To what extent would an investment portfolio be affected by different variables in terms of Markowitz and Index model?
Bibliographic record
Abstract
Portfolio Theory has been widely used in the securities market. Investors expect to maximize the return of the portfolio based on a given level of risk. By using naive diversification, investors can partly reduce the portfolio's risk by reducing the firm-specific influences. However, due to the macro-economic factors (inflation, interest rates, exchange rates, etc.), the risk cannot be eliminated entirely. Based on two popular models, the Index and Markowitz models, we chose seven stocks as one portfolio and set five constraints to simulate a real stock market. Even though results are very similar between Markowitz and Index model, the Markowitz model is more suitable than the index model in terms of circumstances we faced. And with any additional constraints added to the "free market," our portfolio return can only be negatively or non-affected. The purpose of this paper is to determine how the portfolio performance would be affected by different factors and how these two models would be used based on our comparative analysis of our portfolio in the index model and Markowitz model.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".