Micro-, meso- and macro-level determinants of stock price crash risk: a systematic survey of literature
Bibliographic record
Abstract
Purpose This article conducts a thorough review and synthesis of the empirical research on the antecedents of stock price crash risk to ascertain the macro-, meso- and micro-level determinants contributing to stock price crashes. Design/methodology/approach The authors systematically reviewed 85 empirical papers published in ABS-ranked journals to assess the macro-, meso- and micro-level determinants causing stock price crashes. Findings The findings indicate that macroeconomic factors such as corporate governance, political and legal factors, socioeconomic indicators and religious beliefs have an effect on firm-level corporate behavior contributing to stock price crash risk. At a meso-level customer concentration, industry-level characteristics, media coverage, structural features of ownership and behavioral factors have a substantial effect on stock price crash risk. Finally, micro-level variables influencing stock market crash risk include CEO qualities and compensation, business policies, earnings management, financial transparency, managerial characteristics and firm-specific variables. Research limitations/implications Based on our analysis we identify priority areas for future research. Originality/value This is a seminal work using a multilevel framework to categorize the determinants of stock price crashes into micro-, meso- and macro-level factors.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".