Date of Submission
Open Access Senior Thesis
Bachelor of Arts
The 2015 article Creating Diversified Portfolios Using Cluster Analysis proposes an algorithm that uses the Sharpe ratio and results from K-means clustering conducted on companies' historical financial ratios to generate stock market portfolios. This project seeks to evaluate the performance of the portfolio-building algorithm during the beginning period of the COVID-19 recession. S&P 500 companies' historical stock price movement and their historical return on assets and asset turnover ratios are used as dissimilarity metrics for K-means clustering. After clustering, stock with the highest Sharpe ratio from each cluster is picked to become a part of the portfolio. The economic and financial implications of the clustering results are also discussed. In the end, portfolios constructed with clustering results of stocks' historical price movements perform poorly, but portfolios constructed with clustering results of companies' financial ratios consistently exceed market average. Using an alternative portfolio construction method that represents each cluster proportionally with regards to their sizes, portfolios constructed with historical stock price movements gain an increase in performance, while the returns of portfolios constructed using companies' financial ratios decrease. Further studies should be done with a different portfolio performance index and a larger dataset.
Bin, Shu, "K-Means Stock Clustering Analysis Based on Historical Price Movements and Financial Ratios" (2020). CMC Senior Theses. 2435.