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

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 un...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2022-07, Vol.26 (4), p.451-460
Main Authors: Umino, Kazunori, Kikuchi, Takamasa, Kunigami, Masaaki, Yamada, Takashi, Terano, Takao
Format: Article
Language:English
Subjects:
Algorithms
Asset management
Data analysis
Datasets
Empirical analysis
Informatics
Portfolios
Prices
Securities markets
Stock exchanges
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Summary: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.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2022.p0451