JACIII Vol.26 No.4 pp. 451-460
doi: 10.20965/jaciii.2022.p0451


Empirical Analyses of OLMAR Method for Financial Portfolio Selection in Stock Markets

Kazunori Umino*1, Takamasa Kikuchi*2, Masaaki Kunigami*1, Takashi Yamada*3, and Takao Terano*4,†

*1Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

*2Keio University
4-1-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8521, Japan

*3Yamaguchi University
1677-1 Yoshida, Yamaguchi-shi, Yamaguchi 753-8511, Japan

*4Chiba University of Commerce
1-3-1 Konodai, Ichikawa-shi, Chiba 272-8512, Japan

Corresponding author

November 17, 2017
January 13, 2022
July 20, 2022
on-line portfolio rebalance algorithm, anomaly detection, mean reversion, OLMAR method
Empirical Analyses of OLMAR Method for Financial Portfolio Selection in Stock Markets

Conceptual diagram of DCO

The OLMAR method, which stands for the on-line moving average reversion method, is reported to be one of the most powerful among portfolio selection algorithms in the stock markets. In this research, we use intensive statistical and simulation analyses of long-term data on stock market changes to uncover the secrets of why and when the superiority appears. We find that there have been long-lasting fluctuations in the stock markets and that the OLMAR method actively makes use of such characteristics. In this paper, we analyze long-term stock data from Japan and the United States. The analyses confirm the following points. 1) The OLMAR method yields superior returns. 2) By using the moving average divergence rate provided by the OLMAR method, it is possible to detect specific fluctuation characteristics in long-term stock data from Japan and the United States. 3) Superior returns cannot be obtained from data in which specific fluctuation characteristics have been corrected.

Cite this article as:
K. Umino, T. Kikuchi, M. Kunigami, T. Yamada, and T. Terano, “Empirical Analyses of OLMAR Method for Financial Portfolio Selection in Stock Markets,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.4, pp. 451-460, 2022.
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Last updated on Aug. 05, 2022