Machine learning for corporate failure prediction : an empirical study of South African companies

 

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dc.contributor.advisor Everingham, Geoff en_ZA
dc.contributor.advisor Greene, John en_ZA
dc.contributor.author Kornik, Saul en_ZA
dc.date.accessioned 2015-11-04T10:37:55Z
dc.date.available 2015-11-04T10:37:55Z
dc.date.issued 2004 en_ZA
dc.identifier.citation Kornik, S. 2004. Machine learning for corporate failure prediction : an empirical study of South African companies. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/14643
dc.description Includes bibliographical references (leaves 255-266). en_ZA
dc.description.abstract The research objective of this study was to construct an empirical model for the prediction of corporate failure in South Africa through the application of machine learning techniques using information generally available to investors. The study began with a thorough review of the corporate failure literature, breaking the process of prediction model construction into the following steps: * Defining corporate failure * Sample selection * Feature selection * Data pre-processing * Feature Subset Selection * Classifier construction * Model evaluation These steps were applied to the construction of a model, using a sample of failed companies that were listed on the JSE Securities Exchange between 1 January 1996 and 30 June 2003. A paired sample of non-failed companies was selected. Pairing was performed on the basis of year of failure, industry and asset size (total assets per the company financial statements excluding intangible assets). A minimum of two years and a maximum of three years of financial data were collated for each company. Such data was mainly sourced from BFA McGregor RAID Station, although the BFA McGregor Handbook and JSE Handbook were also consulted for certain data items. A total of 75 financial and non-financial ratios were calculated for each year of data collected for every company in the final sample. Two databases of ratios were created - one for all companies with at least two years of data and another for those companies with three years of data. Missing and undefined data items were rectified before all the ratios were normalised. The set of normalised values was then imported into MatLab Version 6 and input into a Population-Based Incremental Learning (PBIL) algorithm. PBIL was then used to identify those subsets of features that best separated the failed and non-failed data clusters for a one, two and three year forward forecast period. Thornton's Separability Index (SI) was used to evaluate the degree of separation achieved by each feature subset. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Machine Learning en_ZA
dc.subject.other Financial Prediction en_ZA
dc.title Machine learning for corporate failure prediction : an empirical study of South African companies en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Commerce en_ZA
dc.publisher.department College of Accounting en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MCom en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Kornik, S. (2004). <i>Machine learning for corporate failure prediction : an empirical study of South African companies</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,College of Accounting. Retrieved from http://hdl.handle.net/11427/14643 en_ZA
dc.identifier.chicagocitation Kornik, Saul. <i>"Machine learning for corporate failure prediction : an empirical study of South African companies."</i> Thesis., University of Cape Town ,Faculty of Commerce ,College of Accounting, 2004. http://hdl.handle.net/11427/14643 en_ZA
dc.identifier.vancouvercitation Kornik S. Machine learning for corporate failure prediction : an empirical study of South African companies. [Thesis]. University of Cape Town ,Faculty of Commerce ,College of Accounting, 2004 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/14643 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Kornik, Saul AB - The research objective of this study was to construct an empirical model for the prediction of corporate failure in South Africa through the application of machine learning techniques using information generally available to investors. The study began with a thorough review of the corporate failure literature, breaking the process of prediction model construction into the following steps: * Defining corporate failure * Sample selection * Feature selection * Data pre-processing * Feature Subset Selection * Classifier construction * Model evaluation These steps were applied to the construction of a model, using a sample of failed companies that were listed on the JSE Securities Exchange between 1 January 1996 and 30 June 2003. A paired sample of non-failed companies was selected. Pairing was performed on the basis of year of failure, industry and asset size (total assets per the company financial statements excluding intangible assets). A minimum of two years and a maximum of three years of financial data were collated for each company. Such data was mainly sourced from BFA McGregor RAID Station, although the BFA McGregor Handbook and JSE Handbook were also consulted for certain data items. A total of 75 financial and non-financial ratios were calculated for each year of data collected for every company in the final sample. Two databases of ratios were created - one for all companies with at least two years of data and another for those companies with three years of data. Missing and undefined data items were rectified before all the ratios were normalised. The set of normalised values was then imported into MatLab Version 6 and input into a Population-Based Incremental Learning (PBIL) algorithm. PBIL was then used to identify those subsets of features that best separated the failed and non-failed data clusters for a one, two and three year forward forecast period. Thornton's Separability Index (SI) was used to evaluate the degree of separation achieved by each feature subset. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - Machine learning for corporate failure prediction : an empirical study of South African companies TI - Machine learning for corporate failure prediction : an empirical study of South African companies UR - http://hdl.handle.net/11427/14643 ER - en_ZA


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