Impact of Portfolio Strategies on Portfolio Performance and Risk
Bibliographic record
Abstract
The Portfolio strategies are the effective investment tools pertaining to active and passive investment approaches. This signifies the investor’s inclination of buying and selling the risky and risk-free assets. The research includes four strategies namely buy and hold strategy, dynamic asset allocation, strategic asset allocation and tactical asset allocation along with their dimensions. Strategies based hypothetical portfolios are generated on the basis of 14 years’ stock prices (2005-2017). The annually and monthly risk-adjusted return ratios; Sharpe ratio, Treynor’s measure, CAPM and Jenson Alpha are calculated individually. Simulated annualized portfolios generate significant result with Sharpe and treynor measure. Alpha return is generated with buy and hold if based on growth in stock prices. For empirical result, One-way analysis of variance (ANOVA) is used for studying the relationship between the strategies. Post hoc Tukey’s test is applied to find the difference between the strategies. The ANOVA and Tukey’s post hoc test for monthly portfolios gives significant results with three measure Sharpe ratio, CAPM and Jenson Alpha. No empirical significant result is measured on the basis of treynor measure.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".