Bibliographic record
Abstract
This article selects 10 companies in the financial sector, energy sector and consumption sector, as well as SPX500 index. This paper uses two models, not only the Markowitz model but also the index model, to calculate the correlation coefficient matrix, minimum variance, maximum Sharpe ratio, capital allocation line and so on to analyze the return rate and volatility of 10 specific companies. Four limitations were calculated for Markowitz model and Index model respectively and the two models were compared under the same constraints. Because common financial constraints and specific industries are rarely noticed in reality, the results of this paper reflect the following three aspects: First, in order to strike a balance between risk and return, SPX is an investment worth considering due to its high correlation coefficient; the second is that for certain investors with added constraints, the capital allocation line performs relatively poorly, as does the minimum variance boundary. Thirdly, because the Markowitz model uses stock's covariance while the beta and alpha of stocks are components that the index model uses to construct a portfolio, the results show that under certain risk conditions, Markowitz model is inferior to index model in pursuing maximum return and minimum risk under certain return conditions.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".