The Spinoff Scorecard: An Investment Strategy to Separate the Best Performing Spinoffs from the Worst

Staffan Erik Linus Bülow & Nils Petter Glave Mjörnemark

Student thesis: Master thesis

Abstract

This paper designs and tests an investment strategy for spinoffs which we call The Spinoff Scorecard. The scorecard is a binary scoring system based on ten variables which measure the spinoff from seven perspectives: insider incentives, corporate governance, organizational structure, market neglection, capital structure, valuation and quality. We show that investing in a portfolio of spinoffs that receives a high score, earns on average a 60.5 % one-year excess return between 2000 and 2015. The corresponding figure for a low score portfolio is -23.5 %. A passive investment strategy that invests in all spinoffs earns on average a 7.3 % one-year excess return. Hence, a high score portfolio outperforms all spinoffs by 53.1 %. Therefore, our results indicate that it is possible to separate the best performing spinoffs from the worst performing spinoffs by utilizing The Spinoff Scorecard. The results from the scorecard imply that this study provides new insights into which variables that may explain why some spinoffs outperform and some underperform. Earlier research has primarily focused on spinoffs return performance and concludes that they outperform the market on average by 10.9 % (excess one-year return). However, no previous study has provided any extensive evidence that answers why spinoffs outperform the market and which variables that determine the strong performance and how to exploit this through a concrete active investment strategy. The success of the scorecard may be attributed to spinoffs generally being neglected by analysts and institutional investors. Our results document that spinoffs have a median analyst coverage of only one analyst. We argue from a behavioral finance perspective that this neglection may cause owners and investors to act in a biased manner and spinoffs may, therefore, become subject for inefficient pricing. Hence, this neglection may be exploited by a systematic and unbiased investment strategy as The Spinoff scorecard. This study utilizes a dataset of spinoffs which covers the United States, Canada and Western Europe between 2000 and 2015. The data sample of 690 spinoffs is the largest sample size in comparison to studies we have identified, which test spinoffs’ long run excess performance

EducationsMSc in Finance and Investments, (Graduate Programme) Final Thesis
LanguageEnglish
Publication date2019
Number of pages137
SupervisorsPeter Feldhütter