In this article part of our Clustering series, [1] [2] [3] [4] which began by identifying clusters in relative percentile ranking vs. S&P 500 component returns. We discovered that market capitalization (MCAP) was a poor variable for clustering and highlighted a growth bias. We then extended the periodicities to observe the evolution of this growth bias. Today, we conclude by compiling all these periodicities of growth bias into a single chart. This "composite of a composite" (COC) reveals a moving picture of market dynamics, reminiscent of the seminal work on curvatures by Nobel Prize winner William Sharpe.
Humans are naturally biased, and nothing biases us like a winner. This is a straightforward explanation of growth bias. Because humans are growth-biased, stock markets are too. You don’t need science to observe growth bias—it’s conspicuous. However, you do need science to build models that can perform well despite growth bias, meaning models capable of predicting the evolution or decay of this bias.
This paper presents an empirical study of the S&P 500's performance over a period of 24 years, utilizing a novel approach termed "The Systemic Cluster." It diverges from traditional market analysis methods by employing annualized returns and percentile scoring in conjunction with K-means clustering to elucidate systemic patterns in stock market performance. The study's findings reveal a consistent positive drift for stocks with higher relative percentile scores, suggesting an inherent market tendency to reward winners. Additionally, the occurrence of occasional spurts in the highest decile indicates the presence of market extremities. The implications of these findings are discussed in relation to their predictive capacity for macro market trends and investment strategies.
