Kuva tutkielman etukannesta

Master's Thesis in Finance Theory, Olli Arojärvi, 2001, Helsinki School of Economics and Business Administration

Two very complex but important fields of science are brought together in this study: valuation and biotechnology.

This study is aimed at providing information and tools for anyone interested in biotechnology, especially from the economic or financial perspective.

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Aim of the study

  1. to explain biotechnology and provide the understanding of the dynamics of the industry
  2. to identify and analyze current approaches for biotechnology firm valuation and summarize the current state of the research
  3. to construct a framework for biotech R&D project valuation that appropriately takes into account the special features of biotechnology and is useful in practice
  4. to identify and discuss about key value drivers in biotechnology and to form hypothesis around them
  5. to validate the discussion by testing these value driver hypotheses empirically with a real life sample

Table of Contents

1. INTRODUCTION
1.1 MOTIVATION FOR THIS STUDY
1.2 MAIN OBJECTIVES OF THIS STUDY
1.3 THE STRUCTURE OF THE STUDY
2. BIOTECHNOLOGY INDUSTRY
2.1 BIOTECHNOLOGY
2.1.1 Definitions
2.1.2 Sectors
2.1.3 The Importance of Biotechnology
2.2 INDUSTRY OUTLOOK
2.2.1 The History
2.2.2 The Current State
2.2.2.1 Finland
2.2.2.2 The USA versus the EU
2.3 INDUSTRY DYNAMICS
2.3.1 Industry Specific Features
2.3.2 Industry Specific Problems and Risks
2.3.3 Key Players in Biotechnology
2.3.4 Bioclusters and Alliances
2.3.5 Biopharmaceutical Business Models and Strategies
2.4 KEY SUCCESS FACTORS
2.5 NEW DRUG DEVELOPMENT PROCESS
3. HOW TO VALUE A BIOPHARMACEUTICAL R&D PROJECT / COMPANY
3.1 TRADITIONAL APPROACH -THEORETICAL DISCUSSION
3.1.1 Entity DCF Model
3.1.2 Economic Profit Model
3.1.3 Taking into Account Growth Opportunities and Flexibility
3.2 REAL OPTIONS APPROACH
3.2.1 Real Options Literature
3.2.2 Basics of Real Options
3.2.3 Types of Real Options
3.2.3.1 Option to Defer
3.2.3.2 Option to Abandon
3.2.3.3 Option to Expand
3.2.3.4 Option to Contract
3.2.3.5 Option to Switch
3.2.3.6 Growth Options
3.2.3.7 Applicability to Biopharmaceuticals
3.3 METHODOLOGIES TO BIOTECH VALUATION -SUMMARY OF THE PREVIOUS STUDIES
3.3.1 Qualitative Models
3.3.2 Decision Trees
3.3.3 Discrete Binomial Lattice
3.3.4 Continuous Models
3.4 MODELING PHARMACEUTICAL DEVELOPMENT PROCESS
3.4.1 Success Probabilities
3.4.1.1 Actual Success Probabilities
3.4.1.2 Risk-Neutral Success Probabilitiesv 3.4.2 Cash Flows
3.4.2.1 Estimation Period Based on the Product Life Cycle and Patent Protection
3.4.2.2 Out-licensing vs. Own Sales
3.4.2.3 Different Scenarios
3.4.2.4 Basic Dynamics behind Revenues, Costs and Margins
3.4.3 The Discount Rate
4. VALUATION BASED ON KEY VALUE DRIVERS
4.1 BIOTECH COMPANY AS A PORTFOLIO OF PROJECTS AND INTANGIBLE ASSETS
4.2 PREVIOUS STUDIES
4.2.1 Individual Investment Opportunities and Exercise Capacity
4.2.2 The Value of Knowledge-based Alliances and Progression of Clinical Trials
4.2.3 Explaining Innovation Output and Commercial Ties
4.3 THE USE OF MARKET VALUE, RELATIVE MEASURES AND CONTROL VARIABLES
4.3.1 Market value and B/P -ratio
4.3.2 Control Variables
4.4 HYPOTHESIS
4.4.1 Focus vs. Diversification
4.4.1.1 Sectors
4.4.1.2 Projects
4.4.2 Pipeline Balance
4.4.2.1 R&D Projects
4.4.2.2 Products in the Market
4.4.2.3 A Balanced Pipeline
4.4.3 Collaboration
4.4.3.1 Total Number of Collaboration Agreements
4.4.3.2 Project Specific Collaboration in General
4.4.3.3 Focused Collaboration
4.4.3.4 R&D-based and Market-based Collaboration
4.4.4 Company's Financial Situation
4.4.4.1 Cash and EPS Trend
4.5 VARIABLES
5. DATA
5.1 THE U.S. SAMPLE -PIPELINE DATA
5.2 DATA FOR CONTROL VARIABLES AND RELATIVE PRICING
5.3 LIMITATIONS OF THE DATA
5.4 SUMMARY INFORMATION AND DESCRIPTIVE STATISTICS
5.4.1.1 Market Values and Profits
5.4.1.2 Control and Biotech Variables
5.4.1.3 Projects and Sectors
6. METHODOLOGY
6.1 CORRELATIONS
6.2 REGRESSION ANALYSIS
7. RESULTS
7.1 CORRELATIONS
7.1.1 Correlations between lnMV, B/M and Control Variables
7.1.2 Correlations between lnMV, B/M and Biotech Variables
7.2 RESULTS
7.2.1 Chosen Variables
7.2.2 Results from the Regression
8. CONCLUSIONS AND FURTHER RESEARCH
8.1 SUMMARY OF THE STUDY
8.2 SUGGESTIONS FOR FURTHER RESEARCH
9. REFERENCES