Financial Performance Analysis and Financial Distress Prediction of Indonesia State-Owned Enterprises in The Construction Industry Listed on IDX Before and During Economic Crisis in the Covid-19 Pandemic Era (Period 2019 - 2021)
Bibliographic record
Abstract
The Covid-19 pandemic has brought an immense impact on Indonesia’s economy. Indonesia officially went into recession after the Central Statistics Agency (BPS) announced negative GDP growth for two consecutive quarters, namely in the second quarter (-5.32%) and the third quarter (-3.49%) of 2020. Indonesia’s contracted economy has caused depression in many Indonesian sectors. The results of a survey by BPS in 2020 noted that the construction sector was recorded as one of the sectors that experienced the most decline in revenue, which was 87.94%. This study aims to measure the financial performance and health condition of Indonesian construction SOEs listed on IDX namely ADHI, PTPP, WSKT, and WIKA based on the decree of the Ministry of SOEs no. KEP-100/MBU/2002 as well as the financial distress prediction (bankruptcy potential) by using the Altman Z-Score method for the period 2019 to 2021. The result of the financial health rank level of each company from 2019 to 2021: ADHI (BBB, CCC, and B), PTPP (BBB, B, and BB), WSKT (BB, CC, and B), and WIKA (A, B, and B) respectively. According to the Altman Z-score result, all companies experienced declining in the total Altman Z-score results from 2019 to 2021 and were interpreted as being in a state of financial distress, except for WSKT. This study will complete previous research with a different approach and focus that can give a more equipped view regarding the impact of Covid-19 on the construction industry in Indonesia.
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How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".