This article examines the viability of securitizing the professional athlete earnings of college tennis players choosing to pursue a professional career. The authors simulate the investment performance of securitized portfolios of tennis players on both a random basis and an informed basis. Historically, only 17% of college players who turn professional make a profit after expenses. They find that randomly selected portfolios of college tennis players have limited profit potential for investors, even after including generous estimates of endorsement income. This stands in contrast to the profitability of securitizing high-profile professional athletes with established brand values (in which endorsement income is very important). The authors explore the use of an informed selection of tennis professionals to form securitized portfolios. Informed selection substantially enhances the profitability of portfolios of players based even on simple metrics, such as their college rankings. They also explore the use of machine-learning selection models to form portfolios of securitized tennis players. However, they found no financial benefit of machine-learning selection models beyond the use of a simple selection criteria based on college ranking.
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Farinella et al. (2024) studied this question.
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