Bibliographic record
Abstract
Liquidity is the pre-condition for a well-functioning and efficient market. Liquidity can be perceived, but difficult to measure due to its multi-dimensional characteristics. Studies have discussed various characteristics of liquidity and its influencing power on return and asset pricing. The article has considered Indian MidCap stocks and measured its liquidity using Amihud and trading volume as proxies. It has found Indian MidCap stocks have varying degree of liquidity. During the intraday, MidCap stocks have L-shaped liquidity pattern. The article observed that P-E ratio, P-B ratio, Dividend Yield and Index of Industrial Production are the significant determinants of liquidity. The article has estimated liquidity betas and carried out Granger non-causality test to articulate its relation with CAPM beta. The article has also found stability of liquidity beta across MidCap stocks. The liquidity betas of MidCap stocks have time-varying volatility. Relative Strength Index (RSI) and Change in Trading Volume are exogenous variables in explaining the time-varying volatility of beta. The study observed that MidCap stocks are claiming liquidity premium and liquidity premium is influencing the asset pricing along with WML, HML and EMR factors.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".