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عنوان فارسی مقاله: رگرسیون lasso و ridge لجستیک در پیش بینی شکست شرکت
عنوان انگلیسی مقاله: The logistic lasso and ridge regression in predicting corporate failure
مجله/کنفرانس: پروسیدیای مالی و اقتصاد – Procedia Economics and Finance
رشته های تحصیلی مرتبط: مدیریت
گرایش های تحصیلی مرتبط: مدیریت کسب و کار، مدیریت مالی
کلمات کلیدی فارسی: ورشکستگی شرکت، مدل های پیش بینی، حداقل عملکرد کوچک سازی و انتخاب مطلق (Lasso)، رگرسیون ستیغی (Ridge)
کلمات کلیدی انگلیسی: Corporate Bankruptcy; Prediction Models; Lasso; Ridge Regression
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/S2212-5671(16)30310-0
دانشگاه: IPCA – Polytechnic Institute of Cavado and Ave, Campus do IPCA, 4750-810 Barcelos, Portugal
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2016
شناسه ISSN: 2212-5671
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 8
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13812
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1-Introduction


2-The Ridge and Lasso logistic regression


3-Methodology


4-Results


5-Comments


References

نمونه متن انگلیسی مقاله

Abstract


The prediction of corporate bankruptcy is a phenomenon of interest to investors, creditors, borrowing firms, and governments alike. Many quantitative methods and distinct variable selection techniques have been employed to develop empirical models for predicting corporate bankruptcy. For the present study the lasso and ridge approaches were undertaken, since they deal well with multicolinearity and display the ideal properties to minimize the numerical instability that may occur due to overfitting. The models were employed to a dataset of 2032 non-bankrupt firms and 401 bankrupt firms belonging to the hospitality industry, over the period 2010-2012. The results showed that the lasso and ridge models tend to favor the category of the dependent variable that appears with heavier weight in the training set, when compared to the stepwise methods implemented in SPSS.


Introduction


There are several undesirable consequences of business failures. Its economic and social cost can be significant. So, it is quite natural that this issue has occupied a significant part of researcher’s agenda. In spite of recent growing interest on non-financial attributes in explaining business failures, traditionally investigation on this issue has been focused on financial attributes. In most of the works statistical or artificial intelligence techniques were applied to the accountancy data of the companies, aiming at obtaining prediction models that would indicate whether the company would or would not reach a bankruptcy situation in the future (Beaver, 1966; Altman, 1968; Martin, 1977; Tam and Kiang, 1992). In a study on corporate bankruptcy prediction, one of the aspects we immediately need to clarify is the concept of bankruptcy we shall use. In specialized literature the term has been used in different ways by different authors: legal bankruptcy, insolvency, inability to do payments or continued losses. As we lack a general theory on corporate bankruptcy, there is also no unique definition for this concept. This is an important limitation, since the sample’s selection, both in terms of firms that have and have not “bankrupt”, depends on the definition of corporate bankruptcy used.

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