Mrežna analiza prometa dionica: uvid u strukturalne obrasce unutar indeksa CROBEX
Bibliographic record
Abstract
Ovaj rad primjenjuje mrežnu analizu za istraživanje promjena u strukturi hrvatskog tržišta kapitala usporedbom dvaju razdoblja: globalne financijske krize 2008. i postpandemijske stabilizacije 2023. godine. Korištenjem parcijalnih korelacija logaritamskih promjena prometa dionica unutar indeksa CROBEX, uklanja se utjecaj zajedničkog tržišnog faktora, čime se dobiva precizniji uvid u idiosinkratične obrasce međupovezanosti dionica. Na temelju Spearmanovih korelacija konstruirane su mreže za različite razine značajnosti (1 %, 5 %, 10 %), odvojeno za pozitivne i negativne povezanosti, uz izračun ključnih mrežnih mjera poput gustoće, asortativnosti, modularnosti i centralnosti. Rezultati pokazuju da je mreža iz 2008. godine centraliziranija, s izraženim središnjim čvorovima hubova i nižom modularnošću, što odražava prisutnost sistemskog rizika i ponašanje stada karakteristično za krizna razdoblja. Nasuprot tome, mreža iz 2023. godine pokazuje veću fragmentiranost, višu modularnost i uravnoteženije pozitivne i negativne povezanosti, što ukazuje na tržište koje je decentraliziranije, ali i potencijalno manje likvidno. Ovakve mrežne karakteristike mogu poslužiti kao signal za identificiranje strukturalnih promjena, promjena u sentimentu ulagača te kao alat za unapređenje portfeljnih strategija i praćenje tržišne stabilnosti.
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.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".