Assessing financial distress predictability among publicly listed non-financial firms in the Philippines: Utilizing five traditional predictive models - Damgo, Diomyka C. (Animo Repository, 2019)
Financial distress predictive models have been established and developed throughout the years as they often play a crucial part in the investing public’s decision-making. Previous researches have tested the predictability of these models using both financial and market data of publicly-listed companies from different sectors in their respective countries. This has resulted to a body of literature that led to the development of estimated prediction models that best fit the financial environment of mostly developed countries. In light of this, the researchers set out to assess the accuracy of five traditional prediction models (TPM) — Altman (2000) Z-Score Model, Ohlson (1980) O-Score Model, Zmijewski (1984) Probit Model, Shumway (2001) Hazard Model, and Blums (2003) D-Score Model — in predicting the financial distress of 175 non-financial companies listed in the Philippine Stock Exchange (PSE) from 2015 to 2017. Financial and market data were used to compute financial distress scores for every company in order to assess each TPM’s predictive power based on their odds of accuracy. Novel to this research are the two areas in which accuracy were assessed in: (1) at which year prior to experiencing financial distress does a TPM have the most accurate predictive power, and (2) for each year, which of the five TPMs had the most accurate predictive power. Results show that using a wide variety of financial and market data from the Philippines that all five TPMs have differences in prediction accuracy. Shumway’s Hazard Model provides the highest overall accuracy, but also the highest Type I error. Ohlson’s O-Score model provided the highest overall accuracy the closer it is to the year of financial distress. However, the results also showed that the accuracy of the TPMs are low compared to when they were applied to developed markets, showing that there may be a need to design models and assigned coefficient for emerging markets like the Philippines.