Bibliographic record
Abstract
This study aimed to verify the existence of commonality in liquidity in the Brazilian market by delivering common factors of liquidity with an innovative technique (dynamic factor analysis). Also sought to examine the relationship between commonality and return on individual assets. Most studies of commonality are proceeded with data analysis and worked out daily in developed markets like the United States (Chord, Roll and Subrahmanyam (2000) Huberman and Halka (1999), Hasbrouck and Seppi (2001), Henker and Martens (2003 ), Lee (2005) and Brockman, Chung and Perignon (2009)), but some use intraday data on the formation of the sample and, moreover, show the commonality in emerging markets. Brockman and Chung (2002), Zheng and Zhang (2006), and Giouvris Galariotis (2008) are examples of studies in these markets, using a variety of measures and different methodological approaches. There were no Brazilian studies involving the commonality, but a study of foreign Brockman, Chung and Perignon (2009) reported weak evidence in Brazil. The procedure adopted for estimating the dynamic factor analysis (DFA) was based on a study of Frederic (2006) using the software Stata version 11. This survey was conducted with the shares belonging to the Bovespa index (Bovespa) from intraday data every five minute interval in the period from January 4 until April 30, 2010, total assets of 63 theoretical portfolio of first quarter 2010. Due to the limitation of the software, the sample was divided into three groups (group 1, 2 and 3), each composed of 21 companies with 498 5 minute intervals during periods of 83 observations for each trading day, the day 01/04/2010 until 01/11/2010 generating a total of 10,458 observations for each group. Common factors were found from the liquidity variables, which explain in part the common variation in liquidity. After analyzing the factors we proceeded to estimate the regressions by group. For each group had three regressions, only the first return of Ibovespa regressing against the return of the asset. Then we included a factor for liquidity and, after all factors were included in the model. Among the results of the regressions, the Group 1 stands out, presented the highest coefficient of determination and where the Bovespa index return and Factor 1 were significant, indicating that beyond the market beta the common factor in liquidity also produces impacts on return the company. This study showed that there is commonality in liquidity in the market and also that there is influence of liquidity in the return of individual assets, confirming the evidence found by Brockman, Chung and Perignon (2009).
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".