Design and Pricing of Three-Barrier Executive Stock Option with Chinese Characteristics Based on the Multinomial Lattices Approach
Why this work is in the frame
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Bibliographic record
Abstract
In view of the long-standing problem of stock price manipulation by executives of listed companies in China’s capital market and the challenges faced by inadequate supervision, this paper proposes a new equity incentive tool, the American three-barrier Parisian option. The tool innovatively integrates the flexibility of American options’ early execution and the triple path dependence barrier mechanism, including knockin and knockout conditions, which corresponds to three types of market failures: short-term irrational surge, long-term lack of vitality in horizontal trading, and continuous decline with no investment value of the stock price. Only when the stock price achieves stable, healthy, and sustainable growth can executives benefit from exercise; once there is abnormal fluctuation caused by manipulation or governance failure, the incentive will automatically fail. Research shows that the mechanism can effectively curb short-term speculation, guide executives to focus on long-term value creation, realize risk sharing and benefit sharing, and improve corporate governance efficiency. At the same time, the tool can reduce market manipulation, stabilize investor expectations, and form synergy with regulatory policies. This paper proposes to promote the implementation and application of this marketization tool through the combination of system guidance, technical support, and pilot promotion to provide reference and operable system innovation for improving the ecology of global emerging markets.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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