Corporate Default Prediction among Tehran Stock Exchange’s Selected Industries
Publish place: Iranian Journal of Finance، Vol: 2، Issue: 1
Publish Year: 1397
نوع سند: مقاله ژورنالی
زبان: English
View: 168
This Paper With 52 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJFIFSA-2-1_001
تاریخ نمایه سازی: 24 فروردین 1401
Abstract:
This study aims to present a model for predicting corporate default among Tehran Stock Exchange’s selected industries. To do this, corporate default drivers were identified and selected by referring to previous research findings and using experts’ opinions. These drivers were divided into five categories: accounting ratios, market variables, macroeconomic indicators, nonfinancial factors, and earnings quality measures. Structural equation modeling (SEM) technique was used to derive the prediction model. In this technique, corporate default drivers were used as latent independent variables, and their constituent factors were considered as observable indicators of the above variables. In addition, corporate default, as the latent dependent variable, was calculated by a measure based on the Black-Scholes-Merton (BSM) option pricing model. After implementing structural equation modeling (SEM) technique by use of Smart PLS software, a prediction model that contains influential drivers of corporate default was derived and presented for each of the selected industries.
Keywords:
Corporate Default , Accounting Ratios , Black-Scholes-Merton (BSM) Option Pricing Model , structural equation modeling , Tehran Stock Exchange
Authors
Jafar Babajani
Ph.D. in Accounting and Professor, Allameh Tabataba’i University
Mohammad Taghi Taghavi Fard
Ph.D. in Industrial Engineering and Associate Professor, Allameh Tabataba’i University
Maysam Ahmadvand
Ph.D. in Finance, Allameh Tabataba’i University
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :