Reassessing Index Inclusion: Algorithms vs. Human Bias

This chapter examines the shortcomings of traditional index methodologies such as the S&P 500, contrasting them with data-driven approaches like those of Bloomberg. It discusses the biases in committee decisions that shape index inclusion, using Tesla and Microsoft as case studies, while emphasizing the benefits of algorithmic decision-making. Furthermore, the chapter explores the complexities of corporate finance in relation to Bitcoin and its growing relevance in managing capital and mitigating risks for innovative companies.