سیستم استنتاج نوروفازی برای معاملات بسامدبالا با استفاده از مدل مشاهده درون-روزانه

نوع مقاله: کاربردی

نویسندگان

1 استادیار گروه مدیریت دانشگاه امام صادق (ع)، تهران، ایران

2 دانشجوی دکتری مدیریت مالی، دانشگاه شهید بهشتی، تهران، ایران

چکیده

هدف اصلی این مقاله پیش‌بینی سری زمانی قیمت سکه برای انجام معاملات بسامدبالا است. بدین منظور، نوعی شبکه عصبی-فازی معرفی می‌شود که به کمک استدلال فازی و ترکیب آن با قابلیت شناسایی الگوی شبکه‌های عصبی پیش‌بینی‌هایی ارائه می‌دهد و بر مبنای پیش‌بینی‌ها، معاملات بسامدبالا انجام می‌شود. برای پالایش ورودی‌های غیرضروری در فرایند آموزش، نوعی مدل نوسان مبتنی بر رخداد، پیشنهاد و به انفیس متصل شده است. مدل‌های پژوهش با داده‌های قیمت سکه در بازه زمانی خرداد 1393 تا اردیبهشت 1395 آزمون شد. نتایج آزمون‌ها نشان می‌دهد بر اساس معیارهای نرخ موفقیت، عامل سود، نسبت بازدهی و نسبت شارپ، مدل نوسان‌پذیری مبتنی بر رخداد موجب می‌شود سیستم انفیس، ورودی‌های مناسب‌تری دریافت کند. همچنین، ترکیب انفیس با مدل نوسان مبتنی بر رخداد، برای پیش‌بینی و انجام معاملات بسامدبالا منجر به کسب نرخ موفقیت 69 درصدی می‌شود.
JEL: C32, C45, G15, G24
نحوه استناد به این مقاله : صالح آبادی، ع.، و فرازمند، س. (1396). سیستم استنتاج نوروفازی برای معاملات بسامدبالا با استفاده از مدل مشاهده درون-روزانه. فصلنامه مدلسازی ریسک و مهندسی مالی، 2(1)، 80 - 97.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

An ANFIS System for High Frequency Trading using Intraday Seasonality Observation Model

نویسندگان [English]

  • Ali Saleh Abadi 1
  • sajjad Farazmand 2
1 Assistant Prof., Finance Department, Imam Sadiq University, Tehran, Iran
2 Ph.D. Student. Financial Management, Shahid Beheshti University, Tehran, Iran
چکیده [English]

The main goal of this article is prediction of gold price time series. An ANFIS and its fuzzy reasoning combined with pattern recognition ability of NN is used for predicting and trading in high frequency. Taking volatility of financial time series into consideration has brought about the development of the Intraday Seasonality Observation Model. Models are examined with data from June 2014 to April 2016. This model allows us to observe specific events and seasonality’s in data and subsequently removes any unnecessary data. This volatility based model provides the ANFIS with more accurate inputs. Based profit factor, ROI, and Sharpe ratio the model has increased the overall performance of the system so that winning rate of the system reaches 69 percent.
JEL: C32, C45, G15, G24
How to cite this paper: Saleh Abadi, A., & Farazmand, S. (2017). An ANFIS system for High Frequency Trading using Intraday Seasonality Observation Model. Quarterly Journal of Risk Modeling and Financial Engineering, 2(1), 80 –97. (In Persian)

کلیدواژه‌ها [English]

  • ANFIS
  • Neuro-fuzzy network
  • Volatility Model
  • High-frequency trading (HFT)

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