Perception of Investors towards the Investment Pattern on Different Investment Avenues - A Review
Bibliographic record
Abstract
In India, usually all investment avenues professed risky by the investors. The main features of investments are security of principal amount, liquidity, income stability, approval and easy transferability. Investment avenues are available such as shares, bank, companies, gold and silver, real estate, life insurance, postal savings and so on. The required level of returns and the risk tolerance decided the choice of the investor. The investment may be differ choices from national savings certificates, provident fund, mutual fund schemes, insurance schemes, chit funds, bank fixed deposits, and company fixed deposits, company shares, bonds /debentures, government securities, postal savings schemes and real estate. It would be concluded that in this fast affecting world, we save get extra money. Added risk directs to more profit. For the example total liquidity, income stability a variety as shares, bank companies, gold and silver, real estate, life insurance postal etc., but, most of the people preferred bank deposit by the cause of more respondents invested for purchasing home and long-term growth but, most of the investors could not aware to investing their money in mutual funds and shares. More of debate and confusions in the investment pattern, investment avenues. Therefore, in this paper, the researcher wants to check the earlier research work based on investors among the investment avenues to get an idea about the investment pattern.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".