Stock Market Prediction using Hierarchical Agglomerative and K-Means Clustering Algorithm

Objectives: The stock market performance has more impact on national economy. The purpose of this work is to generate a portfolio to reduce the uncertainty of stock in short term basis. Methods: Hierarchical clustering is more efficient while non-determinism is of concern when compared with flat clustering. Hierarchical agglomerative Clustering is used, which results in more informative structure than flat clustering on unstructured data. Single-link clustering is taken into account as it does not pays more attention to outliers and amalgamation criterion is local than complete-link clustering and results in intuitive cluster structure. Dendrogram is used to represent the progressive formation of clusters in HAC. Findings: Flat clustering K-means algorithm is used to combine the clusters generated by Hierarchical agglomerative clustering (HAC). As the number of samples has been reduced, iterative use of k-means will choose better centroid. Applications: The final list of the recommended stocks is then showcased to the investor on short term basis. The baseline data is downloaded from National Stock Exchange (NSE).