Do Market and Herding Effect Really Impact on Investment Decision Making in the Indian Share Market?
Bibliographic record
Abstract
“Mad March – 2020, witnessed dramatic down-slide in the world’s top stock exchanges due to COVID-19 pandemic with worrying volatility which resulted in traders panic sold off their holdings out of fear”.2020’s first quarter witnessed substantial losses in the several well-recognized stock indices, especially between March 6 to 18, more than 20% that were triggered downward by the outbreak of COVID-19. Dow Jones Industrial Average and S&P 500 experienced the worst first quarter ever in the history during the year 2020 reducing its value by 23.2%. The year 2020 witnessed several historical landmark changes in the Indian share market movements along with other prominent stock exchanges of the globe. On March 23rd, 2020, Benchmark index SENSEX touched intraday lowest value of 25880 and NIFTY fell to the lowest value of 7583. Throughout the globe, including Indian investors, started to rush for clearing their holdings ahead of dark lines created by the pandemic in spite of most of the financial analysts’ suggestion for fresh buy and/or to hold previous purchase for long. Supporting financial experts’ views, within the next nine months SENSEX has gained around 100% and stood at 48834.34 on 8th Jan 2021. There are many studies both in India and outside the country that have provided evidence for the role of behavioral factors on investment decision-making at respective stock markets. Here authors attempted to verify, ‘weather market factor and herding effect of behavioral variables do influences on investment decision making of Indian share market investors?’
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".