Assessing Commonality in Liquidity with Principal Component Analysis: The Case of the Warsaw Stock Exchange
Bibliographic record
Abstract
The studies concerning commonality in liquidity on emerging markets in Central and Eastern Europe are scarce and, in particular, they do not utilize the Principal Component Analysis (PCA) to identify latent factors in liquidity. Therefore, the main aim of this research is to assess commonality in liquidity on the Warsaw Stock Exchange (WSE) with the use of the PCA to extract common components of liquidity across a sample of stocks, and from a set of several liquidity proxies. The robustness tests within the whole sample and sub-periods are provided. The PCA results reveal that common latent factors in liquidity estimates exist on the Polish stock market, and three principal components are sufficient to substitute for the seven liquidity proxies utilized in this research. The regressions using these three principal components of liquidity proxies as latent factors in the market model of liquidity indicate no evidence of co-movements in liquidity on the WSE. The results are homogenous for all investigated periods so no reason has been found to reject the research hypothesis that commonality in liquidity does not exist on the Polish stock market. To the best of the authors’ knowledge, no similar research has been conducted for the WSE thus far.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".