How do Individual, Institutional, and Foreign Investors Win and Lose in Equity Trades? Evidence from Japan<sup>*</sup>
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We investigate the gains and losses from equity trades of individual investors, various institutional investors, and foreign investors in the Tokyo Stock Exchange. We develop a trade‐weighted performance measure and examine the impact of trading intervals, price spreads, and market timing on performance. We find that different investor types gain or lose from different sources. For example, we discover that individual investors have poor market timing ability but potentially gain during short‐run trading intervals as their average sell price is consistently higher than the average purchase price. In contrast, we find that foreign investors consistently generate gains from trade due to good market timing, although their average sell price is lower than the average purchase price. Also, we find that foreign investors extract significant portion of their gains by trading against Japanese institutional investors when Japanese investors trade before their fiscal‐year end.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it