Skip to Main Content
Using Graham's rules on picking stocks has been proven to generate profits for value investors. We propose using 3D subspace clustering to generate rules to pick potential undervalued stocks; 3D subspace clustering is effective in handling high dimensional financial data, is adaptive to new data, and its results are not influenced by human's biases and emotions, and are easily interpretable. We conducted extensive experimentation in the stock market over a period of 28 years (from 1980 to 2007). We found that using rules generated by two 3D subspace clustering algorithms, CATSeeker and MIC, results in 60% more profits than using Graham's rules alone.