Financial distress among SMES in Malaysia: An early warning signal

• Main Author: Ma'aji, Muhammad Muhammad
• Format: Thesis
• Language: English; English
• Published: 2014
• Subjects: HG Finance
• Online Access: https://etd.uum.edu.my/4719/1/s815698.pdf; https://etd.uum.edu.my/4719/6/s815698_abstract.pdf; https://etd.uum.edu.my/4719/
• Institution: Universiti Utara Malaysia

Predicting financial distress among SMEs can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance, variables and analyse the influence of major corporate governance characteristics, i.e., ownership and board structures, on the likelihood of financial distress. The two extensively documented approaches, MDA and logit methods were used. The final sample for the estimation model consists of 172 companies with 50 percent nonfailed cases and 50 percent failed cases for the period between 2000 to 2012. The prediction models perform relatively especially in the logit and MDA model that incorporate governance, financial and non-financial variables, with an overall accuracy rate of 93.6 percent and 90.7 percent respectively in the estimated sample. The accuracy rate in the holdout sample was 91.2 percent for the logit and MDA model. This evidence shows that the models serve as efficient early warning signals and can thus be beneficial for monitoring and evaluation. Controlling shareholder, number of directors and sex of managing director are found to be significant predictors of financially distressed SMEs