Identification of Indicators’ Applicability to Settle Borrowers’ Probability of Default

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Borrowers default risk is one of the most relevant types of risk in commercial banking and its assessment is important to secure business profitability and avoid huge losses during economic turbulences. This leads to necessity to investigate topics related to assessment of borrowers’ default probability and applicability of factors, which would enable to capture the newest trends of borrowers’ markets. Leading economic indicators (in addition to financial and other economic indicators) are often suggested as forward-looking in scientific literature. However, there is still a discussion going on applicability of financial ratios and economic indicators. As the problem is relevant in theoretical view as well as for practitioners, this article aims to identify applicability of leading economic indicators for the estimation of default probability. Further, the qualitative criteria for factor selection were identified and used when using detailing, grouping and SWOT analysis methods. Based on current scientific literature analysis, this paper concludes that although leading economic indicators are able to capture forward-looking signals, they should be used with careful analysis of its drawbacks and in combination with financial factors in order to avoid overshooting effects. The limitation of the article is the analysis of factors based on rather theoretical analysis than estimation of quantitative criteria. This suggests that every time using leading economic indicators requires using empirical study of particular indicators’ set.

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  • Albrice D. (2014). Opinion Article on Leading Indicators. Asset Insights Database. [Accessed 10.02.2016]. Available from Internet:

  • Brett W. Brown T. & Onsman A. (2012). Exploratory Factor Analysis: A Five-step Guide for Novices. Australasian Journal of Paramedicine. [Accessed 10.02.2016]. Available from Internet:

  • Carling K. Jacobson T. Lindé K. & Roszbach K. (2002). Capital Charges under Basel II: Corporate Credit Risk Modelling and the Macro Economy. Sveriges Riksbank Working paper Series (142) 1-54.

  • Chan-Lau J. A. (2006). Market-Based Estimation of Default Probabilities and Its Application to Financial Market Surveillance. IMF Working Paper. Monetary and Financial Systems Department. [Accessed 10.02.2016]. Available from Internet:

  • Cibulskienė D. Butkus M. & Žakarė S. (2014). Bankroto Diagnostikos Modelis ir jo Pritaikymas Bankroto Tikimybei Lietuvos Įmonėse Prognozuoti. Taikomoji Ekonomika: Sisteminiai Tyrimai. [Accessed 10.02.2016]. Available from Internet:

  • Comptroller of the Currency Administrator of National Banks (2015). Rating Credit Risk. Comptroller’s Handbook the U.S. department of treasury. [Accessed 10.02.2016]. Available from Internet:

  • Derbali A. & Hallara S. (2012). The Current Models of Credit Portfolio Management: A Comparative Theoretical Analysis. International Journal of Management and Business (4) 271-292.

  • Dzidzevičiūtė L. (2010). Statistinių Vertinimo Balais Modelių Kūrimo ir Taikymo Ypatumai. Pinigų Studijos. [Accessed 10.02.2016]. Available from Internet:

  • European Banking Authority (2015). Guidelines on the Application of the Definition of Default under Article 178 of Regulation (EU) 575/2013. EBA Guidelines. [Accessed 10.02.2016]. Available from Internet:

  • Evans J. R. & Lindner C. H. (2012). Business Analytics: The Next Frontier for Decision Sciences. Decision Line 43(2) 4-6.

  • Financial Accounting Standard Board (2012). International Convergence of Accounting Standards-Overview. [Accessed 10.02.2016]. Available from Internet:

  • Fisher I. (1930). The Theory of Interest. The Macmillan Company. Library of Economics and Liberty [Accessed 10.02.2016]. Available from Internet:

  • FitzPatrick P. J. (1932). A Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies. Journal of Accounting Research. (5) 598-605

  • Frankel J. A. & Saravelos G. (2011). Can Leading Indicators Assess Country Vulnerability? Evidence from the 2008-09 Global Financial Crisis. Harvard Kennedy School. NBER Working Paper. [Accessed 10.02.2016]. Available from Internet:

  • Fritsche U. & Stephan S. (2002). Leading Indicators of German Business Cycles - An Assessment of Properties. Journal of Economics and Statistics (222) 289-315.

  • Gervasio D. & Montani D. (2013). IFRS Subjectivity: the Other Side of the Coin. Universal Journal of Accounting and Finance. [Accessed 10.02.2016]. Available from Internet:

  • Henson R. K. & Roberts J. K. (2006). Use of Exploratory Factor Analysis in Published Research: Common Errors and Some Comment on Improved Practice. Educational and Psychological Measurement (66) 393-328.

  • Hillegeist S. Keating E. K. Cram D. P. & Lundstedt K. G. (2004). Assessing the Probability of Bankruptcy. Review of Accounting Studies (9) 5-34.

  • Hogarty K. Hines C. Kromrey J. Ferron J. & Mumford K. (2005). The Quality of Factor Solutions in Exploratory Factor Analysis: The Influence of Sample Size Communality and Overdetermination. Educational and Psychological Measurement (65) 202-26.

  • International Monetary Fund (2015). Ninth review of the IMF fund’s data standards initiatives. Statistics. [Accessed 10.02.2016]. Available from Internet:

  • Izani I. & Raflis C. A. O. (2004). The Lead-Lag Pattern of Leading Coincident and Lagging. Investment Management and Financial Innovations. [Accessed 10.02.2016]. Available from Internet:

  • Jimenez G. & Saurina J. (2006). Credit Cycles Credit Risk and Prudential Regulation. MPRA Papers. [Accessed 10.02.2016]. Available from Internet:

  • Kaplan R. S. (2010). Conceptual Foundations of the Balanced Scorecard. Harvard Business School Press. [Accessed 10.02.2016]. Available from Internet:

  • Kiff J. Kisser M. & Schumacher L. (2013). Monetary and Capital Markets Rating Through-the-Cycle: What does the Concept Imply for Rating Stability and Accuracy? IMF Working Papers. [Accessed 10.02.2016]. Available from Internet:

  • Mileris R. & Boguslauskas V. (2011). Credit Risk Estimation Model Development Process: Main Steps and Model Improvement. Engineering Economics (22) 126-133.

  • Mader A. Wupper H. & Boon M. (2007). The Construction of Verification Models for Embedded Systems. Technical Reports. [Accessed 10.02.2016]. Available from Internet:

  • Martis M. S. (2006). Validation of Simulation Based Models: A Theoretical Outlook. The Electronic Journal of Business Research Methods (4) 39 -46.

  • Nippala E. & Päivi J. (2012). Management of Construction: Research to Practice Leading indicators for Forecasting Civil Engineering Market Development. University of Tampere. [Accessed 10.02.2016]. Available from Internet:

  • Organization for Economic Co-operation and Development (2012). OECD System of Composite Leading Indicators. [Accessed 10.02.2016]. Available from Internet:

  • Pluto K. & Tasche D. (2005). Thinking Coherently. Risk (18) 72-79.

  • Standard & Poor's (2013). Request For Comment: Corporate Criteria. [Accessed 10.02.2016]. Available from Internet:

  • Špicas R. & Nekrošiūtė G. (2012). Įmonių Kredito Rizikos Vertinimo Modelių Taikymas Lietuvos Kredito Unijose. Ekonomika ir Vadyba: Aktualijos ir Perspektyvos (28) 120-132.

  • Theobald D. (2012). 29+ Evidences for Macroevolution: Scientific Proof Scientific Evidence and the Scientific Method. [Accessed 10.02.2016]. Available from Internet:

  • Vrieze S. I. (2012). Model Selection and Psychological Theory: a Discussion of the Differences Between the Akaike Information Criterion and the Bayesian Information Criterion. Psychological Methods (17) 228-243.

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