An integrated K-means-GP approach for US stock fund diversification and its impact due to COVID-19

The stock fund diversification process is a tedious task due to the erratic nature of the stock market. On the other hand, work is more challenging due to high annual return expectations with low risk. This research work explores the potential of goal programming (GP) and K-means algorithm as an integrated K-means-GP approach for fund diversification, where K-means is used to create groups of stock based on their performance. Then GP is used to diversify total funds into various groups of stocks to achieve a high annual return. The experimental work has been done in 30 stocks of DOW30 of the years 2017-2018, 2018-2019, and 2019-2020. A comparative study was carried with three different cases based on individual year data and an average of two and three years of data. The empirical results show that: the K-means-GP approach outperformed the GP approach for stock fund diversification; the annual return is higher in the case of the K-means-GP approach using three years of average data with 12.59% of annual return against the expected annual return of 20%. Due to COVID-19, few stocks perform in the negative direction, and hence the annual return is being affected after fund diversification.