The effect of financial distress on stock returns, through systematic risk and profitability as mediator variables
Bibliographic record
Abstract
This study aims to determine the relationship between financial distress and systematic risk, the relationship between financial distress and profitability, the relationship between systematic risk and stock returns, the relationship between profitability and stock returns, and the indirect effect between financial distress and stock returns through systematic risk and company profitability. by collecting data on the Indonesia Stock Exchange on chemical companies and the element industry in 2018-2020. This study was conducted to find out the answers to the impact caused by the global economic turmoil. Using the PLS-SEM method and four latent variables, which are divided into one endogenous variable, two moderating variables and one exogenous variable, it is hoped that it can provide value for the statistical calculation activities carried out. This study uses a quantitative descriptive method with two moderating variables that link financial distress and stock returns. This study produces a specific indirect effect; the financial distress variable significantly impacts Stock Return through systematic risk and profitability variables with a p-value < 0.05. The main finding of this study is the significant impact of world economic turmoil that must be faced by creating systematic risk to convince. Investors and provide education to potential investors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".