Optimizing Portfolio Efficiency in the Digital Era: A Data Envelopment Analysis of Range-Rebalanced Asset Investments
Bibliographic record
Abstract
In the digital era, the advent of new asset classes like cryptocurrencies and the application of advanced analytical tools have significantly reshaped portfolio management. This study employs Data Envelopment Analysis (DEA) to assess the efficiency of range-rebalanced investment portfolios incorporating diverse assets such as cryptocurrencies, major currencies, technology securities, and commodities. The analysis spans from October 1, 2016, to June 30, 2022, evaluating various rebalancing strategies including Allowed Range, Threshold, Drifting Mix, and Tactical approaches during different market conditions, including pre-COVID-19, during COVID-19, and post-COVID-19 periods. The findings highlight the superiority of strategic rebalancing, particularly combining high-value cryptocurrencies with technology securities, in enhancing portfolio performance and risk management. This research provides valuable insights for optimizing asset allocation in the dynamic financial landscape, underscoring the importance of strategic rebalancing in maximizing returns while managing risk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".