Construction of an Industry Cycle Indicator for Profitability Prediction Analysis of Aggregate Firms in Bangladesh
Abstract
The purpose of this study was to construct a unique industry cycle indicator (ICI) for Bangladeshi aggregate firms within the industrial sector, aiming to provide early signals of industrial vulnerability, identify turning points, and evaluate predictive performance. The methodology involved constructing the ICI following the approach of the Conference Board (2000) and testing its robustness with a macro-stress test using lagged independent variables to allow early predictions. The findings indicate that the ICI model effectively demonstrates macroeconomic fluctuations in the industrial sector, with a lead time of around six months for predictions. It outperforms existing leading indicators when compared to the reference series. The model also highlights the significance of supply-side reforms over demand-side interventions, underplaying the role of aggregate industrial efficiency in influencing the economic cycle. The conclusion drawn from this study is that the ICI can provide policymakers with advanced warnings of potential industrial -vulnerabilities, enabling them to take precautionary steps to mitigate risks. The study rejects both the Keynesian and monetarist approaches, aligning more closely with neo-classical economics, which assumes that productivity shocks lead to economic fluctuations and subsequent adjustments to a new equilibrium. The implications of this research are substantial for financial market supervisory authorities. Enhanced knowledge of the macroprudential policy framework and standardized macroprudential tools and indicators can significantly improve their capability to forecast systemic risks and avoid or reduce the impact of industrial crises. This study sets a foundation for future researchers interested in exploring and developing this area further.
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Copyright (c) 2020 Maria Afreen
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