Enhancing Financial Distress Prediction Using Logistic Regression and Managerial Literacy: A Conceptual Framework for the Malaysian Construction Industry

Abstract

Financial distress prediction has remained an area of prime concern to regulators, investors, and corporations, especially regarding high-risk sectors like construction and related capital-intensive industry domains. Despite years of research and achievement, traditional ratio approaches, especially the Altman Z-Score, have remained less than satisfactory in terms of overall prediction power across various industry sectors and within emerging market economies. In specific regard to the Malaysian construction industry, existing empirical evidence has indicated persistent levels of misclassification, especially involving instances of Type II or ‘False Negative’ errors where ‘PN17/GN3’ classified entities were previously considered to be ‘financially sound.’ To address such limitations within traditional early warning systems, this article conceptually refines an intersectionary approach to financial distress prediction by systemically blending ratios based on theories of Financial Distress with non-financial ‘Business Management Literacy’ variables based on theories of Agency and Signal Theory approaches. This article’s conceptual framework includes managerial education, managerial experience, and firm capacity variables regarding management excellence and governance competency as specific managerial variables to improve traditional model-based ‘causality gaps.’ Instead of advancing completely new theory-based approaches to financial distress prediction, this article’s conceptual framework proposes to juxtapose overall ‘Altman Z-Score’ based prediction logics to refined ‘Logistic Regression’ approaches with general probabilistic interpretation power and overall ‘context-based’ adaptabilities to traditional models’ specific recursive limitations. At its conceptual level, this article’s intersectionary framework should improve overall early warning system performance by jointly addressing specific ‘symptoms’ and ‘causes’ of financial distress in specific prediction domains with contemporary focus on especially ‘cost-reducing’ ‘False Negative’ classifications within traditional approaches’ specific industry sectors like construction projects in developing markets. This article’s conceptual framework should be especially relevant to existing construction industry sectors where ‘management competence’ like certain other sectors has remained a ‘make-or-break’ ‘firm-level’ element within overall firm ‘survival’ processes.