Serviceability Analysis of Monte Carlo Simulation for Stock Market Trading Price
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When stocks are traded in the market, the price of stock has a great variation. Therefore, predicting the stock price is a huge challenge. Monte Carlo Simulation (MCS) is a typical method for stock price simulation. However, the serviceability of MCS is still insufficient. In this paper, the serviceability analysis has been done to evaluate the performance of MCS in different stocks price simulation. The results show that the Group 1, Group 5 and Group 8 have the highest predicted return under our condition settings. Among them, PDD has the biggest contribution, and the combinations holding PDD have a good return rate. Besides, the combinations holding WMT have a good performance of resisting risk because the WMT has better stability. The findings illustrate that the performance of Monte Carlo is influenced by stock itself more than the investment combination.
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.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.005 |
| 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) | 0.003 | 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 it