A fuzzy online portfolio selection algorithm based on pattern matching approach

The present study seeks to propose an online portfolio selection algorithm based on a pattern-matching approach considering fuzzy returns. Online portfolio selection algorithms are concerned with capital allocation to several stocks to maximize the portfolio return over the long run by deciding the optimal portfolio in each period. Contrary to literature algorithms that calculate daily returns merely based on the closing price of the day, this study considers fuzzy returns to recognize similar patterns more efficiently. The Fuzzy C-Means (FCM) method has been used to cluster fuzzy returns in the sample selection step. In the second step, a log-optimal portfolio with transaction costs has been used to optimize the portfolio. The suggested models have been applied to five datasets with unique characteristics to evaluate their performance. Results indicated these algorithms outperformed the algorithms proposed previously in the literature on online portfolio selection in terms of total return and risk-adjusted return.