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An online portfolio selection algorithm using beta risk measure and fuzzy clustering | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 3، شماره 2، اسفند 2023، صفحه 63-76 اصل مقاله (342.86 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2024.76113.1107 | ||
| نویسندگان | ||
| Matin Abdi* 1؛ Seyyed Babak Ebrahimi2؛ Amir Abbas Najafi2 | ||
| 1Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran | ||
| 2Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran. | ||
| چکیده | ||
| An online portfolio selection algorithm has been presented in this research. 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. Despite other online portfolio selection algorithms that follow Kelly's theory of capital growth and only focus on increasing return in the long term, this algorithm uses the beta risk parameter to exploit upside risk while hedging downside risk. This algorithm follows the pattern-matching approach, uses fuzzy clustering in the sample selection step, and the log-optimal objective function along with the transaction cost and considering the beta risk measure in the portfolio optimization step. The implementation of the proposed algorithm in this research on a 10-stock dataset from the NYSE market in the period of December 2021 to December 2022 shows the superiority of this algorithm in terms of return and risk and the overall Sharpe ratio compared to the algorithms proposed previously in the literature on online portfolio selection. | ||
| کلیدواژهها | ||
| Pattern-Matching Approach؛ Risk-averse Model؛ Fuzzy C-Means؛ Transaction Cost | ||
| مراجع | ||
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