Financial distress prediction model in Hong Kong: determinants of financial distress in Hong Kong listed firmsTools Goh, Yi Wern (2023) Financial distress prediction model in Hong Kong: determinants of financial distress in Hong Kong listed firms. [Dissertation (University of Nottingham only)]
AbstractThis paper identifies factors from four groups of variables (financial data, market variables, macroeconomic variables and corporate governance variables) which possess significant financial distress predictive ability using 2,849 firms listed on the Main Board of the Hong Kong Stock Exchange during the period 1999-2022. Firms that have delisted from the Main Board of Hong Kong Stock Exchanges under Practice Note 17 and Rule 6.01(A) under the exchange listing rules are classified as financially distressed firms in this study. Besides, the four groups of variables are also examined to determine which of them has the greatest predictive power. This study constructs the financial distress prediction model logistic regression and machine learning-based approach. It is found by the logistic regression that financial data and macroeconomic variables are significant predictors of financial distress among the Hong Kong listed firms. The prediction model built using machine learning-based approach also exhibits great ability in classifying financial healthy and financial distressed firms.
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