Analysis of LQ45 share portfolio on Quadrimester I during the Covid-19 pandemic
Bibliographic record
Abstract
Investment is a way of getting profit by investing a certain amount of capital in certain assets. Investing in shares in LQ45 amid the Covid-19 pandemic is one way to benefit when many sectors are experiencing an economic downturn. The purpose of this study was to analyze the differences in the optimal portfolio of LQ45 stocks in the 2019 and 2020 quadrimester I. The samples of this study were companies listed in LQ45. This research method uses the treynor index and t-test. The results of this study are that there is a significant difference in the optimal portfolio using the treynor index model between quadrimester I 2019 and 2020 on LQ45 stocks, this is influenced by conditions amid the Covid-19 pandemic which affects all sectors. The highest optimal number of purchases in the month April 2020 is occupied by companies with the KLBF code, this is an advantage that the company gets during the Covid-19 pandemic. Future research is expected to be able to allocate investment funds optimally for each share to achieve optimal profits. The investor is expected to be able to estimate in advance the stocks that will be selected for their investment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".