OPTIMAL PORTFOLIO SELECTION FOR A DEVELOPMENT BANK
Bibliographic record
Abstract
This paper examines the formation and optimization of the investment portfolio of the development bank, which implements the state policy of financing socially significant projects that contribute to the economic growth of the country. This model takes into account the bank’s objectives and risk attitude.We propose a methodology for forming an optimal portfolio based on the Markowitz theory. An important feature of the proposed methodology is that it takes into account differences in priorities of the development bank and commercial banks. In particular, the development bank is less interested in maxi-mizing profits and is more interested in developing products and industries with high value added. The main focus of our methodology is on practical implementation issues arising because of data availability constraints existing for Kazakh companies. With that focus in mind, we model the portfolio optimization problem for the development bank that invests a limited amount of funds in private companies from various sectors of Kazakhstan’s economy. To make this example as useful as possible for the practical activities of Kazakhstan financial institutions, we use real yield data for large Kazakhstani companies listed on the Kazakhstan Stock Exchange
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".