A Modified Markowitz Multi-Period Dynamic Portfolio Selection Model Based on the LDIW-PSO
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
<p>Modern financial market is an extremely complicated nonlinear system, while gaming and speculation in the market makes the returns and risks of financial assets a great deal of uncertainty. How to construct an effective portfolio, realize the maximization of portfolio returns and the minimization of risks, and optimize the investment capital allocation efficiency are becoming increasingly a hot topic. This paper discusses a revised Markowitz Multi-period Dynamic portfolio mode by introducing LDIW-PSO in the process of solving the optimal investment weight. The LDIW-PSO has greatly improved the efficiency of searching the optimal weight of the portfolio. In addition, this paper introduces exponential-revised Sharpe ratio (Ex-Sharpe) as the objective function and adopts the optimal variance bound to reflect the real risk preferences of the investors in the financial markets better and modify covariance estimation errors of Mean-Variance model. The empirical study results show that the LDIW-PSO is very suitable for solving the dynamic portfolio model, and the exponential-revised Sharpe ratio can reflect financial market investment situation accurately and avoid covariance errors effectively.</p>
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it