Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Predicting financial distress of Turkish non-financial firms: evidence from micro data
Download
index.pdf
Date
2019
Author
Yılmaz, Muhammed Hasan
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
303
views
205
downloads
Cite This
In this thesis, determinants of financial distress probabilities are analyzed for the period over 2006-2016 by utilizing firm-level and loan-level data sets. Based on the financial distress definition constructed from the existence of non-performing loans, univariate tests indicate that financially problematic Turkish corporates have lower liquidity, profitability and asset turnover, while they are also the ones with inferior short and long-term debt paying ability. To form multivariate specifications, variable selection techniques such as univariate logit, principal components analysis and bootstrap stepwise logit estimations are applied on a broad set of financial ratios. Multivariate logit and panel logit models indicate that stronger liquidity buffers, abundant profits and improved asset turnover decrease the probability of financial distress. On the other hand, rising interest expenses and considerable debt burden increase the likelihood of facing financial problems. The informative nature of multivariate models are evaluated for in-sample predictions and out-of-sample forecasts. Survival analysis is also performed to assess the time to financial distress. It is found that firm size, profitability, liquidity and operational performance lengthen the period until the occurence of financial problems, while leverage significantly shortens it.
Subject Keywords
Corporations.
,
Financial Distress
,
Logit Model
,
Variable Selection
,
Survival Analysis.
URI
http://etd.lib.metu.edu.tr/upload/12624635/index.pdf
https://hdl.handle.net/11511/44438
Collections
Graduate School of Social Sciences, Thesis
Suggestions
OpenMETU
Core
Modeling the CDS Prices: An application to the MENA Region
Namuslu, Merve; Ayaydın Hacıömeroğlu, Hande; Danışoğlu, Seza (2017-01-05)
The objective of this paper is to study the pricing of a single-name credit default swap (CDS) contract via the discounted cash flow method with reduced-form survival probability functions depending on stochastic intensity. The ability of the model in predicting the market-observed spreads is tested as well by using bond and CDS data of MENA countries. In credit risk modeling, the CIR (Cox-Ingersoll-Ross) model is used. The main reason for using a reduced-form model in pricing the CDS contracts is the advan...
Industry specific information content of financial ratios and financial distress modeling
Sayarı, Naz; Oran, Adil; Muğan, Fatma Naciye Can; Department of Business Administration (2013)
The aim of this study is to investigate uncertainty levels of industries and explore those financial ratios that have the highest information content in determining the set of industry characteristics and use the most informative ratios selected in developing industry specific financial distress models. First, we employ factor analysis to determine the set of ratios that are most informative in specified industries. Second, we use entropy method as a Multiple Attribute Decision Making Model, to measure the ...
Expected Value of Partial Perfect Information in Hybrid Models Using Dynamic Discretization
Yet, Barbaros; Fenton, Norman; Neil, Martin (2018-01-01)
In decision theory models, expected value of partial perfect information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This...
Overconfidence and bubbles in experimental asset markets
Şahin, Serkan; Küçükkaya, Halit Engin; Yılmaz, Özlem; Department of Business Administration (2013)
The aim of this study is to investigate uncertainty levels of industries and explore those financial ratios that have the highest information content in determining the set of industry characteristics and use the most informative ratios selected in developing industry specific financial distress models. First, we employ factor analysis to determine the set of ratios that are most informative in specified industries. Second, we use entropy method as a Multiple Attribute Decision Making Model, to measure the ...
Robust estimation in multivariate heteroscedastic regression models with autoregressive covariance structures using EM algorithm
GÜNEY, YEŞİM; ARSLAN, OLÇAY; Gökalp Yavuz, Fulya (2022-09-01)
© 2022 Elsevier Inc.In the analysis of repeated or clustered measurements, it is crucial to determine the dynamics that affect the mean, variance, and correlations of the data, which will be possible using appropriate models. One of these models is the joint mean–covariance model, which is a multivariate heteroscedastic regression model with autoregressive covariance structures. In these models, parameter estimation is usually carried on under normality assumption, but the resulting estimators will be very ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
M. H. Yılmaz, “Predicting financial distress of Turkish non-financial firms: evidence from micro data,” Thesis (M.S.) -- Graduate School of Social Sciences. Economics., Middle East Technical University, 2019.