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Paparrizos, J., & Gravano, L. (2015). K-Shape: Efficient and Accurate Clustering of Time-Series. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (pp. 1855-1870). ACM.
https://doi.org/10.1145/2723372.2737793

has been cited by the following article:

• TITLE: On Prediction of Stock Return and Volatility Using Clustering Techniques: Taking an Example of Japanese Stock Market

  AUTHORS: Jieni Liu, Hisashi Tanizaki

  KEYWORDS: Stock Return, Volatility, Clustering, K-Means, K-Medoids

  JOURNAL NAME: Open Journal of Social Sciences, Vol.13 No.10, October 24, 2025

  ABSTRACT: Stock returns exhibit nonlinear dynamics and volatility clustering. It is well known that we cannot forecast the movements of stock prices under the condition that market is efficient. In most research, it is concluded that stock markets are efficient and accordingly stock returns are not predictable. However, using some clustering techniques and choosing the stock returns in the cluster giving us high returns, in this paper we examine whether stock returns are predictable or not. To address this issue, we combine various data preprocessing techniques with three clustering methods (i.e., one K-means and two K-medoids clustering algorithms) in Japanese Nikkei 225 financial market. As a result, we cannot predict stock price returns, but we can predict volatility of stock returns. This result is consistent with a lot of past studies.