCA data on life science venture capital performance (Booth, 2015)
Early and late stage investment equilibrium. There is less quantity and more quality
execution of investments with an average of 130 deals per year for the 2011-2015 period
as compared to 235 deals per year for the 2001-2010 period. For Ford et al (2014), the
venture capital landscape has changed considerably with the majority of general funds
moving to late stage biotech investment, while a minority of specialized life science funds
are capable to identify high potential early and mid stage start-ups. This explains the less
deals and higher amounts per deal rising by 60% from €8 million to €13 million average
for the period 2012-2014 (Figure 2a). Then we observe that less risky late stage
investments represent two third of all investments (Figure 2b). Those late stage
investments have also contributed to high return for private equity firms as shown by the
300% return of the AXA Framlington Biotech fund (Caldwell, 2015).
a-Average deal size b-Total fundings per stages
Figure 2 – Period 2012-2014 in Life Science (PWC, 2015a, page 3;5);
In conclusion, in this performance context, the specialized life science venture capital
firms will easily attract more funding from limited partners to launch new funds. We also
expect to see the emergence of expert small-focused funds (independent or corporation)
willing to move in the empty area of risky very early stage investment. Their expertise
and tangible decision-making will serve as a lever to re-engage some general large
venture capital firms in syndication, attracted by the high IRR potentials.

1.2 Life Science Venture Capital Transformation
The venture capital industry is at a high level of uncertainty, when taking into account
different technology sectors, geographic zones, early and later stage investment. In a
recent review paper, Ledenyov et (2013) looked at the optimal portfolio strategy of
Venture Capital Firm to compensate for highly volatile capital markets and explained why
limited partners have shifted their investment into specialized funds. In the particular
field of Life Science, Ford et al (Ford, 2014) have studied the venue of new actors and the
investment practice changes as shown on Figure 3.
Figure 3 – Old (a) and new (b) life science investor landscape, (Ford, 2014, page 16)
Importance to nurture innovation at the early stage. The recent article from
Balakrishnan (2015) alerts on the innovation rupture caused by the VCs. Indeed, by
decreasing their risk profile moving investment to later stage companies, the VCs do not
encourage new pool of high potential start-ups. The role of expert niche VCs, Business
Angels, Crowdfunders is thus crucial to detect and fund very early talents but will only
succeed if VCs do not dilute them when coming at a later stage. Therefore alliance on early
stage funding and agreement of continuity in syndication steps contributes to sustain the
VCs future profitability model and high IRR projections.

Investors new landscape. Limited partners diversify their asset investing in venture
capital funds to maximize returns. According to Ford (Ford, 2014), the general venture
capital funds have failed since 2000 to identify potential winners. This is confirmed by
Hugett et al (Hugett, 2013) who observe between 2008 and 2011 a reduction by 50% of
the investment of venture capital funds engaged at high-risk stage of start-ups (Figure
4a). Early stage companies represent only 10% of the investments while less risky
mid/late stage companies concentrate 90% of total investments (Figure 4b). This also has
led to the emergence of Biopharma specialized funds and recent multiplication of
corporate venture capital firms (Krogh, 2012).
a- Amount invested b- repartition per stage of investment
Figure 4 –(Hugett, 2013, p398) and (Bains, 2014, page 15)
In an newspaper recent article, Gupta et al (2014) identify that few VCs leading firms
attract most of fund investors. In parallel there is a growing community of niche VCs with
focused strategy and marketing to offer limited partners investors to diversify their
portfolio and risks.
The “Forms of Capital” theory from Bourdieu (1986) emphasizes the importance of
cultural, social and economical capital. Specialized VCs maximize their qualification and
skills contributing to a cultural capital. The network of institutions, entrepreneurs and
potential syndication partners contribute to the social capital. Seizing opportunities and
maximizing return attract new fund and sustain economical capital.
Specialized venture capital. The specialized funds that have a focus in life science often
don’t need to syndicate with a general fund to conduct an early stage deal. They are
usually well structured venture capital funds having large amount of money under
management (up to €500 million) who have significant managing fees around €5 million
to hire general partners with strong biotechnology acumen and can manage the whole
due-diligence solely without syndication.
In a recent paper, Patzelt et al (2009) have looked at the quality of VCs portfolio in relation
to the general partner background in term of education and experience. The main findings
are that firms with partner having high technical and industry background are keen to
conduct investment in early stage companies and across the industry. There are around
hundred of those mature venture capital companies operating worldwide; a list is
provided in annexe (page 96). Those funds compete against each others for early stage
investment, in particular for breakthrough start-ups with high return potentials.

Expert small-focused funds. Looking at early stage investment, we observe that expert
small-focused venture capital funds are usually mixing geographical and technical
expertise and therefore have a great understanding of the microenvironment and
ecosystems.
Patzelt et al (2006) insist on three factors for risk diversification:
1. Geographic and start-up advancement stage (early, mid, …)
2. Life science specialty such as immunotherapy, nanomedicine, diagnostic, device
3. Platform technology and development pipeline with candidates and indications
The expert small-focused venture capital firms, including corporate venture capital,
usually manage funds around $50 million. In order to increase the performance success,
the expert small-focused venture capital firms need to have a critical size and organisation
to build a risk equilibrated portfolio. The managing fees barely cover the fixed costs. An
amount of €0.5 million on a five years period, can not cover all costs for business analysis,
due-diligences and board sitting. This obliges those funds to syndicate usually with at
least two small funds and a third, generalist one. The syndicate therefore shares the costs
of high quality due-diligence, involves fields experts as well as accounting and legal
advisors. This model is not perfect, it requires good partnerships between investors
having sometime divergent goals. It also obliges investors to negotiate together their
share of stake and can be disequilibrated with biggest fund having more weight in
negotiation.
Early vs. late stage investment risk. Fleming (2015) warns on the challenge to sustain
innovation when decreasing the amount of early-stage investment. For late stage
investment, start-up companies are more likely to succeed in M&A or IPO when having
achieved their minimum viable product proof of concept and engaged on partnerships
with key customers. The specialized VCs will compete against each others for those easily
identifiable valuable deals. With this strategy, the late stage investors create dilution of
the shares and disequilibrate the risk/reward ratio taken by the early investors. Werth
(2014) highlight the importance of syndication between experts small VCs and
specialized VCs to ensure continuity in early stage investment at lower risk but this
requires a set of rules for anti-dilution and priority for reinvestment.
Derisking the venture capital industry. We aim to reduce the uncertainty at the early
stage investment by improving the decision-making. This would give the opportunity for
small funds to select better deals and get higher return on investment. Better ROI will help
attracting more funding from limited partners. This is a virtuous cycle where better
performing venture capital firms making rational investment will contribute to increase
the total investments and therefore nurture the innovation in the biomedical sector at the
earliest stages.
In addition, a better share of reward between general and experts venture capital firms
during syndication will create strong incentive for securing capital for early stage
biomedical start-up. By having a better equilibrium in the relationship, the uncertainty of
the whole sector will decrease contributing to better return for limited partners moving
back in the field. The expert small funds will therefore be rewarded in scouting the best
opportunities in their niche and keeping substantial stake along the syndication.

1.3 Research question
Question. The research question we would like to address is: “How supervised decision
making methods could help to reduce uncertainty in life science venture capital and increase
their return on investment by shifting funding to an earlier stage characterized by higher
risk/reward ?”
Objective. We have introduced the general uncertainty in investments that has convinced
limited partners investors to shift their funding to specialized venture capital firms. Our
objective is to suggest some comprehensive models linking entrepreneur and investors
for life science investment decision-making in new venture. Those decision-making
models are supported by rational and psychological criteria that influences judgment of
several actors involved.
Investment triple-actors system. Our analysis focuses on three actors for investment
decision-making that are the start-up venture, the investor and the entrepreneur. As
shown on Figure 5, for each of these actors we consider the influence of micro and macro
environments on criteria that guide the decision and reduce judgment biases.
Figure 5 – Life Science investment decision framework
Industry environment. In the case of investment in niche life science domains, because
they evolve in a common ecosystem, we will have a common microenvironment for the
start-up, the investor and the entrepreneur. This means that a strong understanding of
the microenvironment by all actors should contribute to better alignment of goals and
then successful investment and return. The macroenvironment will be different for the
three actors, however, understanding eachothers threats and convert it into
opportunities will make the difference for strong cooperation and long term return. This
is highlighted by the VCs general partners being ex biotech executives and by the CXO
roles in new ventures fulfilled by former consulting firm experts.
Start-up
company
Investor
Entrepreneur
MICRO
Environnement
MACRO
Environnment

1.4 Research plan
The following dissertation addresses the question of decision-making process of capital
investors in funding biomedical innovative companies. Our objective is to provide a
tangible heuristic that will reinforce investors and entrepreneurs understanding of
investment decision workflow and reduce judgment biases. The heuristic relies on three
pillars that are the firm, the entrepreneur and the investor. For each of those pillars we
use decision criteria related to the micro and macro environment of the biomedical
industry ecosystem.
We are applying this investment decision workflow to generic new biomedical venture
using the case study framework methodology. The case study offers the possibility to
reflect on any company fundraising campaign as an ethnographic approach. We have
selected an investment decision-making scenario that works well with these generic
companies business model and that we compare to other biotechnology companies.
Therefore, the case study relies on qualitative data obtained from statements of two
hundreds of professionals from the Life science Venture Capital sector that we have
connected with a dedicated Linked-in profile.
This first chapter presents the research question on reducing uncertainty of venture
capital investment, in the field of biomedical industry and the need to improve financial
return. Opportunities and threats are considered in the strengthening of the investment
decision-making process with insight from the micro and macro environment.
In the second chapter of the dissertation we introduce a literature review on the
investment decision-making heuristics. We present first the psychological criteria and
biases that entrepreneurs and investors confront. Then we describe the operational
research theory with a preliminary focus on interpretive modelling methods.
The third chapter of the dissertation presents the research methods, introducing new
ventures generic business model using a case study framework. We present two models
for biomedical new venture investment decisions, one using the decision tree analysis and
the second using the system dynamics. We finally introduce our methodology for
collecting qualitative data from a dedicated linked-in profile.
The fourth chapter focuses on the case study application. Looking at the micro and macro
environment, we use tangible criteria to demonstrate how companies business model
should align well with the biomedical industry recent transformation. We also consider
investors and entrepreneur criteria in the previously described models to build
investment decision scenarios. We then suggest a realistic and comprehensive
implementation strategy for the capital investment campaign of new biotech companies.
In the fifth chapter we analyse investors feedback and additional qualitative information
from biomedical companies fundraising. A reflective analysis on the decision making
process suggest on improvement, bias and limits and beneficial learning for the
entrepreneurs and investors. We then conclude on what are the best strategies for
biomedical innovative companies to rise funding from venture capital investors.

Chapter 2 – Literature review
Models and behaviours for investment decision
Chapter 2 – Abstract
The risk and returns are at the core of any Venture Capitalist (VC) investment decision.
Shepherd et al. (2003) insist on a good balance of information in the decision-making
process. Indeed, venture capital investment decisions take place in an evolving highly
complex and competitive environment, depending on regulatory changes both in the
financial and technology fields (Rafferty, 2007). Therefore understanding the decision-
making in VC investment has been subject to several academic researches (Tyebjee,
1994), (Fried, 1994), (Muzyka, 1996), (Zopounidis, 1994), (Shepherd, 1999), (Zutshi,
1999), (Mainprize, 2002).
Most of the VCs investments rely on multiple criteria, subject to judgment biases (Monika,
2015). Zacharakis, Meyer and Shepherd studied the robustness of decision-making
(Zacharakis, 1995), (Zacharakis, 1998), (Zacharakis, 2001). They found that at the time a
new venture enters the VC investment assessment process and due-diligence stage, the
evaluation is guided by
 New venture performance potential
 Investors judgment intuition in the context of high return for their partners
 Entrepreneur personality in the context of fundraising strategy
In this chapter, we describe the capital investment decision-making process and in
particular the criteria that influence a deal at the venture level, the investor level and the
entrepreneur level. After introducing these criteria we consider two methods from the
operational research theory that could help to model the decision making process and
therefore reduce judgment biases therein.
- Criteria to be used in models from venture, investor and entrepreneur
- The main judgment bias risks observed in investors and entrepreneurs
- Rational and behavioural criteria that impact the potential return on investment
- Definition and theory on heuristics and operational research
- Theory on decision tree and system dynamics modelling

2.1 Rational and behavioural criteria for venture capital investment
New venture performance criteria in investment decision
Investors have rational and psychological criteria that influence the decision of whether
to invest.. Some rational criteria can be linked to regulation or rules established with the
limited partners at the creation of the fund. In France, for instance, regulation and tax
incentives have impacted on two types of funds: FCPI and FNA (Bpifrance, 2015). Some
existing funds have a very specific technology focus like Bioseed from Auriga which will
only invest in Microbiology (Bpifrance, 2011), irrespective of a venture’s potential outside
of this field.
Fundamentals for investing in start-up firms. The new venture performance relies on
several criteria described by the VCs such as the quality of the team, the market potential
and the uniqueness of the solution offered and the measurable competitive advantage on
the market place that can be assessed during the due diligence process preceding the VC
investment in a new venture. Khanin et al (2008) studied 40 years of VC criteria. Those
criteria are always balanced by the analysis of the risk and reward potential and the
intrinsic judgment experience of the investor (Figure 6).
Figure 6 – Investment rational/behaviorial criteria (Waitman - AssentCompliance)
Fried et al (1994) emphasize four main topics of interest; the technology concept, the new
venture people/management, the competitive environment of the market and the
business model maximizing financial return potential. In terms of the technology concept,
the most important criteria are the potential earning growth, the short time to market and
the low and controlled cash burn before the point of break-even. In terms of management,
the first criteria are integrity, leadership, pragmatism and flexibility. The management
experience is indispensable, both in understanding the business and in identifying risks.
The market topic emphasizes the importance of profitability and competitive advantage.
The last key topic is the return on investment with the availability of rewarding exits.
Criteria comparison. Another survey from Bachner et al (1996) highlighted decision-
marking criteria by degree of importance. For Bachner, the most important criteria is the

management team’s commitment to performance, this is strongly linked to a sense of
urgency and the capacity to define priorities. The second key criterion is the need to
design the offering directly from the market demand and customer needs. The offer
should have several superior features as compared to competition. A third criterion is to
create value for shareholders and follow a strategy, in particular for taking a leading
position on the market. Last is the importance to recruit the right people and to have the
relevant leadership experience to drive their performance. Some of the criteria are
summarized in the analysis conducted by Mascré et al (Mascré, 2005) and listed in Figure
7.
Figure 7 – General criteria and information investors want to know (Redis, 2010)
Rational criteria have been adopted by the VC communities to strengthen their decision
making (MacMillan, 1985), (MacMillan, 1987), (Hall, 1993). This compensates for the
reality of high potential new ventures that provide incomplete and unreliable
information. Inexperienced teams may conduct a venture following an untested business
model. This is especially important when considering healthcare market dynamics, which
evolve quickly compared to the slow time to market of life science ventures. New venture
technology can’t be the only criteria to support an investment decision. There is a risk in
following an innovation trend that can quickly become obsolete due to fast turnaround of
technology and expertise.

Investors judgment criteria in investment decision
The Venture Capital industry only makes a few investment decisions a year and that
investment takes several years to confirm its return. VCs have therefore little room to
learn from their investment process. This leaves some room for intuition and personal
judgment at the time of committing to an investment.
Minimize judgment biases. Using decision models for the selection process helps to
minimize cognitive errors (Oguz, 2014). The improvement of decision models takes into
account judgment biases such as patterns anchoring, availability of information,
groupthink, reinvestment due to loss aversion and overconfidence. Intuition in decision-
making can be swayed by a VCs experience with similar deals, industry trends, stock
market attention for IPOs. Investor psychological influence is also linked to their risk and
return criteria. In case of competition between VCs bidding for a same deal, a future
negotiation will impact severely on its potential return., in particular leading to higher
valuation and reducing options for “Liquidation Preferences and Participation”.
Looking at rational investment processes, Tyebjee et al (1984) highlighted that Venture
Capital investor’s decision making follows a rational path to source potential deals;
evaluating those deals during due diligence and conducting negotiations at the time of
their investment. According to Dawson et al, (2006), (2011), despite a strict frame for
rational analysis, investors have been sensitive to biases in their decision making criteria.
Those biases relate to some emotional heuristics (Finucane, 2000), (Slovic, 2007).
Pragmatism to counter psychology influence. Behavioural finance has proposed a
theoretical framework based on psychology to explain irrational investment decision-
making (Yazdipour, 2010). The theory assumes that the investment environment
influences investors decision and therefore disturbs the whole industry. Byrne (2008)
highlights reasons why decision-makers deviate from rational attitudes. The aversion to
loss is a major criteria, (Benartzi, 1995). Other bias such as familiarity with easy available
information could also mislead an investor’s intuition. Entrepreneurs that have successful
communication, press releases, pitch qualities will perform better (Romans, 2013). The
representativeness is also source of misjudgement where investors use stereotypes
(Bordalo, 2014), comparison with similar business they invested in, small sample trends;
it gives them an illusion of validity. Some bias’ are well know;, the confirmation bias for
instance where VCs having too much interest in a start-up misinterpret all new
information. They should instead play a devil’s advocate role or involve independent
reviewer at the time of decision. Another bias, known as anchoring, is the lack of revision
of the accumulated experience in the context of industry evolution. The difficulty is for
VCs to find contradictory facts to challenge their decision making process.
Tversky et al (1981) have a deep understanding of the importance of an investors own
heuristics and psychology related decision-making in investment. The investor sentiment
has been described by Barberis et al (1998).

Balancing emotional and rational decision-making. According to Sinyard (2013),
investors have learned from past mistakes and they know better the risk of deviation from
rationality. Wheale et al (2003) highlighted previous heuristic patterns where returns
were correlated to a few basic measures of market performance. It appears that investors
were over-optimistic due to the great prospects of a new technology sector (see Gartner
analysis in Appendix p66). Investors have been more and more careful in recent years as
unfamiliar sectors with unknown success criteria created artificial self-reinforcing
investment loops that lead in turn to boom and bust bubbles. Mobus et al (2010)
described the heuristic developed by investors. Those heuristics mix tangible analysis,
past experience learnings and intuition (Figure 8).
Figure 8 – Rational and emotional pathway to judgment (Mobus, 2010)
Investors have learned to master their emotions and refine their initial judgments with
rationality in a context of a complex decision-making environment. Kahneman et al
(2011) studied how to minimize the impact of cognitive biases in strategic investment
decision-making. In particular, incorporating psychology into the decision-making
process has shown positive preliminary outcomes with a 5% increase in the return on
investment (Lovallo, 2010). With the growing psychology criteria taken into account,
Thaler et al (1999) believes that the investment strategies of private equity firms will
improve. This has to be taken into account by entrepreneurs who build on the emotional
relationships with investors and adjust their fundraising strategies accordingly (Wiliams,
2015).

Entrepreneur personality criteria in investment decision
The entrepreneur’s role is to get the best deal possible, raise enough money to achieve
their short-term objectives of value creation and ensure attractiveness for future
investment rounds. The reference book from Vance et al (2005) introduces best practice
in raising capital. Entrepreneurs need to understand the investor’s mindsets to establish
a good partnership. Entrepreneurs will be successful in establishing a competition
between VCs to get the best deal. This requires entrepreneurs to understand the VCs
needs and the established rules of their industry. This strategy is usually complex for
technology minded entrepreneurs that have huge sunk costs constraints,
misunderstanding of financial practices and over-confidence about their business.
Biases in assessing company development stages. In a whitepaper from the University
of Berkeley, Mann et al (2015) differentiated the seed stage venture from more mature
first round start up (Figure 9). Most of the time Entrepreneurs misevaluate the
advancement stage of their company and therefore don’t adopt the relevant financing
instruments. Despite a good advancement of their technology, start-ups often lack solid
management team and processes to achieve their objectives (Bonnstetter, 2013).
Figure 9 – General criteria and information investors wants to know (Mann, 2015)
From the company point of view, entrepreneur focuses generally on their product and
final large market rather than the value creation on the short run. Because they are smart
and knowledgeable, they assume they can get through the investment process easily. This
is a common mistake, Cable et al (1997) explain that entrepreneur generally miss the
essential input investors expect from them.
Biases in evaluating an opportunity. Burmeister et al (2007) studied entrepreneur
biases. Entrepreneurs have to face lots of different situations and rely on heuristics in

their decision making process. The entrepreneur judgment error is well explained in
Minniti’s Book on entrepreneurial dynamic (Minniti, 2013). The logical path for an
entrepreneur would be to collect all information necessary to make a decision or at least
to challenge their position and review their decision heuristic. The time of a window of
opportunity is often short, that is why there is a need to find the right balance between
the information gathering and the use of heuristics.
Busenitz et al (1997) investigated the bias associated with entrepreneur heuristics and in
particular the overconfidence and representativeness. In their overconfidence,
entrepreneurs tend to believe their judgment is the only truth. This is linked to
representativeness, referring to limited past experience and shortcuts in observations.
Dushnitsky et al (2010) gives an example of overconfidence and biased heuristics in the
way entrepreneurs overprice their companies without a realistic valuation. In addition,
Malmendier et al (2005) studied how CEOs overestimate the future return of their
companies. This overconfidence is an asset for companies who seek for strong leadership,
commitment and experience. However this also comes with hubris and blindness;
decision-making becomes weak and confidence is kept mainly coming from self-
attribution bias. The risk is to have a negative impact on shareholder value, in particular
when looking for investors.
Behavioural skills that influence decisions. Wickham et al (2003) studied
representativeness bias in an entrepreneurs decision-making process. This bias usually
influences their judgment with an overestimation of probability of low likelihood events.
This bias relies on weak heuristics that lead to poor quality managerial decision, in
particular financial decisions in the new venture creation. Carr et al (2009) and Sanchez
et al (2011) studied how entrepreneurs were influenced by cognitive bias and how this
impacted their thinking. According to Corbett et al (2007) and Mitchell et al (2007), the
entrepreneurial mindset comes during risk analysis at the time of starting new ventures.
Skills of successful entrepreneurship include an understanding of inner bias regarding
their decision making process and related heuristics.
A recent article from Elsworth (2015) emphasizes the importance of agility as a principal
skill. Indeed, it is crucial for entrepreneurs to demonstrate adaptability, autonomy and
revise their judgment and maintain their independence. A second major skill is the
problem-solving pragmatism that includes objectivity with numbers, facts, being realistic
and practical. It is key for an entrepreneur to have knowledge of financial numbers in
particular cash flow and return on investment. Objectivity includes for entrepreneurs to
base their decisions on data and externally processed information in order to balance
their intuition.

2.2 A decision making model for venture capital investments
We believe that operational research can help in mapping the complex multicriteria
investment decision-making process. Operational research has been used in several
industry fields for formalizing heuristics and for weighing different scenarios before the
final decision. We trust the judgment quality of VC investors but we believe that providing
such decision-making tools will help in establishing a better relationship with the
entrepreneurs. Sharing a common vision and understanding the respective objectives will
impact positively the new venture performance.
Nasica et al (2009) have shown that VCs adopt a “portfolio failure avoidance” strategy that
requires rational behaviour and decision-making process. VCs have therefore precise
rules and hypotheses to invest in an environment characterized by a strong uncertainty
and asymmetric information. According to Nasica, this strategy leads VCs to compromise
their portfolio between early and later-stage investments. The risk of loss decreases as
the venture develops, confirming the relative safety of later stage investment. Macro-
environment uncertainty remains the same in early and late stage investments, however,
whereas earlier stage investment returns higher rates. Specialized investors therefore
embrace modelling for decision-making under uncertainty.
Heuristic and operational research in decision making
The use of heuristic and cognitive maps has shown a successful impact in both
entrepreneur and venture capital investor decision-making (Vestraete, 1998). Heuristics
can be described as a set of rules and links between those rules that people will refer in
order to improve their judgment at the time of making decisions in a complex
environment. Heuristics replace information with an efficient mix of conscious and
unconscious cognitive processes (Gigerenzer, 2011). We use the example of tag cloud as
a heuristic example to highlight the impact of visual representation (Figure 10).
Figure 10 – Tag Cloud heuristics arrange words importance by size, font and colour
Concept of heuristics. The heuristics consist in cognitive shortcuts that reduce
complexity of a problem when defining a hierarchical importance of the problem
parameters. Heuristic usually simplify the logic, rationality and probability involved in a
choice. This is compensated by cognitive shortcuts including intuition and experience that
are subject to bias. In certain cases heuristics can be governed by automatic intuitive
judgment which compensates for the limited information available.

The role of intuition in reducing complexity. Cognitive scientists such as Simon (1991)
and Khaneman (2003) have developed a theory of bounded rationality. The concept is
well described by Jones et al (1999) as the limitation of complex problem solving and
rational decision making due to the incapacity of human minds to process information in
terms of time available and cognitive limitation. Therefore, modelling of decision-making
using rational and behavioural criteria is a way to take into account the psychological and
cognitive limitation of decision makers (Yazdipour, 2010). Simon et al (1955) and later
Tversky (1974) and Kahneman (1979) introduce the concept of heuristics to explain the
decision making process under intuitive judgments: "Judgment Under Uncertainty:
Heuristics and Biases”.
Even heuristics lead to errors, when they rely on strong rational information. But they
have shown to be good as a starting point when access to information is limited. In
addition, understanding these simplifications has helped people to better frame their
information and to positively influence the heuristic based decision. In this way,
transparency, integrity and trust between the entrepreneur and investor are the highest
criteria to make an investment decision. In a specific industry sector, a proper decision
making tool could help to balance wisely between rational processed information and
complexity shortcuts. Several research works have suggested improvements in heuristics
and bias reduction. A recent survey from Courtney (2003) insists on the importance of
having different scenarios to identify plausible outcomes of a decision and the realistic
probability of their eventuality. Mixing relevant (unbiased) information with data will
help to adjust decision patterns and avoid the anchoring of risk. This approach
contributes to reduce systematic and predicable errors associated with heuristics.
Operational research methods for investment supervision. A recent book from
Baddeley (2013) on behavioural finance underlines that the coming challenge is to
develop a robust framework of interconnected simplified heuristics. The strength of those
heuristics is the underlying theory and the supporting environment. Venture capital
investment is one of the most complex fields of the private equity industry. Therefore,
most of the general partners have clear understanding of their investment behaviours and
heuristics in decision-making. In the past decade, analysis of investment failure and
improvement has led to better decision-making process in the VC industry. Shepherd and
Zacharakis (2002) have conducted several researches on the bias analysis and the
decisions aid both from rational and psychological finance decision-making.
The field of operational research focuses on applying mathematical modelling to improve
decision-making (Kunc, 2011). Those methods help to deal with complex problems and
provide near-optimal solutions. Operational research emphasizes the interaction of
rational information (normative) and psychological intuition (descriptive) in the decision
making process (Larichev, 1999), (Bertrand, 2002). Techniques usually consist in
building model and scenario to maximize or minimize a key indicator such as risk or
performance of a business. The core of operational research relies on the use of
computation of algorithms to explain complex organizational systems.

Decision tree
Among the operational research decision support tools, the decision tree uses a graphical
algorithm with options and probabilities and consecutive outcomes (Magee, 1964). Those
decision trees are helpful in identifying a strategy with a clear goal to achieve. Decision
tree works on sequential decisions made under uncertainty linking outcomes and their
probability to happen. The design of decision tree obliges to define carefully the nodes
interactions and their outcomes. Recent literature (Frini, 2012), (Wong, 2013), confirms
that the decision tree provides a simple visualization of options for decision-making and
the outcome resulting from user choices. Calculations are usually simple combination of
probabilities. A sensitivity analysis can easily be conducted varying several parameters in
a range of values to spot several risk scenarios linked to choices values and weights.
According to a recent survey from McKinsey (McKinsey, 2009), the scenarios analysis
would help in reframing a problem and discuss potential options that usual heuristics
would not have spotted.
Benchmark. A recent paper from Go (2015), Venture Capital investor, proposes a
simplified model decision tree for Venture Capital investment (Figure 11).
Figure 11 – Example of Decision tree in VC investment at NextView (Go, 2015)
Bueschen (2015) insists in a recent article in Tech Crunch on the bias in investments. The
article refers to the work of Clint Korver who uses a specific decision analysis to reduce
cognitive biases in his venture capital investment decisions. Clint Korver use decision tree
heuristics (Korver, 2015) to reduce influence that can be linked to wrong early stage
investment. The decision analysis is only a tool that helps to confront evidences and offers
logical structure of judgment and intuition to test hypothesis.

Literature cases review. In 1965 consulting companies such as McKinsey have used
decision trees in investment decision (Hespos, 1965). Haaster et al (2002) have described
investment scenarios in the field of agriculture flower production looking at alternatives
between production and marketing activities. The model helped showing the impact of a
decision on the business profitability. Decision tree methods have also been used for
residential property investment strategy. Manganelli et al (2014) have used the decision
tree method to estimate the risk of investment relying on knowledge and probability of
events to happen impacting property value. Owusu et al (2008) have studied the
importance of financial decision in the oil and gas industry. The use of decision tree helped
to optimize investment in a portfolio by looking at consequences of varying probabilities
of hypotheses.
System dynamics
Another central decision tool in operational research is the use of system dynamics
(Morecroft, 1999). The model describes complex systems and processes that happen in
parallel or that have mutual interaction. The method is well explained in a review paper
from Sterman (2001). The system dynamics uses cognitive mapping of the complex
system and its sub-units. Variation (over time) is described using stock and flows and
feedback casual loops between the system subunits. The use of heuristic helps in a
representation of knowledge blocks and their links within an organization. The
methodology is well described by Radzicki in the book from Meyers et al (2010) on
complex systems in finance.
Benchmark. In a recent paper Yepez (2004a) have applied system dynamics in the
complex relationship of venture capital investment (Figure 12). They have first built the
organization sub-units and blocks as variables and casual loops and feedback relationship
to structure all blocks and sub-units together. They have defined standard rules and
causal model in investment process as well as model of investment dynamics. Yepez
(2004b) have used the model to simulate several investment scenario to explore the VC
investment performance.
Figure 12 – Example of System Dynamics in VC investment (Yepez, 2004b, p9)

Literature cases review. System dynamics have been described in the literature for
decision-making in several domains. In the field of biotechnology, Morecroft et al (2006)
used system dynamics modelling to capture knowledge from the biotechnology company,
create debate and help to reflect on the growth strategy. Kunc et al (2013) studied the
dynamics of a drug on a market. This simulation helped in identifying the impact of timing
access to the market on the drug price and reimbursement. In terms of resource
management, Kunc and Morecroft (2009) developed a strategic optimisation of resource
usage, linking resource based view mapping and system dynamics. The model helps to
interpret managerial decision-making process for strategic decision-making. Hart et al
(1987) studied the dynamics of innovative venture creation. The modelling has been
useful in defining criteria for successful venture in the context of the ecosystem and
helping in the optimization of environmental resources. Reiner et al (2009) looked at
system dynamics to analyse Venture Capital investment processes, in particular in term
of deal analysis flow. This has contributed to the definition of indicators for performance
and better allocation of resources. In a recent book, Grossman et al (2012) used system
dynamics to model new biotechnology entrepreneurial processes. The model helps to see
the weigh of decision in the global performance of the start-up.
Limits of operational research in decision making
In a recent survey McKinsey (2014) addresses the limits of decision-making models.
Despite the usefulness of reducing biases and undermining leaders judgment, decision-
making models can be wrongly implemented or used, leading to mistakes, in particular
when leader disengage from their role to take the final decision.
Decision tree limits. Sherman et al (2013) have studied the limits of decision tree models
in the context of cargo screening process. One of the limits of the decision tree is the
linkage between independent variables where a small change can have a strong global
influence. Decision tree also needs to have a hierarchy of criteria on their level of influence
on the decision; this is subject to implementation bias from the users.
System dynamics limits. The use of system dynamics in heuristics has been studied to
explore the potential improvement of the decision-making process in the context of
bounded rationality. Morecroft et al (2007) studied flow and processing features for
complex problem solving simplification. They first divided an organization into sub-units
linked together and having their individual blocks with their own goals and constraints.
Those goals and constraints are known from a limited and uncertain sources of
information that defines casual relations and feedback loops between sub-units blocks.
The decision making usually follows some operating procedures and standard rules that
combine casual relations and whose complexity overpasses human cognitive capacities.
Forrester et al (1987) further developed the rational framework on feedback-structured
sub-units that they called system dynamics. Sterman et al (2002) studied the complexity
of casual and feedback loops and interdependency between blocks. They insist that
missing linkage between decision and their environment were sources of failure. Thus
complex decision making is not only limited by bounded rationality but also by weak
heuristics that underestimate feedbacks coming from substantial part of on the
organization structure.

2.3 Overview of the modelling of investment decision
The concept of bounded rationality explains that an individual will base decisions on their
intuition because they cannot gather and analyse complex information. When conducting
the analysis for investment in a new venture, there are several rational criteria derived
from the venture characteristics. However, the intuition of both the entrepreneur and the
investor will interfere at the time of the investment decision. Those intuitions are
associated with psychological biases such as representativeness, anchoring, and
overconfidence, mainly influenced by the micro and macro environments.
The investment decision in early stage ventures offers the highest returns but requires
minimizing risks. We define rational and psychological criteria and rules between them
relying on theory. Those constitute the decision-making models (Figure 13).
Figure 13 – Overview of concepts for modelling in investment decision
Decision-making models such as Decision Tree or System Dynamics limit the judgment
decision risks based on a unique criterion. Following such rules reduces the fixation on a
specific criterion’s importance at the time of the decision. This leads to a more precise,
data-driven rather than gut-driven decision and also helps in highlighting blind spots. In
other situations, decisions can be postponed awaiting the improvement of the parameters
associated to the criterion.
Criteria + Heuris cs rules + Opera onal research
=
Decision Making Models
Independent Criteria
= Decision Tree
Combined Criteria
= System dynamics
Venture criteria
Investor criteria
Entrepreneur criteria
Emo onal and
Ra onal biases
Reduce judgment biases from the
ecosystem for investment decision
Macro & Micro
Environment

Chapter 3 – Research Methodology
Case study for modelling biomedical investment
In this chapter we introduce our research methods and in particular using the case study
methodology we conduct an ethnographic analysis of new biotech companies. The
business model context highlights the main characteristic of new ventures and insists on
fundamental parameters that will be taken into account in the investment decision. We
complete this generic company context with data from the ecosystem and explain our
strategy for gathering information, in particular qualitative data extracted from a social
network sourcing method. We then introduce a list of 24 criteria that we will use to drive
our investment decision models. We have built two models one with decision tree and
another one with system dynamics. Both model’s structures and functions are detailed in
this chapter.
- The method we use on case study research
- The biotech company business model
- The qualitative data collection on LinkedIn
- The Micro and macro environment criteria that influence our decision models
- The decision tree model
- The system dynamics model

3.1 Case study research methodology
Methodology and framework of the case study
In a literature review, Baxter et al (2008) define the case study research being “an
approach to research that facilitates exploration of a phenomenon within its context using
a variety of data sources. This ensures that the issue is not explored through one lens, but
rather a variety of lenses which allows for multiple facets of the phenomenon to be revealed
and understood. “
Definition and context of application. The case study research is generally used to test
how scientific models and hypothesis works in a realistic situation. Social scientists such
as ethnographer have validated the research methods frameworks and emphasize an
holistic approach mixing the general picture with concrete evidence. The case study
research methods have been well described in the literature by Stake et al (Stake, 1995)
and Yin et al (2003). Some methodology description and additional framework have been
suggested in the literature to guide students conducting research (Zucker, 2009). In a
2003 paper Johansson et al (2003) introduce four case study methodologies generally
used that include Hypothesis testing; Theory generating; Naturalistic generation;
Synthesising a case (Table 1).
Table 1 – Case study methods (Johansson, 2003, page 10)
Proposition for our research question. In our case, a study of the investment decision
making in early stage biomedical start-ups, we want to determine the types of decisions
made by VCs investors and the influencing factors. A case study was chosen on generic
new venture creation because the investigation could not be studied without the context
(a start-up company) and more specifically the entrepreneur, the investor and the
common biomedical ecosystem where they evolve including the incubators, advisors and
mentors.

The procedure we are following is a “Hypothesis testing” approach. We are considering
testing VCs investment decision-making model in a context of a new venture creation
case. In order to reduce a unique view bias, we will reflect on the case outcomes after
testing the models, taking into account the evidence of other start-up deals within the
common ecosystem. Thus, discussing our findings in a “Theory generating” methodology,
we aim to reflect on VCs decision in early stage venture investment.
Conducting the case study. The case study research methods (Yin, 2014) work well to
analyse research questions on “How to make decisions”, in particular when observations
do not influence the practices of the field studied and where the phenomenon studied is
embedded in the context and situation. The method, as described on Figure 14, consists
therefore in establishing evidence, arranging those in a structured context and reflecting.
The case study relies on its unit of analysis, this determines how the evidence is linked to
the research question propositions and on criteria to derive conclusions from the
findings.
Figure 14 – Chain and convergence of evidences in case study (Yin, 2014, page 100)
The relevance of the case study relies first on evidence linked to the deep understanding
of the start-up we are creating. New venture case studies have been considered in the field
of life sciences (Laurel, 2013), (Park, 2005), (Siegel, 2007). The relevance of our research
also comes from the narrow community of life science venture capital investors we have
built using the LinkedIn social network and qualitative data we have extracted.
We aim to address the following points on evidence we observe during the case study:
 What are the sensitive criteria that impact the model on investment decision?
 Which levers on the macro and microenvironments reduce the complexity?
 Can models help to align investors and entrepreneur on a rational venture funding?
 Does the risk reduction have the potential to trigger more investment in early stage?

Context of the case study: new venture creation
Business model
Biotech companies efforts to attract private investment rely on the strength of their
business model (Figure 15). Companies focus on few years periods to deliver the most
value. Companies business plans generally include achievable objectives (short/long
term) and structured a roadmap with milestones measuring value creation at different
stages using the Osterwalder et al framework (Osterwalder, 2013).
Figure 15 – Short term value creation business model

This strategy gives companies a clear vision of business priorities and performance
indicators as suggested in the biomedical industry by Chen et al (Chen, 2012): “The
research results show that the KPIs of this Biotechnology enterprise are the turnover rate of
cash, the percentage of patent numbers and the growth rate of sales.”
Tzu-Chun Sheng - How to Evaluate the Performance of the Taiwan Biotech and
Biopharmaceutical Corporations? - International Business Research; Vol. 8, No. 10; 2015

External information gathering balancing the case study
We are using the professional social network LinkedIn to collect qualitative information
for our case study analysis. LinkedIn is a social network that mixes professional profiles
as well as company profiles. Therefore Linked-In offers to their users access to a
continuous flow of professional information predominantly coming from corporation
official channels, relay of specialized press journalists and individuals expert blogs.
LinkedIn gives also the possibility for people to connect with each-other and engage in
private discussions mostly around their professional sector (business development,
career move, professional advise). The use of social network in case study research has
been highlighted in several papers (James, 2007), (Salman, 2005).
Collection of qualitative evidences
In a three months period, we have created a profile in LinkedIn called Life Venture Capital,
following the venture capital firms in the field of life science and connecting the life
science investor professionals. The profile was transparent for networking with
professionals, stating that it was indented to gather information in a context of a master
thesis research at the University of Warwick.
Following around 200 companies. With the Life Venture Capital profile we have
selected the main venture capital firms involved in life science investment. We have
therefore access to company posts and information on their portfolio, recent exits,
partnerships in syndication, performance index, issues and challenges they want to
address and claim to overcome.
Network of around 200 professionals. With the Life Venture Capital profile we have
connected investors specialized in life science. These professionals are either analysts,
general partners, investment advisors coming from large to small independent VCs
including biomedical industry corporate VCs.
Findings extraction from information gathering
The LinkedIn profile allowed us to gather an enormous amount of information and
observation from the life science sector. We have had access to many opinion leaders
comments on the ecosystems, reacting and following-up as experts in the community.
Observations from the sector and statements collection. Following companies and
connecting individuals provided us a daily flow of information that we are reading and
analysing in details when relevant. This information gathering, analysis, findings and
derived conclusions helped us to understand the importance of micro and macro
environments in the operation of life science start-up investments.
Consultation of expert via qualitative open questions and polls. We have taken the
opportunity to follow some leaders’ vision on LinkedIn. This has allowed us to collect
opinions from the community, test ideas, see the influence of biases in topics comments.
We have identified a first circle of experts and we have been able to compare views and
perspectives angles from different VC firms differentiated by size, location and
background.

3.2 Investment decision models
Models criteria
Our analysis focuses on three actors for investment decision-making that are the venture
start-up, the investor and the entrepreneur. In order to keep the modelling relevant we
have limited the number of variables to four criteria both macro and micro environments.
The model will maximize input from the microenvironment looking at companies paving
the way within their ecosystem and minimize the threats from the macroenvironment
looking at solid and flexible structuration to adapt to changes.
Microenvironment criteria
The microenvironment in Biomedical investment is an ecosystem wherein evolves the
three actors (Figure 16). The biomedical industry context is described in annexe (p65).
The more the actors master their common environment, the better they will succeed in
creating value, in particular matching the expectation from the major industry players in
term of open innovation, medicine personalization and cost-effectiveness.
Figure 16 – Microenvironment criteria influencing investment performance
Venture. Creating value in the venture while mastering its environment requires the
competitive advantage of the product and entry barriers, the importance of the problem
solved in term of market. The success will also depend on the expertise, experience of the
team and the business model on cash needs, break even, revenue, scalability.
Investor. The performance of the sector and its attractiveness is a main driver. The
investor will also equilibrate their portfolio in terms of risk and rewards including
geography and technology. At the earliest stages, investor will share risks and
investigation expenses with others. The capability to syndicate other investors and
connect with large industry players is key to maximize revenue and exit opportunities.
Entrepreneur. Combining experience and creativity is necessary to foresee
opportunities. The credibility in the science and business increase success in engaging
partners within the ecosystem. Mastering the ecosystem with mentors to take distance is
an asset and when combined with pragmatism and profit focus, it becomes a talent.

Macroenvironment criteria
The macroenvironment factors such as the politics, economics, social, technology and
environment are disconnected from the business control and therefore can impact
positively or negatively the investment process and its outcome (Figure 17). We have
identified criteria that minimize the bad influences of the macroenvironment on the
investment performance. We divide those criteria between our three actors and suggest
what organization and behaviour help to anticipate threats to create opportunities.
Figure 17 – Macroenvironment criteria influencing investment performance
Venture. The technological changes can impact the business, we recommend to be
focused on the problem to solve and adapt the technology accordingly. Competition
outside of the ecosystem helps to identify to potential threats or alliances and refine the
business model. The regulation and reimbursement practices are different in the region
of the world of operation, in particular in emerging markets. Solid scorecard and slick
processes along the supply chain help to adapt to macroenvironment changes.
Investor. With the recent banking industry downturns, financial regulations have been
put in place by the government as well as incentives (tax breaks) to invest in start-up. The
profits and return drives the investment strategy and decisions. In 2010 profits were rare
with little M&A and IPO exits due to economical slow-down and stock market crash.
Investment operations in new technology start-up need a lot of effort at lower cost to
select potential deals, conduct deep analysis and due diligence and advise companies
during the investment period to maximise their return. The investor has built around its
firm a “capital” that allows to attract future funding and sustain its business.
Entrepreneur. Entrepreneurs should demonstrate generic skills and behaviour that will
sustain the business regardless the macroenvironmental changes. Trust and business
ethics are essential when involving the investor reputation. A key success factor comes
from engaging people on company values and creating leadership that keep the team
together during the hard times. The deep motivation and tenacity should stay intact but
there are multiple ways to achieve the final goal. Thus, the capacity to reflect on mentors
and investors challenging advice is essential. The entrepreneur awareness, self-
knowledge and emotional mastery are key to make decision in a systematic way and resist
to pressure when facing difficulties.

Decision tree model
Decision tree theory, advantages and limits
Decision trees are flow-chart structures with branches and nodes. The nodes represent a
test with a weight (probability) of an outcome to happen. The branches describe the
outcome specifics. The succession of branches and nodes constitute a path from root to
leaf that summarizes the whole complexity of the problem addressed. The combination of
weights or conditional probabilities along one path gives the score of a scenario.
Comparing scores and visualizing paths helped both to support a decision and consider
alternatives.
More precisely, decision tree understanding comes from the hierarchy in branches from
roots to leaf and the decision nodes impact (Damodaran, 2015). The root represents the
start of the decision. They usually measure a significant risk linked to a strong outcome.
Secondary nodes usually are more on gambling and their combination of negative
outcomes can be compensated with positives ones. Decision tree node rules usually rely
on precise information and facts.
Build the model for venture capital investment decision making
We suggest an optimal decision tree model for investment decision-making in biomedical
new venture that includes rational and psychological judgment criteria from venture,
investor and entrepreneur at the time of an investment (Figure 18).
Figure 18 – Decision tree for Biomedical early-stage investment decision making
We consider in order of importance first the venture, its microenvironment
counterbalanced by its macroenvironment, then come the investor and last the
entrepreneur both also influenced by micro and then macro environments. There are a
total of 24 criteria knowing that each of the three actors has are four criteria for each of
the micro and macro environments. Each criteria has a probability weight for its
importance as compared to other criteria and is evaluated on scale from 1 to 5. A Go/NoGo
score and threshold is defined to further qualify the investment decision.
Venture
Investor
Entrepreneur
Micro
Micro
Micro
Macro
Macro
Macro Go
NoGo
Go
NoGo
Go
NoGo
Refine venture project
Refine investor target
Refine team roles and
leadership
Due
Dilligence

Scoring system. At each node, 100% of weight is divided between the 8 criteria. We
allocate 70% of weight for microenvironment criteria and 30% of weight for
macroenvironment criteria. This highlights that the microenvironment contributes more
to success while macroenvironment uncertainty risk can be universally reduced with
flexibility and anticipation associated with structuration. We consider an average score of
3/5 on will allow for a Go threshold, any score below will require the project to refocus
on its venture fundamentals and/or its investor profile and/or its managing team roles,
governance and leader.
We also associate the final investment Go decision with a weight of 50% of the Venture, a
weight of 30% with the Investor and 20% for the Entrepreneur. This highlights that the
venture potential is the most important success factor where investor or entrepreneur
can be adapted or consolidated.
Justification. Criteria scoring from 1 to 5 will follow a scale where 1 represents high
failure risk and 5 represents best in class. Venture capital will generally require an
average score between 3 and 4 before conducting an investment decision. We will
consider three recent investments in the biomedical field to justify our model.
System dynamic model
System dynamics theory, advantages and limits
System dynamics diagrams comprise feedback causal loops, and stock and flows
describing the accumulation of parameters over time. A causal loop diagram is a simple
map of a system sub-units that are linked together with feedback loops that produces
positive reinforcement (labelled R) generating growth and negative balancing force
(labelled B) reducing the accumulation. Feedback loops have different forces depending
on the time frame when the model is applied. For instance, system would expect growth
in short run and then decrease in the long run. A stock and flow diagram describes the
stock as a sub-unit that accumulates or depletes over time and the flow as the rate of
change in a stock. Mathematically, the equations linking sub-units in a diagram are usually
a system of coupled, nonlinear, first-order differential equations.
The quality of the model is linked to the quality of the key variables and causal
relationship of the dynamics of VC investment. Focusing on rational and psychological
judgment criteria of VCs would help to build a comprehensive model for investment
decision-making in biomedical new venture.
Build the model for venture capital investment decision making
We suggest an optimal system dynamic model for investment decision-making in
biomedical new venture that includes rational and psychological judgment criteria from
venture, investor and entrepreneur at the time of committing to an investment.
We consider a funding system where investment creates value that creates potential
return. The time scale we use is the average 6 years period with initial investment effort
and later return expected. Our time scale on the 6 years period is represented as a 0-100%
Effort unit.

On Figure 19, we consider a stock and flow diagram with on the Y-axis the Investment
decreasing and flowing into a Venture return. The X-axis takes into consideration the
amount of time shown in effort unit to consolidate the venture. Each criterion is scored
from 1 to 5 and has a probability weight allowing a gearing effect.
Figure 19 – Example of stock and flow graph derived from the model
Model and scoring. Venture investments decrease with the level of effort in the venture
and venture returns increase. The gearing effects from the microenvironment create
opportunities that positively multiply the return on investment for the venture. The
Macroenvironment criteria negatively impact the investment in the venture.
All three actors venture, investor, entrepreneur, contribute together to the
microenvironment opportunities in a synergetic manner with different weights
(respectively 50%, 20%, 30%). The macroenvironment negative influence intervene at
two levels, first decreasing the available fund invested taking into account
macroenvironment at the level of the venture, investor and entrepreneur (respectively
50%, 20%, 30%). Second, initial venture investment takes into account
macroenvironment contingencies at the level of the venture (weight 50%).
Figure 20 – System dynamics for Biomedical early-stage investment decision making
+
+
+
Sy
In
E
V
Micro influence
on value crea on
0%#
20%#
40%#
60%#
80%#
100%#
0# 20# 40# 60# 80# 100#
Return#
Invested#
0%#
20%#
40%#
60%#
80%#
100%#
0# 15#30#45#60#75#90#
0%#
50%#
100%#
150%#
200%#
0# 15#30#45#60#75#90#
Venture
investment
Venture
return
Con ngencies
Venture Macro
+
+
-
-
+
-
Funding capacity
Investor Macro
Entrepreneur Macro
Venture Macro
Synerge c Opportuni es
Investor Micro
Entrepreneur Micro
Venture Micro
Micro influence
on value crea on
Macro influence
on investment
Investment
Value crea on

Model hypothesis. The total return is the opportunities created corrected from the
investment spent, we calculate it as an absolute difference for simplification. Our objective
is to extract some indicators that are representative of influence of the criteria on the
investment return. We have therefore defined a multiplier parameter which is the ratio
on the return compared to the investment in the venture and multiplied by the macro and
micro weighted criteria. We measure a multiplier parameter of 2 for a time representing
an effort of 20% and a multiplier parameter of 10 for a time representing an effort of 80%.
We consider those are good indicators of the potential theoretical returns for investors.
 Macro and Micro factor:
Macrofactor = Venture Crit x weight + Invest Crit x weight + Entrep Crit x weight
Microfactor = Venture Crit x weight + Invest Crit x weight + Entrep Crit x weight
 The investment curve follows a decreasing exponential law over time:
Invested = 1 – exp - ( (Effort/100) / (5-Macrofactor)/5 )
The slope of investment curve depends on the Macroenvironment criteria that combine
venture, investor and entrepreneur with different weights. This allows to distribute
influence of actors with the importance of investment.
 The Macro influence of the venture impact negatively the investment curve:
Macro influence = Invested (1 – (Venture Macro Criteria/5) * weight) )
The investment is slightly impacted by contingencies linked to the macro venture criteria
exclusively at the time of the new venture value creation.
 We consider that from the fist moment of investment, there is creation of value. We
introduce a Micro influence curve that follow a logarithmic law over time:
Micro Influence = Log( 1+ (Effort/100) x (Microfactor/5)^2 )
The Micro influence increase with the time and effort invested in the venture. The slope
of the Micro influence curve depends on the Microenvironment criteria that combines
venture, investor and entrepreneur with different weights. The Microenvironment
criteria synergetic effect in the ecosystem is highlighted using a square factor.
 We define the return parameter as the abosulte difference between the Micro and
Macro influence curves.
Return=Abs(MicroInfluence-MacroInfluence)
 We define the multiplier parameter as the ratio between the Micro and Macro influence
curves and multiplied by the Macrofactor and Microfactor.
Multiplier= (Micro/Macro Influence) X ½ (Microfactor/5 + Microfactor/5)

Chapter 4 – Case Study Application
Supervised analysis of biomedical investment
In this chapter we collect information from the case study on the biotech company to
evaluate the criteria and apply the two models. We conduct first an environmental
analysis using frameworks and tools usually used to asses companies performances. This
analysis allows us to identify the company strategic advantage and therefore evaluate
accurately criteria for the models. Applying the models on those criteria we obtain results
on the investment return potential. We analyse those results to extract findings for further
discussion.
This editor version of the manuscript only present empty framework that could be
applied to the evaluation of a Biotech company. Only generic information is available for
confidentiality purpose.
- The internal analysis with strengths and weaknesses
- The external analysis with opportunity and threats
- The Strategic advantage and criteria for the models

4.1 Strategic advantage
Environmental analysis
The environmental analysis is divided between the internal and external evaluation of the
firm environment. We use the company general assessment toolkit (appendix p78). This
will define criteria to support the investment decision models.
Internal analysis. We are considering biomedical industry new venture competition
around the generic kind of biotech company using Porter’s five forces framework on
Figure 21 (BusinessWire, 2010). We focus on the importance to attract new partnerships
with the biomedical industry and private investment. The competition between biotechs
for investment makes it necessary to strengthen a marketing strategy for attracting
venture capital.
Figure 21 – Porter 5 forces company analysis
Strengths and weakness in the internal environment. The biotech company needs to
be excellent in its technology as barrier criteria. Then the company needs to demonstrate
its value and differentiation as compared to substitute and different technology that
address the same therapeutic indications. Communication of those distinctive features via
scientific journals and rely on specialised websites and social networks will make a
difference to distinguish from the crowd. The pharmaceutical industry co-development
opportunities will also depend on the support from patient and caregivers associations
whose role is central in ensuring adequate pricing.

External analysis. It is crucial to understand the environment where the biotech
company operates to define the right value creation strategy aligned with our objectives.
We use the ESTEMPLE framework at the macroeconomic level and Porter’s five forces at
the industry level (Reuters, 2011).
Table 2 – ESTEMPLE analysis for the company environment
Opportunity and threats in the external environment. One important focus for the
general strategy of biotech is the requirement for streamlining the partnership with
academia and hospitals in order to preserve the value creation. The priority is to focus on
drug access to market for treating patients with incurable diseases. The quality of the
agreement and negotiation at the start of the company creation are fundamental. If the
company dilutes its value creation, they will not attract investors to sustain its growth.
Another threat comes from the regulatory framework and the expertise required to
archive the several milestones to the clinical proof of concept. A final threat is linked to
the increase in first stage amount funded to fewer companies. This requires more initial
seed funding for sustaining the company structuration and its first years development.

Strategic advantage
The strategic advantage analysis helps to identify the key resources that company has
already or need to acquire. We clearly see that a major asset with IP, know-how of staff
and knowledge shared with key suppliers. This constitutes the first sustainable
advantage.
Resource-based view & Core competencies. We are looking at the generic biotech core
capabilities that can be considered as a competitive advantage. We refer to some
innovation concept from Kostopoulos et al (2002) to conduct an analysis using the VRIO
and the Wernerfelt and Barney frameworks on human capital, physical and technological
assets, and organisational process as shown on Figure 22 (Kostopoulos, 2002).
Figure 22 – VRIO analysis for competitive advantage
SWOT Analysis. We use the framework (Figure 23) comparing the generic biotech
company organization with the biomedical industry landscape to emphasize strategic
advantages and options. This is a common approach from the biomedical industry well
described in the Austin et al reference book (Austin, 2012).
Strategic options
 Convert weakness into strengths
 Convert threats into opportunities
 Match strengths and opportunities
 Avoid threats becoming weakness
Figure 23 – SWOT Analysis for strategic options

Chapter 5 – Case Study Discussion
In this chapter reflect on the investment decision-making. First we look at limitation and
improvements of our models and our case study approach. We consider some
recommendations to improve the methodology. In a second part we look at the qualitative
data sourced from LinkedIn to describe the investment environment and identify trends
and facts that challenge our model. We then rethink the investment strategy for the new
venture and suggest a plan that improves our investment assessment according to our
models.
- The limits and improvement of model based decision making
- The limits and improvement of case study centric approach
- The environment and trends from early stage life science investment
- The biotech company refined fundraising strategy to involve investors

5.1 Discussion on model based investment strategy
Case study feedback on the benefit and limits of implementation of model
based investment strategy
Heuristics and modelling methodology strengths and limitations. We have
introduced heuristics that relies on a panel of criteria, weights and rules allowing to tests
the influence of several parameters and actors point of view when taking a decision. The
tool definitively helps to make systematic analysis of the environment and avoid most of
the general judgments biases (Bueschen, 2015) such as similarity and local bias,
anchoring, information overload. We follow the “Analyse, define and adjust” rules in the
decision-making and we look at improvements reflecting and analysing on recent
investment conducted by successful VCs.
We have conduct a sensitivity analysis on both models to tune the parameters and extract
metrics that seems relevant according to our new venture company as compared to a
panel of reference companies more mature that have data on post investment
performance and exits. Our case study however lacks enough data to be relevant and our
models could be refined accordingly, parameters, weights and criteria adjusted and
renamed. In the particular case of system dynamics we make the assumption of a
synergetic effect of the microenvironment criteria between the three actors that
maximize the investment return potential. This is a strong hypothesis on the trends
observed in the biomedical industry but we need more data to validate more accurately
the model. Models are also biased and can be easily tuned to answer questions the way
we want. The use of model therefore is not intended to make a decision but more to
highlight sensible criteria in companies we analyse and reflect on why certain criteria
influence the decision and what we can do about it. In the case of our new venture, the
analysis has highlighted an exposure to macroenvironmental risks whereas we were
focussing mainly on our excellent product, market and people.
Case study methodology strengths and limitations. Merriam (2009) insist on the
advantage of case study being “the best plan for answering the research questions; its
strengths outweigh its limitations. The case study offers a means of investigating complex
social units consisting of multiple variables of potential importance in understanding the
phenomenon.” The limitations can be significant. First bias comes from the sensitivity and
integrity of the investigator. We will have the tendency to highlight positively the assets
of our new venture and minimize some difficulties and challenges we encounter. We are
careful not to compromise our integrity but being part of an entrepreneurial project
induces some pressures that influence our subjectivity and sensitivity. The second bias
would come from our lack of direct experience inside Venture Capital firms. We have the
feeling we miss the inner view and have lots of difficulties reading between the lines. The
few meetings we had with investors looking for advices in preparation of our fundraising
campaign is not enough. There is a also a gap of knowledge that qualitative data from
LinkedIn may not fill. As we orient our answer to the question that best match our project,
there is therefore a bias risk on what information we have selected and put in front and
others we have decided not to present. We have been careful to use strong source of
validated information and avoid the misunderstandings about case study research
(Flyvbjerg, 2006).

Benefit and limits of implementation of model based investment
Improving judgment and decision making with models. The decision-making tools
intention was to reflect on an investment in a systematic way avoiding biases and
blindness on own ideas. The recent event with the biotechnology stock market bubble will
produce a halo effect for investors on liquidity risks and entrepreneur on valuation. We
have shown that those two macroenvironment factors will only impact slightly two of our
criteria and final score of the investment because the other fundamentals are good.
Therefore we believe our modelling based heuristics and systematic decision making
improve the process in particular it obliges all actors to maximise their interactions on
microenvironment and define actions to contain the macroenvironmental risks.
Reflective analysis considering the investment environment. We have investigated
our new venture fundraising strategy with the environmental analysis toolkit and
reframed our finding using our two decision making models. This has allowed to spot
some strengths but has also pointed some improvements that we need to address as part
as our fundraising strategy. An evaluation framework is in appendix (p89). Looking at
the qualitative data from the Life Science innovation community, we have identified what
were the trends and expectation from the stock market and how investors were reacting
to it. With more than 200 investors and 200 VCs firms connected through LinkedIn, we
have collected ideas from experts, example of deals, explanation from financial advisors
and statements from specialists. This has given us some understanding of the context and
levers that will accelerate our growth potential by mobilizing the right investors at the
right development stage.
A summary of our learning is in Table 3. We have conducted a reflective analysis on the
qualitative data that have corrected some of our bias and understanding of the venture
capital community. Some statements from experts have even reinforced our new vision
on approaching our business strategy. In addition, the model and the criteria analysis has
allowed us to identify which parameters have the most impact and prioritise our efforts
when redesigning our fundraising strategy.
Table 3 – Learning from the research
Learn from case study Learned from LinkedIn qualitative data
- Inside a start-up need for strategic decision
- Balance between asset and operations
- Team, governance, leadership and association
- Investor strategy and structuration around deals
-Macroenvironment affecting the investment
dynamic
Recommendations in term of practices. We have highlighted the importance of biases
in the investment process and all the factors that contributes to maximize the return on
investment. Our model based analysis has shown for our new venture that, despite high
quality company and science, our exposure to macroenvironment was higher compared
to reference companies and thus our attractiveness for investment was reduced.

Nutopya White Paper Biomedical Investment Decision

Nutopya White Paper Biomedical Investment Decision

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