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dc.date.accessioned2020-12-10T11:36:05Z-
dc.date.available2020-12-10T11:36:05Z-
dc.date.issued2020-
dc.identifier.citationAbela, R.K. (2020). Maltese company failures and their recent financial history: an analysis (Master's dissertation).en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/65480-
dc.descriptionM.ACCTY.en_GB
dc.description.abstractPurpose : The purpose of this study is threefold. First, to identify financial and non-financial factors that can predict insolvency. Second, to compare and contrast these factors with the factors identified in the literature. Third, to analyse whether the factors identified are in line with how users of the financial statements assess the risk of an entity in the Maltese scenario. Design : A mixed methodology approach is applied. Data gathered from financial statements of both failed and non-failed companies is statistically analysed through 2 logistic regressions that can predict corporate insolvency within 1 and 2 years before failure. The qualitative aspect of the study comprises of 10 interviews with auditors, company representatives and the Malta Association for Credit Management. The findings are then compared to the factors found significant in the models. Findings : Model 1 that predicts corporate failure from one year prior has 11 signifcant variables. These include 5 financial variables : Total Assets/Total Liabilities, Positive Profit and Net Assets, Dividend Paid/Total Assets, Cash/Total Assets and Trade Receivables/Current Assets, 3 internal non-financial variables : Going Concern Paragraph, Audit Opinion and Owner Managed, 2 industry variables; Accommodation and Food and Wholesale and Retail, and 1 macroeconomic variable; Inflation Model 2 that predicts corporate failure from 2 years prior has 4 significant variables which include 3 variables from Model 1; Owner Managed, Going Concern and Positive Profit and Net Assets and another financial variable which is the Current Ratio. Model 1 and 2 have a classification accuracy of 93.3% and 90.6% respectively. From the interviews conducted it was noticed that cashflow is perceived as the most important financial factor. Company representatives believe that clients failing to honour their payments is the major risk that can lead to insolvency. The interview with MACM also provides reasons why Malta has a high daily outstanding sales ratio when compare to the EU average. Conclusions : Company failure prediction models can be used as a tool to assist users of financial statements to get a quick indication of the risk of insolvency of a company. However, it is important to complement the model with other factors, some of which are mentioned by the interviewees. Value : A failure prediction model study dates back to 2011, which, given a dynamic market may not be suitable anymore. Also, this is the first study of this kind that incorporates non-financial information including also industry variables and macroeconomic variables.en_GB
dc.language.isoenen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectBankruptcy -- Malta -- Mathematical modelsen_GB
dc.subjectBankruptcy -- Forecasting -- Mathematical modelsen_GB
dc.subjectBusiness failures -- Maltaen_GB
dc.titleMaltese company failures and their recent financial history : an analysisen_GB
dc.typemasterThesisen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.publisher.institutionUniversity of Maltaen_GB
dc.publisher.departmentFaculty of Economics, Management and Accountancy. Department of Accountancyen_GB
dc.description.reviewedN/Aen_GB
dc.contributor.creatorAbela, Renè Kriss-
Appears in Collections:Dissertations - FacEma - 2020
Dissertations - FacEMAAcc - 2020

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