Can stock market time the FPIs: a study of seasonality
Bibliographic record
Abstract
The purpose of the paper is to analyse the presence of the days of the week effect, quarter effect, and month effect on the net investment of foreign portfolio investors (FPI) in India. For this purpose, research has been conducted for the time period from 4th January 2000 till 30th November 2016. FPI's daily net inflows have been analysed for occurrence and existence of month effect along with day of the week and quarter effect. Interaction effect between the quarter, month and nifty returns has been also analysed. Augmented dummy regression with ARIMA and GARCH has been used to analyse presence of seasonal anomalies in FPIs. Results confirmed the presence of calendar effect i.e., December month of the year (December) effect on the FPIs. Also, Friday effect and the Quarter 4 effect were present in the investing pattern of FPIs. Study concluded that FPIs were more biased towards trading more on Fridays and in the month of December in the overall analysis. Also, the presence of quarter effect was seemed to be caused by the presence of month effect along with the nifty returns of those specific months. Hence, there exists the strong interaction effect among the anomalies in the FPI net investment to India.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".