بررسی سودمندی انتخاب متغیرهای پیش‌بین در پیش‌بینی نوع اظهارنظر حسابرسان

نوع مقاله: مقاله علمی پژوهشی

نویسندگان

1 استاد حسابداری، دانشگاه شیراز، شیراز، ایران

2 دکتری حسابداری، دانشگاه شیراز، شیراز، ایران

3 دانشجوی دکتری حسابداری، دانشگاه شیراز، شیراز، ایران

4 کارشناس‎ارشد حسابداری، دانشگاه شیراز، شیراز، ایران

چکیده

در اغلب پژوهش‌های انجام‎شده، متغیرهای پیش‌بین بدون ضابطه و فقط براساس مطالعات گذشته انتخاب شده‎اند. فرایند انتخاب متغیرها را می‌توان به‎عنوان مرحلۀ پیش‌پردازش برای حذف متغیرهای نامربوط و اضافه و انتخاب متغیرهای بهینه قبل از ایجاد مدل دانست. در این رابطه، پژوهش حاضر به بررسی سودمندی روش­ انتخاب متغیر مبتنی بر همبستگی برای پیش‎بینی نوع اظهارنظر حسابرسان شرکت­های پذیرفته‎شده در بورس اوراق بهادار تهران می‎پردازد. طبقه­بندی­کننده­های این پژوهش، شبکه‌های عصبی مصنوعی و رگرسیون لجستیک است. به‎طور کلی، یافته­های تجربی مربوط به بررسی 1214 مشاهده (سال ـ شرکت) در بازۀ زمانی 1386 تا 1393 نشان‌ داد سودمندی استفاده از متغیرهای منتخب روش­ انتخاب متغیر همبستگی، در عملکرد پیش­بینی نوع اظهارنظر حسابرسان است. به بیان دیگر، در صورت استفاده از متغیرهای منتخب این روش نسبت به استفاده از کلیۀ متغیرهای اولیه­، میانگین دقت افزایش و خطای نوع اول و دوم کاهش خواهد یافت. افزون‎بر این، یافته­های پژوهش حاکی از عملکرد مناسب و بهتر شبکه‌های عصبی نسبت به رگرسیون لجستیک است.

کلیدواژه‌ها


عنوان مقاله [English]

The Usefulness of Feature Selection in Auditors Opinion Type Prediction

نویسندگان [English]

  • Mohammad Setayesh 1
  • Mostafa kazem Nejad 2
  • Gholam Reza Rezaei 3
  • Ali asghar Dehghani 4
چکیده [English]

Abstract: Despite the importance of predictive variable in prediction, in most of the research in the field of auditors’ opinion the purpose was rendering the suitable models. Meanwhile, less attention was paid to the selection of optimal predictive variable and appropriate models of these selection. Therefore, in most of these research the predictive variables were chosen randomly and according to the prior research. The process of selecting variables could be used as a preprocess for omitting irrelevant variables and selecting optimal variables before creating the model. In this regard, this study investigates the usefulness of Correlation-Based Features Selection (CFS) in auditors’ opinion prediction of listed companies in Tehran Stock Exchange. The classifiers including Artificial Neural Networks (ANN) and logistic regression were used. In overall, the experimental results of investigating 1214 firms-years from 2008 to 2015, confirmed the usefulness of CFS Method in predicting auditors' opinion. In other words, the application of the CFS method, increases the mean of accuracy in comparison with using all variables, and reduces the occurrence of type I and type II errors. Furthermore, the results indicated that ANN outperforms the logistic regression.

کلیدواژه‌ها [English]

  • Correlation-Based Features Selection (CFS) method
  • Auditors Opinion Type
  • Artificial Neural Networks
  • Logistic regression
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