نوع مقاله : پژوهشی
نویسندگان
دانشکدهی مهندسی صنایع و سیستمها، دانشگاه تربیت مدرس
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In this paper, the problem of finding profitable pairs by automatically limiting the search space of pairs using machine learning techniques and integrating an unsupervised learning algorithm, OPTICS, to pair identification and selection in pair trading is discussed. In addition, to optimize the portfolio consisting of pairs of assets and allocate optimal capital to them, a genetic-based algorithm to increase the Sharpe ratio is used. The proposed technique for automatic clustering is better than the conventional methods of searching for pairs of assets used by investors and leads to a higher average rate of return on investment and a higher Sharpe ratio for portfolios in trading using selected pairs of clusters. These calculated evaluation criteria for the portfolio were improved after using a bi-objective optimization genetic algorithm. This study was simulated using intraday price data of a group of stocks in the Tehran Stock Exchange between the years 2015 to 2020 and taking into account the transaction costs.
کلیدواژهها [English]
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