Calendar anomaly in 3 Indices of CNX Nifty with respect to empirical study of quarter of the year effect, month of the year effect for the period of January 2004 -March 2013
Bibliographic record
Abstract
Calendar anomalies in CNX Finance index which consist of 15 Finance-Housing, Banks, Financial Institution, CNX IT index consist of 20 Computer-Software companies, CNX Pharmaceutical index consist of 10 Pharmaceutical companies. This study tests the presence of the ‘quarter of the year effect’, ‘month of the year effect’ on stock market indices volatility by using the CNX Finance index, CNX IT index, CNX Pharmaceutical index during the period of 1st January 2004 to 31st March 2013. Data was analysed using descriptive statistics and inferential statistics. Thus findings revealed that quarter of the year effect, month of the year effect is present in all 3 indices volatility i.e. risk and returns. The maximum returns of CNX Finance index, CNX Pharmaceutical index are observed in 2nd Quarter and minimum returns are observed in 4th Quarter. Whereas maximum returns of CNX IT index is observed in the Quarter 2 and minimum returns in the Quarter 1. CNX Finance and CNX IT both are showing maximum volatility in 1st quarter, Quarter 4 is highly volatile for CNX Pharmaceutical index. Finance index maximum returns in the month of September. Whereas IT index shows maximum returns in December month. Pharmaceutical index shows maximum returns in the month of April. Finance index shows minimum returns in the month of October, IT index shows minimum returns in the month of May, Pharmaceutical index shows minimum returns in the month of January. Finance and IT index shows maximum volatility in the month of May, whereas Pharmaceutical index shows maximum volatility in the month of October.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".