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A Novel Approach for Fuzzy Online Portfolio Selection Model Using Dual Beta and Trading Volume

M. Abdi, S. B. Ebrahimi, A. A. Najafi

Abstract



The online portfolio selection has gained significant interest in recent years. It involves allocating capital among assets and updating the portfolio to maximize long-term return. This research presents an online portfolio selection algorithm incorporating fuzzy returns, dual-beta risk measure, and trading volume. The algorithm employs dual-beta to achieve maximum utility by exploiting positive market movements while avoiding negative ones. To make the model more realistic, each day’s return is represented as a fuzzy number, which avoids the simplifying assumption of trading at the stock’s closing price. Another key feature of this study is using trading volume to identify similar time windows. The algorithm presented in this study follows the pattern-matching principle. Fuzzy clustering is used in the sample selection step to cluster fuzzy returns and trading volumes. This helps identify time windows similar to the recent one, using Manhattan and Canberra distance criteria. In the portfolio optimization step, an optimal logarithmic objective function with the dual-beta risk measure incorporating transaction costs is employed. The algorithm underwent evaluation by testing it on five datasets of different time intervals and characteristics. The evaluation results demonstrate a substantial enhancement in returns and risk-adjusted returns compared to prior algorithms in the literature.


Keywords


Pattern-matching, Fuzzy return, C-Means clustering, Dual-beta, Trading volume. Regression, data structure, prediction, simulation.

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