Predicting Long-Run and Short-Run Movement of Sectoral Index: Evidence From Philippine Stock Market
Bibliographic record
Abstract
The financial markets provide a viable avenue for investors who wants to invest their idle resources. Investors need accurate information to minimize investment risk and make the right investment decision. This study attempted to test the predictability of the Philippine Stock Exchange (PSE) sectoral indices. The data used in this study are the daily closing price of the six sectoral indices from January 2010 to December 2019. Augmented Dickey-Fuller (ADF) for stationarity test and Johansen Cointegration and Granger Causality analysis were used to test the long-run and short-run relationship among the six sectoral indices. The results showed that all indices are not predictable at the index level (I(0)) but predictable at the first difference (I(1)). The study found no long-run relationships between sectoral indices. The result also revealed that the sectoral indices have a short-run relationship in both directions.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".