Start-up valuation in Switzerland: analysis and methods Master Thesis Candidate: Silvia Lama Supervisor: Prof. Dr. Didier Sornette ETH Zürich Department of Management, Technology and Economics (D-MTEC) Chair of Entrepreneurial Risks June 2019 – November 2019 1.1 Motivation and overview 2 0 Abstract 3 ABSTRACT The aim of this master thesis is to provide an overview of start-up valuations in Switzerland. The first part focuses on the analysis of funding rounds closed in Switzerland, between 2010 and 2019. The existence of patterns and trends is investigated, visualized, and commented. The second part selects the best model to estimate a range of pre-money valuation for a target start- up, as a fair benchmark. This could be used by investors and co-founders as a starting point in their investment negotiation process. Indeed, traditional valuation methods1 cannot be applied to start-ups, due to the uncertainty of the latter, their short history, absence of publicly available data on financials, comparable companies or transactions. As a consequence, new valuation methods have emerged and, in the conclusion chapter, they are compared to our approach, stressing their lack of objectivity, contextuality, accuracy, and precision. Finally, several traces for further research are recommended. 1 (e.g. the Discounted Cash Flow, the Valuation Multiples) 1.1 Motivation and overview 4 ACKNOWLEDGEMENTS I would like to express all my gratitude to Professor Didier Sornette, for the opportunity to conduct my Master Thesis at the Chair of Entrepreneurial Risks and for his guidance, and to Dr. Spencer Wheatley for the useful advice. Besides, I would like to sincerely thank Steffen Wagner and Michael Blank, for the trust demonstrated by choosing me to pursue this delicate and extremely interesting research project at Investiere. My warmest thank goes to Dr. Matteo Farnè, for the valuable support and interest in my work, and for being a reliable point of reference in my life. I also wish to heartily thank my mentor and angel investor, Professor Silvio Marenco, who has been always believing in me, for all the care and time he has been investing in my professional growth. Above all, during these years he taught me the value of respect and of building trustful relationships. If I achieved this goal, it is also thanks to the precious advice of my mentor Andrea Girardello. I feel truly grateful to all his attention and effort, allowing me to avoid many mistakes, and inspiring my winding path as a student-entrepreneur. He taught me to never give up, and that it is always possible to find a smarter way to face challenges, by thinking outside the box. This journey would not have been so special and unforgettable without the fantastic company of my dearest friends, and of my lovely flatmates, bringing sparkling colours to every day of my life. Finally, I am enormously grateful to my family, who makes me feel the luckiest person on the Earth, by supporting all my passions and activities, and by loving me as I am. 0 Acknowledgements 5 TABLE OF CONTENTS Abstract ......................................................................................................................................... 3 Acknowledgements ....................................................................................................................... 4 1 Introduction .......................................................................................................................... 9 1.1 Motivation and overview .............................................................................................. 9 1.2 Research questions ..................................................................................................... 10 2 Start-up valuation methods ................................................................................................ 11 2.1 Overview ..................................................................................................................... 11 2.2 Scorecard method ....................................................................................................... 11 2.3 Berkus model .............................................................................................................. 12 2.4 Venture Capital Method ............................................................................................. 13 3 Data Collection .................................................................................................................... 14 3.1 Sources of data ............................................................................................................ 14 3.2 Process ........................................................................................................................ 14 3.3 Description and pre-processing of the data set .......................................................... 15 3.4 Log Transformation ..................................................................................................... 19 4 Multivariate Data Analysis .................................................................................................. 22 4.1 Treatment of missing data .......................................................................................... 22 4.2 Correlation analysis between continuous variables ................................................... 22 4.2.1 Methodology ....................................................................................................... 22 4.2.2 lThrough_Investiere analysis ............................................................................... 24 1.1 Motivation and overview 6 4.2.3 Employees analysis .............................................................................................. 30 4.2.4 Analysis of the entire data set ............................................................................. 33 4.3 Correlation Analysis between categorical variables ................................................... 41 4.3.1 Pooling levels together ........................................................................................ 41 4.3.2 Methodology ....................................................................................................... 44 4.3.3 Results ................................................................................................................. 44 4.4 Correlation analysis between continuous and categorical variables .......................... 46 4.4.1 Methodology ....................................................................................................... 46 4.4.2 Results ................................................................................................................. 46 5 Predicting the future success of a swiss start-up ................................................................ 62 5.1 Overview...................................................................................................................... 62 5.2 Methodology ............................................................................................................... 62 5.3 Results ......................................................................................................................... 63 6 Multiple Regression Analysis ............................................................................................... 64 6.1 Purpose ........................................................................................................................ 64 6.2 Methodology ............................................................................................................... 64 6.2.1 Overview .............................................................................................................. 64 6.2.2 Steps .................................................................................................................... 65 6.3 Data pre-processing .................................................................................................... 66 6.4 Second Manual Variables selection (from 20 to 8 independent variables): ............... 66 6.5 Best models comparison ............................................................................................. 68 0 Acknowledgements 7 6.6 Best selected model .................................................................................................... 74 6.6.1 Confidence and Prediction intervals ................................................................... 74 6.7 MLR BLUE Assumptions Check .................................................................................... 75 6.7.1 Outlier detection ................................................................................................. 76 6.7.2 Check MLR assumptions ..................................................................................... 77 7 Conclusions ......................................................................................................................... 81 8 References ........................................................................................................................... 85 9 Appendix: Model specification and selection ..................................................................... 88 9.1 Automated Models ..................................................................................................... 88 9.1.1 Stepwise regression ............................................................................................ 88 9.1.2 Best Subset Selection .........................................................................................