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Firma Başarısızlığının Dinamiklerinin Belirlenmesinde Makina Öğrenmesi Teknikleri: Ampirik Uygulamalar ve Karşılaştırmalı Analiz
[Machine Learning Techniques in Determining the Dynamics of Corporate Financial Distress: An Empirical Treatment and a Comparative Analysis of Financial and Non-Financial Micro Data of the Turkish Private Sector]

Author

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  • Cakir, Murat

Abstract

Recent financial crises and especially large corporate bankruptcies, have led bank managements and financial authorities to follow and monitor both financial and real sector risks, and to focus on firm failures. Bank of International Settlements, has therefore, taken the decision to include the necessity for banks to employ internal rating systems among BASEL II criteria. Thus, risk assessment and internal rating systems criteria would be made operational by the individual European Union banking systems, by the end of 2007, and January 2008 in Turkey, at the latest. Financial and operational information of the firms, makes up the input to the risk analysis. This information can be aggregated to portray the sectoral trends, and/or focused upon on a firm basis to understand firms’ financial behaviours. Finance theory summarizes firms’ risks under financial distress and firm failure. There have been a myriad of works under these two headings, particularly in the United States, after the Great Depression. While early studies have focused upon the differences in the financial ratios of financially sound and failed firms, especially with the advances in computing capacity, the last two decades have witnessed an increasing use of machine learning methods in the failure prediction. Therefore, machine learning methods can be considered as having great potential in failure prediction and being good candidates as decision aids for policy-making. This study considers financial distress and firm failure on theoretical grounds, gives a compact but elaborate explanation of machine learning schemes, and analyzes the results of these schemes run with data obtained from the database of Real Sector Data Division of the Central Bank. Cost sensitive learning was given special attention in the analysis.

Suggested Citation

  • Cakir, Murat, 2005. "Firma Başarısızlığının Dinamiklerinin Belirlenmesinde Makina Öğrenmesi Teknikleri: Ampirik Uygulamalar ve Karşılaştırmalı Analiz [Machine Learning Techniques in Determining the Dynamics of Corporat," MPRA Paper 55975, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:55975
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    References listed on IDEAS

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    Cited by:

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    More about this item

    Keywords

    Financial Distress; Firm Failure; Machine Learning; Classification and Cost Sensitive Learning;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G01 - Financial Economics - - General - - - Financial Crises
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G39 - Financial Economics - - Corporate Finance and Governance - - - Other
    • M19 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Other
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M49 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Other
    • Y40 - Miscellaneous Categories - - Dissertations - - - Dissertations

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