استخراج قواعد چند مرتبه به منظور معاملات سهام با استفاده از ساختار شبکه ای و یادگیری بازگشتی کیو

محورهای موضوعی : دانش سرمایه‌گذاری

محمدرضا علیمرادی ¹ ( دانش آموخته کارشناسی ارشد مهندسی صنایع-مهندسی مالی، دانشگاه تربیت مدرس )
علی حسین زاده کاشان ² ( استادیار دانشکده مهندسی صنایع و سیستم ها، دانشگاه تربیت مدرس (نویسنده مسئول) )

تاریخ دریافت : 1396/10/03 تاریخ پذیرش : 1396/12/22 تاریخ انتشار : 1398/04/01

کلید واژه: الگوریتم قهرمانی در لیگ ورزشی, قواعد چند مرتبه, تحلیل تکنیکال, یادگیری تقویتی,

چکیده مقاله :

معامله گران در بازار سهام به هنگام تصمیم گیری در مورد خرید یا فروش سهم علاوه بر اطلاعات روز جاری سهم، اطلاعات سهم در روزهای گذشته را نیز در نظر می گیرند. به منظور تقلید از نحوه ی تصمیم گیری معامله گران در امر سرمایه گذاری در سهام، الگوریتم قهرمانی در لیگ ورزشی مجهز به تیم هایی با ساختار شبکه ای به جهت استخراج قواعد چند مرتبه، توسعه داده شده است. قوانین چند مرتبه توسط الگوریتم استخراج می شوند که در آن هر قاعده علاوه بر اطلاعات روز جاری، حاوی اطلاعات روزهای گذشته نیز می باشد بنابراین یک حافظه به منظور ذخیره اطلاعات مفید در هر یک از قوانین ایجاد شده است. به منظور ارزیابی و بررسی عملکرد مدل ارائه شده از 20 سهم از شرکت ها در بخش های مختلف صنعتی بازار بورس تهران استفاده شده است. در شبیه سازی سرمایه گذاری، مدل ارائه شده سود بیشتر یا ضرر کمتری را نسبت به مدل خرید و نگهداری و مدل برنامه نویسی شبکه ژنتیک ایجاد کرده است.

چکیده انگلیسی:

Traders in stock market consider stock information in the past few days as well as the current day information when making decision about selling or buying stock. To imitate stock traders’ style of decision-making, in this article, League Championship Algorithm (LCA) equipped with teams which have network structure has been introduced to extract multi-order rules. Multi-order rules would be extracted by LCA which not only contain the current day information, but also information of the previous days. Thus, a memory to store useful information has been created for each rule. To evaluate the model, 20 shares of companies in different industrial parts of Tehran stock exchange are used. In the testing simulation, the proposed model shows higher profits or lower losses than the buy & hold and genetic network programming models.  

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