بررسی دقت ماشین بردار پشتیبان بر پایه الگوریتم ژنتیک نسبت به روش‌های ‌متداول خطی در پیش‌بینی سود هر سهم

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار حسابداری، دانشگاه آزاد اسلامی، واحد کرمان، کرمان، ایران

چکیده

اطلاعات مربوط به سود و سود پیش‌بینی شده هر سهم معیارهایی هستند که از دیدگاه بسیاری از استفاده‌کنندگان با اهمیت تلقی می‌شوند؛ لذا شرکت‌ها برای جذب سرمایه‌گذاران تلاش می‌کنند سود هر سهم را با بیشترین دقت پیش‌بینی کنند. از سوی دیگر، علیرغم روش‌های متعدد پیش‌بینی سود، پیش‌بینی دقیق سود هر سهم در حوزه مالی کار چندان آسانی نیست و اغلب پژوهشگران درصدد تعیین بهترین روش برای پیش‌بینی سود هستند؛ بنابراین، هدف اصلی این پژوهش بررسی دقت ماشین بردار پشتیبان بر پایه الگوریتم ژنتیک نسبت به روش‌های ‌متداول خطی در پیش‌بینی سود هر سهم است. بدین منظور، نمونه‌ای متشکل از 100 شرکت پذیرفته شده در بورس اوراق بهادار تهران طی سال‌های 1387-1398 بررسی شده‌ است. در راستای دستیابی به اهداف پژوهش، ابتدا با مطالعه پژوهش‌های پیشین در حوزه پیش‌بینی سود 14 نسبت مالی اثرگذار بر پیش‌بینی سود انتخاب شده است. سپس، به منظور ارائه مدلی در زمینه پیش‌بینی سودآوری شرکت‌ها، به مقایسه مدل ترکیبی ماشین بردار پشتیبان بر پایه الگوریتم ژنتیک، ماشین بردار پشتیبان و رگرسیون خطی پرداخته شده است. نتایج پژوهش نشان داد مدل ترکیبی ماشین بردار پشتیبان بر پایه الگوریتم ژنتیک در پیش‌بینی روند حرکتی سود هر سهم بسیار بهتر عمل کرده و در مقایسه با مدل ماشین بردار پشتیبان بر اساس توابع کرنلی و روش رگرسیون خطی از دقت بالاتری برخوردار است. به گونه‌ای که با توسعه مدل ماشین بردار پشتیبان بر پایة الگوریتم ژنتیک خطای آموزش مدل به مقدار 036/0 کاهش و بر دقت مدل تا 75 درصد افزوده می‌شود.
 

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Evaluation of the Accuracy of Support Vector Machine based on Genetic Algorithm Compared to Common Linear Methods in Forecasting Earnings Per Share

نویسنده [English]

  • Sedighe Azizi
Assistant Professor of Accounting, Islamic Azad University, Kerman Branch, Kerman, Iran
چکیده [English]

Earnings and earnings per share information are metrics that are considered important by many users; Therefore, companies try to attract investors with the most accurate forecast of earnings per share. On the other hand, despite the various methods of forecasting earnings, accurate forecasting of earnings per share in the financial field is not easy and most researchers are trying to determine the best way to forecast earnings; Therefore, the main purpose of this study is to investigate the accuracy of support vector machine based on genetic algorithm compared to common linear methods in forecasting earnings per share. For this purpose, samples consisting of 100 companies listed on the Tehran Stock Exchange during the years 2008-2019 have been studied. In order to achieve the objectives of the research, first by studying previous research in the field of earnings forecasting, 14 financial ratios affecting earnings forecasting have been selected. Then, in order to provide a model for predicting the profitability of companies, a combined model of support vector machine based on genetic algorithm, support vector machine and linear regression is compared. The results showed that the hybrid model of support vector based on genetic algorithm is much better in predicting the trend of earnings per share and has a higher accuracy compared to the model of support vector based on kernel functions and linear regression method. Thus, with the development of the support vector machine model based on the genetic algorithm, the model training error is reduced to 0.036 and the accuracy of the model is increased up to 75%.

کلیدواژه‌ها [English]

  • Earnings Per Share
  • Support Vector Machine
  • Genetic Algorithm
  • Linear Models
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