پیش‌بینی قیمت سهام با ارائه مدلی ترکیبی با استفاده از تحلیل مؤلفه‌های اصلی و تئوری مجموعه‌های راف

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه حسابداری و مدیریت مالی، دانشکده مدیریت و حسابداری، پردیس فارابی دانشگاه تهران، قم، ایران

2 استاد، گروه مدیریت صنعتی، دانشکده مدیریت و حسابداری، دانشگاه تربیت مدرس، تهران، ایران

3 دانشجوی دکتری مالی گرایش بانکداری، دانشکده مدیریت و حسابداری، دانشکدگان فارابی، دانشگاه تهران، قم، ایران

چکیده

در این پژوهش، با ترکیب روش‌های تحلیل مؤلفه‌های اصلی و مجموعه‌های راف، مدلی به منظور پیش‌بینی قیمت سهام ارائه شده است. به این منظور، با استفاده از داده‌های قیمتی شرکت ایران خودرو، ابتدا تعدادی از شاخص‌های تکنیکال محاسبه شدند. به منظور کاهش بعد ماتریس تصمیم، به روش تحلیل مؤلفه‌های اصلی، متغیرهایی جدید به گونه‌ای انتخاب شدند که حداکثر ویژگی‌های داده‌های اولیه حفظ شود. از این متغیرها در ماتریس تصمیم، به عنوان مؤلفه‌های شرطی استفاده می‌شود و متغیر تصمیم، نوسان قیمت سهم در روز بعد می‌باشد. داده‌ها به روش‌های مختلف گسسته‌سازی و به دو دسته یادگیری و کنترل تقسیم شدند. سپس با استفاده از تئوری مجموعه‌های راف بر روی داده‌های یادگیری، قواعد تصمیم استخراج و اعتبار آنها بر روی داده‌های کنترل، ارزیابی شد. نتایج به دست آمده از مدل ترکیبی با نتایج حاصل از مدل مجموعه‌های راف مقایسه شد. با بررسی ضرایب متغیرهای اولیه در عامل‌های جدید این موضوع مهم قابل نتیجه‌گیری است که با حفظ بخش عمده خواص داده‌های اولیه، پنج متغیر اولیه قابل کاهش به دو عامل جدید بوده و قابلیت نام‌گذاری دارند که این موضوع نقش مهمی در کاهش تعداد قواعد تصمیم و ملموس بودن استفاده از آنها دارد. درصد پیش‌بینی‌های صحیح قواعد استخراج شده از مدل ترکیبی نسبت به مدل رقیب یعنی مدل مجموعه‌های راف، بیشتر و تعداد قواعد کمتر می‌باشد. به منظور بررسی استحکام مدل، داده‌های بازه سال‌های 1398-1383 شرکت ایران خودرو و همچنین داده‌های سالیانه بانک صادرات ایران مورد بررسی قرار گرفتند که نتایج با یافته‌های قبلی مطابقت داشتند.

کلیدواژه‌ها


عنوان مقاله [English]

Stock price forecasting by presenting a hybrid model using principal component analysis and rough set theory

نویسندگان [English]

  • Mohammadreza Mehrabanpour 1
  • Adel Azar 2
  • Majid Shahrami Babkan 3
1 Assistant Professor, Department of Accounting and Financial Management, School of Management and Accounting, Farabi Campus, University of Tehran, Qom, Iran
2 Professor, Department of Industrial Management, Faculty of Management and Accounting, Tarbiat Modares University, Tehran, Iran
3 Ph.D student in finance majoring in banking, Faculty of Management and Accounting, Farabi School, University of Tehran, Qom, Iran
چکیده [English]

In this research, by combining the methods of principal component analysis and the Rough sets, a model is proposed to predict stock prices. First, a number of technical indicators were calculated using the one-year price data of IranKhodro Company. In order to reduce the decision matrix dimension, using the principal component analysis method, new variables were selected so that the maximum characteristics of the initial data were maintained. These variables are used as conditional components in the decision matrix, and the decision variable is next day stock price fluctuation. The data were converted into discrete intervals by different methods and then divided into two groups of learning and control. Then, using the theory of Rough sets on learning data, the decision rules were extracted and their validity on the control data was evaluated. The results obtained from the combined model were compared with the results of the Rough sets model. The advantage of the Principal Components Analysis and Exploratory Factor Analysis methods is the ability to name new factors as the factor of the momentum and the moving average factor, which makes the results more tangible. The percentage of correct predictions of the rules extracted from the hybrid model is higher than the alternative model and the number of rules is lower. In order to verify the reliability of the model, the data of the period of 2002-2017 of IranKhodro Company and also the data of the Iran Saderat bank were studied. The results were consistent with the previous findings.

کلیدواژه‌ها [English]

  • stock price forecasting
  • Principal components analysis
  • decision rules
  • Rough sets
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