Seasonality, Quarterly Impact on Net Asset Value of Equity Linked Savings Schemes
Bibliographic record
Abstract
This article attempts to (a) understand the quarterly seasonal behaviour of Net Asset Value of Equity Linked Savings Schemes (ELSS—Tax Saver Schemes), (b) analyse statistical significance of quarterly seasonal behaviour, (c) investigate the existence of unit root (Stationarity and Non-Stationarity time series of deseasonalised NAV) and (d) examine the weak form of efficient market hypothesis. Twenty tax saver schemes launched on or prior to 2006 were selected for analysis. The quarterly NAV data for the study period between 2006 and 2011 were obtained from respective asset management companies. The study reveals that (i) majority of the selected funds follow normal distribution, that is, seasonalised NAV is not skewed, (ii) 3rd quarter and 4th quarter of a calendar year are seen as appropriate for structuring investment decisions, (iii) statistical significance exists among the seasonal indices across quarters, (iv) autocorrelation function among the selected schemes are statistically significant, that is, deseasonalised NAV is non-stationary, (v) majority of schemes contains unit root at base level, that is, deseasonalised NAV is non-stationary, (vi) all schemes do not contain unit root at first differences, that is, deseasonalised NAV is stationary, (vii) deseasonalised NAV is weak–form inefficient, that is, cautious investors can have greater mileage by exploiting historical NAV and at the same time historical NAV facilitates to forecast future NAV on account of its non-randomness. Hence, this study accentuates that mutual fund investments are beneficial in the long run.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".