The Effects of the Financial Crisis on the Financial Performance of Malaysian Companies
Bibliographic record
Abstract
The 2007/08 financial crisis which began in the United States was not felt in Malaysia until the last quarter of 2008 where GDP stalled and then began to fall. The country has a high export to GDP ratio and in 2009 the contraction in manufacturing exports was steep. This paper investigates the effects of the crisis on the financial performance of 70 companies in the manufacturing sector over a period of 5 years from 2006 to 2010. Using factor analysis, an initial set of 21 financial ratios was reduced to just six significant ratios. Using this smaller set of representative ratios, the sample companies were cluster analyzed into 4 categories of poor, below average, above average and good financial performers. The results showed that there is a direct effect of the financial crisis on the financials of companies in the study where 46 companies categorized as good in 2006 fell to just 6 in 2010 while 7 companies in the poor category increased to 27 during the same period. Of particular concerns would be the 15 companies that fell three clusters down from good to poor performers and 15 out of 17 companies in the average categories that dropped into the poor performing category. A key finding from this study is that when a financial or economic crisis occurs, most companies’ financials would be severely and adversely impacted and if the negative economic conditions do not improve, there would be high probabilities that many companies would face liquidity and solvency issues that could eventually lead to collapse and bankruptcies. Finally, with just 6 key financial ratios, a company’s financial performance can be tracked and analyzed over a period of time resulting in the enhancement of the quality of credit evaluations as well as the minimizing of investor risks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".