Clustering-based value investing strategy in the Helsinki Stock Exchange: k-means algorithm - Issakainen, Topi (Faculty of Economics and Business, University of Muhammadiyah Malang, 2023)
The purpose of this research is to study the possibility of combining quantitative clustering of stocks and value investing. The feasibility of this approach is tested using Finnish market data from the period 2005 to 2021. The benchmark index used in this research is the OMX Helsinki Growth Index. The strategy is based on the combination of P/E, P/CF, and P/B ratios, which serve as the basis for the k-means algorithm. The data is pre-processed by removing stocks that have not generated positive earnings and cash flow during the previous 12 months. The k-means algorithm assigns stocks to clusters, and the cluster with the lowest financial ratios is chosen as the value portfolio. The research also includes a sensitivity analysis of value portfolios, where the initial number of clusters in the clustering phase ranges from three to ten. Returns of different value portfolios are compared to each other and the benchmark index. The quality of results is evaluated using the Sharpe ratio and Jensen's alpha. According to the findings, the value portfolio constructed using nine clusters generated the highest risk-adjusted return, with an annual return of 30.27% over the 2005 to 2021 period. Furthermore, the best-performing value portfolio from 2005 to 2017 was compared to the benchmark index from 2018 to 2021. The value portfolio achieved an annual return of 26.05% during the 2018-2021 period, while the corresponding return of the benchmark index was 11.74%.