Impact of Demographics and Perceptions of Investors on Investment Avenues
Bibliographic record
Abstract
The primary purpose of this study is to investigate how investment choice gets affected by the demographics and perceptions of the investor. Investor’s behavior is influenced by many factors at the time of investment decision making. Demographic profile and perceptions play an important role to select a particular choice of investment. This paper helps to enhance the knowledge on different investment avenues like bank deposits, life insurance policies, mutual funds and equity which in turn will be highly useful to the financial advisors as it will help them advise their clients regarding these avenues with respect to their demographic profiles. The study also highlights the evidences that the investment choice depends on and is affected by the demographic variables and perceptions. However, the results of this research shows that the most investors have little knowledge on the investment avenues for their investments. Mann Whiteny ‘U’ test, Kruskal- Wallis has been conducted to test the hypotheses with the help of SPSS. Logistic regression results of this study proves that investors’ age, gender, education and occupation significantly influences the selection of investment avenues. Wealth Management professionals emphasizes that customer behavior and psychology play a vital role in successfully building and sustaining a wealth management relationship. Behavioral finance is new emerging science which focuses on understanding the psychology effects on investment decision.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".