DINAMIČKO TEMPIRANJE TRŽIŠTA INVESTICIJSKIH FONDOVA U HRVATSKOJ: PRISTUP POMIČNE REGRESIJE
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
Investors in stock markets have to continuously re-evaluate their investment strategies due to on-going changes on the markets.Thus, investors are able to achieve their goals more quickly.This paper, for the first time in Croatia, analyses mutual funds from a geographical aspect of funds' investments with a dynamic approach of estimating a market timing model.Therefore, the defensiveness/aggressiveness of a fund, as well as good/bad market timing over time is observed.The results of the analysis on 16 Croatian funds (for different time spans, due to data availability) indicate that parameters in market timing models do change over time.This means that the dynamic approach should be taken when evaluating mutual fund performance and market timing.At the end of the analysis a general guidance is given for potential investors as well as suggestions how to adjust their investment strategies.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it