الگوریتم پیشنهادی برای انتخاب ویژگی‌های مناسب به‌منظور پیش‌بینی شاخص بورس اوراق بهادار تهران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مدیریت مالی، دانشگاه شهید یهشتی، تهران، ایران.

2 دانشیار، گروه مدیریت مالی و بیمه، دانشگاه شهید یهشتی، تهران، ایران

3 استاد، گروه مدیریت بازرگانی، دانشگاه شهید بهشتی، تهران،ایران.

چکیده

عملکرد یک مدل هوشمند تا حد زیادی به انتخاب مرتبط­ترین و تأثیرگذارترین متغیرهای ورودی و کمترین پیچیدگی مدل یادگیری بستگی دارد. از این­رو در مطالعه حاضر، برای پیش­بینی روزانه شاخص کل بورس اوراق بهادار تهران بر اساس متغیرهای مالی و اقتصادی، ابتدا اقدام به اولویت­بندی ویژگی­ها با الگوریتم MID نموده، سپس از 4 مدل مختلف شبکه عصبی (MLP, SVR, RBF, DNN) که از مهم­ترین و بدیع­ترین مدل­های پیش­بینی می­باشند، استفاده می­شود. با توجه به نتایج بدست آمده از تحلیل مدل­های مورد بررسی، در نهایت الگوریتمی برای انتخاب ویژگی­های مناسب برای پیش­بینی شاخص، تحت عنوانISF­_MID پیشنهاد شده و با تعدادی از روش­های مشابه، مقایسه می­گردد. داده­های مورد استفاده در این پژوهش به صورت روزانه در بازه زمانی 28/10/1392 تا 30/5/1397 جمع­آوری شده­اند. مدل­های مورد بررسی در مرحله پیاده­سازی با روش اعتبارسنجی متقابل K-fold مورد ارزیابی قرار گرفتند. همچنین از معیارهای MAE، MSE و RMSE برای ازریابی عملکرد مدل­های مذکور استفاده می­شود. نتایج نشان می­دهد که با روش­ پیشنهادی، می­توان با 7 ویژگی انتخابی به دقت بالایی در پیش­بینی روزانه شاخص کل بورس اوراق بهادار تهران دست­یافت.

کلیدواژه‌ها

عنوان مقاله [English]

The Proposed Algorithm to Select Appropriate Features for Predicting Tehran Stock Exchange Index

نویسندگان [English]

  • Somayeh Mohebbi 1
  • Mohamad Esmaeil Fadaeinejad 2
  • Mohammad reza Hamidizadeh 3
1 Ph.D. Candidate in Financial Management, Shahid Beheshti University, Tehran, Iran.
2 Associate Prof., Department of Financial Management and Insurance, Shahid Beheshti University, Tehran, Iran.
3 Professor, Department of Business Management, Shahid Beheshti University, Tehran, Iran.
چکیده [English]

The performance of an intelligent model largely depends on the selection of the most relevant and most influential input variables and the lowest complexity of the learning model. Therefore, in the present study, to predict the index of Tehran Stock Exchange based on financial and economic variables, first prioritize featuresWith MID algorithm, then 4 different neural network models (MLP, SVR, RBF, DNN) are used, which are the most important and innovative prediction models. According to the results of the analysis of the studied models, an algorithm is proposed to select the appropriate features on the index, as ISF-MID, and are compared with several similar methods. The data used in this study were collected daily in the period of 18/01/2014 to 21/08/2018. Evaluation of the models was performed by K-fold cross validation method. The MAE, MSE, and RMSE criteria are also used to evaluate the performanceof the mentionedmodels. The results show that with the proposed method, with 7 selected features, it is possible to achieve high accuracy in predicting the daily index of the Tehran Stock Exchange.

کلیدواژه‌ها [English]

  • : Stock Index Prediction
  • Feature Selection Algorithm
  • Radial Basis Function
  • Support Vector Regression
  • Deep Neural Network

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چشم انداز مدیریت مالی
دوره 11، شماره 34
شهریور 1400
صفحه 35-67

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