What Has Gone Wrong With Japan’s Stock Performance Over the Last Three Decades?
Bibliographic record
Abstract
Japan has a poor stock performance compared with the US in three decades (1989-2019). At the end of 1989, the Nikkei 225 Index reached its all-time high (38,916); by the end of 2019, the index was 23,657, a change of −39% over the entire period. Meanwhile, the S&P 500 Index increased from 353 to 3,231, a change of 815%. To comprehend this matter, we investigate the areas of economic conditions, corporate governance, corporate financial policies, corporate financial performance, and relative valuation. Our research method is a combination of qualitative and quantitative approaches. Our analyses reveal a variety of problems: slow GDP growth, weak legal protections and low governance ratings, insiders-dominated and cross-holding ownership structure, excess financial assets, low profitability, slow growth of earnings and revenues, and contraction of relative valuation. In the most recent decade (2009-2019), we note some improvements: expansionary monetary policy, productivity growth, better corporate governance, increased dividend payments and stock repurchases, earnings growth, and enhanced profitability. Therefore, Japan’s transformation is substantial, though it is gradual and incomplete.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".