Financial Distress Prediction Through Cash Flow Ratios Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this study to examine the relationship of cash flow ratios in predicting financial distress companies, with industrial and consumer product companies in Bursa Malaysia as the sample. The study on financial distress is critical as it can lead to bankruptcy, which may adversely affect the economy of the country. Therefore it is worth exploring any indicators that can identify the possibility of financial distress in the company. The tools enable to address the potential problems that can mitigate from distressed financial position. Most prior studies in Malaysia focus on traditional financial ratios, while this study exploits the strength of cash flow ratios. The liquidity ratio, solvency ratio, efficiency ratio and profitability ratio utilized in this study are derived from the statement of cash flows. The Altman Z-score is used to measure the level of the financial distress. The findings show mixed relationships between solvency ratio and financial distress and a negative significant relationship between profitability ratio and financial distress, whilst efficiency ratio has no relationship with the financial distress. These results suggest that cash flow ratios are reliable tools to predict financial distress for Malaysian context. The study is useful in giving insights to the stakeholders in their decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 it