MS Banking and Finance

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Browsing MS Banking and Finance by Author "ABDULLAH M. ASHRAF BAJWA"

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    Predicting Corporate Financial distress using logit model, a case of selected non financial firms in Pakista
    (UMT.Lahore, 2020) ABDULLAH M. ASHRAF BAJWA
    State Bank of Pakistan in its different communications instructed lenders to evaluate them borrowers in different dimensions. One of the major borrower assessments is to estimate probability of default based on borrower’s financial strength. Basel Committee for International Banking Supervision proposed two approaches for minimum capital requirements namely foundation F-IRB (Foundation Internal Rating Based) and AIRB (Advanced IRB) approach. In foundation –IRB (F-IRB) approach a single component i.e. Probability of Default is internally estimated while in Advanced –IRB approach, banks have to internally estimate the Exposure at Default (EAD) and Loss given Default (LGD). All non-financial corporates of Pakistan were included in the population of the study. Companies from different sectors like textile, sugar, food and allied, chemicals etc. were in the population. Their financial statements are available on SBP website or PSX website. As research aim to derive a predictive model; population is divided into two categories based on their default status (defaulted and non-defaulted). Final study sample consisted of 304 nondefault and 54 default companies. Financial statements data from financial year 2012 to 2017 were collected from State Bank’s financial statement study document. 27 ratios from solvency, cash flow, liquidity, profitability, and valuation category were computed, and binary logistic regression model was applied. Final model was fitted on one cash flow, three liquidity, one profitability and two solvency ratios. Probability of default of each case in the data set was computed, and it was found that Average probability of default for non-defaulted firms is 1.22% while it is 62.65% for defaulted firms. Any bank which is lending to corporate customers can adopt this model for their probability of default estimation under SBP and Basel guidelines.