Prediction of corporate recovery in Malaysia

Philip Sinnadurai (), Norashikin Ismail () and Noor Marini Haji-Abdullah ()
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Philip Sinnadurai: Independent Researcher
Norashikin Ismail: Universiti Teknologi Mara, Cawangan Kampus Selangor
Noor Marini Haji-Abdullah: Universiti Teknologi Mara Cawangan Kelantan

Review of Quantitative Finance and Accounting, 2022, vol. 59, issue 4, No 2, 1303-1334

Abstract: Abstract Using data from Malaysia, this paper investigates prediction of recovery by distressed companies, during the period 2001–2016. A unique feature of Malaysia is Practice Note 17, a listing rule imposing extra disclosure requirements on financially distressed companies. Our methodology entails logistic regressions. Traditional corporate recovery prediction models are estimated, using financial statement indices and ratios as independent variables. These models are augmented with additional independent variables, indicators of the severity of financial distress and the type of regularization plan. The models are estimated using a treatment sample of 121 companies that entered financial distress during the investigation period. Each financial statement index, for each treatment observation, is calculated relative to the counterpart index of an individual control-match observation. We selected each individual control- match, from a sample of control observations in the same industry-year (defined narrowly) as the relevant treatment observation. Selection of the individual control-match is on the basis of proximity to the relevant treatment observation, with respect to the continuous variables used in the study. The results support our hypotheses. They suggest that distressed companies are more likely to recover if their distress is diagnosed at early stages. The results also indicate that distressed companies are more likely to recover if they pursue an operational versus strategic recovery plan.

Keywords: Financial distress; Corporate recovery; Malaysia; Practice Note 17 (search for similar items in EconPapers)
JEL-codes: G33 G38 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11156-022-01076-6

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