High-Yield Dividend Stocks: Data Mining I

Data mining methods commonly employ either generative or discriminative approaches, where the former is model-driven and based on probability theory and the latter is data-driven and based on discrimination. This article looks at discriminative data-driven unsupervised methods for natural grouping of 43 high-yield dividend stocks based on 46 attributes represented by recent key fundamentals. The idea here is to graphically present feature-rich data in a way that reflects similarity in patterns of association that otherwise are not cognitively discernable through tabular listings. First, we perform linear hierarchical cluster analysis [HCA] which agglomerates stocks together to construct a single tree with the individual stocks acting likes leaves on the end of branches of varying length. The length of a branch reveals how dissimilar the stocks are in terms of correlation. Second, we employed a linear clustering method known as crisp K-means [CKM], which partitions the stocks into k groups of stocks with similar key fundamental data. Similarity here is based on closeness to the average of each feature (key statistic) for the stocks in a group. As an alternative to CKM, we used the non-linear neural gas [NG] method for clustering the stocks -- which is a neural adative learning method based on ranks. For NG, feature values were normalized into a range of [0,1] prior to iterative learning. Finally, another non-linear neural adaptive method called self-organizing maps [SOM] was used for dimension reduction to reduce the 46 key statistics for each stock down to 2 "coordinates" which reveal a map location of each stock after learning is completed. Stocks that are similar end up in the same region of the two-dimensional map. For SOM, feature values were normalized into a range of [0,1] prior to iterative learning.... Read more