Unmasking Delistings: A Multifactorial Analysis of Financial, Non-Financial, and Macroeconomic Variables
Bibliographic record
Abstract
The stability of financial markets is influenced by the strength and transparency of companies listed on stock exchanges. This paper explores how financial, non-financial, and macroeconomic factors influence delisting likelihood among companies listed on the Johannesburg Stock Exchange (JSE), addressing a limitation in the current body of knowledge that often overlooks the combination of these factors, especially within the context of developing economies. Using a sample of 302 companies delisted between 2010 and 2023 and 302 as a control group, we analyzed 72 variables through a multivariate panel probit regression model. Our findings reveal that delisting decisions are driven by a complex interplay of financial health, governance practices, and macroeconomic conditions. Financial health, including liquidity and market valuation, is crucial in mitigating delisting risk. Non-financial factors, such as corporate governance and shareholder composition, further reduce the likelihood of delisting. Macroeconomic conditions, including inflation and interest rates, introduce significant external pressures. This study is especially relevant in developing economies like South Africa, where economic volatility adds risks for listed companies. The results provide insights for companies, investors, regulators, and policymakers to ensure a stable and robust stock market and financial system and identify early warning signals for delisting.
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.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".