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.