پیش بینی شاخص کل بورس اوراق بهادار تهران با استفاده از رگرسیون بردار پشتیبان بر مبنای تکنیک کاهش ابعاد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری رشته مدیریت مالی، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی تهران، ایران.

2 دانشیار گروه مدیریت مالی و بیمه، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی تهران، ایران.

3 استادیار گروه مدیریت مالی و بیمه، دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی، تهران، ایران.

چکیده

    بازارهای سهام نقش مهمی در سازماندهی سیستم­های اقتصادی مدرن دارند. پژوهش­های گسترده­ ای در زمینه پیش ­بینی آن­ها با استفاده از تکنیک­های هوشمند انجام شده است. با توجه به این­که دقت عملکرد این تکنیک­ها به میزان قابل توجهی تحت تأثیر ویژگی­های ورودی آن است، یکی از پیشرفت­های به­ کار رفته در استفاده از مدل­های هوشمند، علاوه­ بر کاربرد مدل­های ترکیبی، استفاده از کاهش ابعاد به ­عنوان یک پیش ­مرحله برای مدل پیش­بینی می­باشد. در این پژوهش برای پیش­بینی روزانه شاخص کل بورس اوراق بهادار تهران همزمان از دو روش کاهش ابعاد (انتخاب و استخراج) به منظور انتخاب ویژگی­های مناسب به­ عنوان ورودی­های مدل استفاده می­شود. به­طوری­که برای انتخاب ویژگی­ها از الگوریتم mRMR-MID و برای استخراج ویژگی­ها از الگوریتم PCA استفاده می­شود. سپس از رگرسیون بردار پشتیبان به ­عنوان مدل پیش­بینی استفاده می­شود. با توجه به نتایج بدست آمده از تحلیل استفاده از تکنیک­های کاهش ابعاد در مدل­ پیش­بینی، در نهایت الگوریتمی برای انتخاب ویژگی­های مناسب بر شاخص، تحت عنوانISF­_MID پیشنهاد 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 با درصد خطا 46/3 دست­یافت. لازم به ذکر است مدل­های مورد بررسی در مرحله پیاده­ سازی با روش اعتبارسنجی متقابل k-fold مورد ارزیابی قرار گرفتند. همچنین از معیارهای MAE، MSE و RMSE برای ازریابی عملکرد مدل­های مذکور استفاده می­شود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting the Tehran Stock Exchange Index Using Support Vector Regression; Based on the Dimension Reduction Technique

نویسندگان [English]

  • Somayeh Mohebi 1
  • Mohammadesmaeel Fadaeinezhad 2
  • Mohamad Osoolian 3
1 Ph.D. Candidate of Financial Management and Insurance, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
2 Associate Prof., Department of Financial Management and Insurance, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran
3 Assistant Prof., Department of Financial Management and Insurance, Faculty of Management & Accounting, Shahid Beheshti University, Tehran, Iran
چکیده [English]

Stock markets play a significant role in organizing modern economic systems. Several research projects have been performed in the field of prediction using intelligent techniques. Considering that the accuracy of these techniques is significantly affected by their input features, one of the improvements made in the use of intelligent models, in addition to the application of hybrid models, is the use of dimensionality reduction as a preprocessing for the prediction model. In this study, in order to predict the daily index of the Tehran Stock Exchange, two methods of dimensional reduction (selection and extraction) are used simultaneously to select appropriate features as model inputs. Hence, the MRMR-MID algorithm is used to select features and the PCA algorithm is used to extract features. Then, support vector regression is used as a prediction model. Finally, an algorithm for selecting suitable features is proposed as ISF_ MID, according to the results obtained from the analysis of the use of dimensional reduction techniques in the prediction model. 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. It should be noted that the studied models were evaluated in the implementation stage by the k-fold cross-validation method. MAE, MSE, and RMSE criteria are also used to evaluate the performance of these models.

کلیدواژه‌ها [English]

  • Stock Index Prediction"
  • Support Vector Regression"
  • Dimension Reduction Technique"
  • "
  • Feature selection"

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  • تاریخ دریافت: 10 فروردین 1400
  • تاریخ بازنگری: 22 تیر 1400
  • تاریخ پذیرش: 19 شهریور 1401

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