The Compounding Effect of Investors’ Cognition and Risk Absorption Potential on Enhancing the Level of Interest towards Investment in the Domestic Capital Market
Bibliographic record
Abstract
It is eminent to understand, be aware of and encourage domestic retail investors towards investment in the capital market in a developing economy such as India for tackling the situation of capital insufficiency and financial instability. Therefore, the study was purposed to find out the different dimensions of cognition that affect investment attitude and the different characteristics of risk absorption affecting the investment decision making. The study also intended to find the direct and the mediating impact of investors’ cognition directly and through risk-absorption scenarios on the level of interest on investment. The study used the causative research design and by using stratified random sampling, received 392 responses from investors with risk-absorption characteristics from four strata of Odisha (a state of India) through a self-constructed questionnaire. Factor analysis was used to find out the factor of cognition and risk absorption. Multiple linear regression was used to find out the effect of both factors of cognition and risk absorption on the intensity of purchase financial product or level of interest in investment. Mediation analysis was used to find the mediating impact showing the direct and indirect impact of cognition on interest in investment and through the factors risk absorption. The study found that the dimensions of cognition (hot, cold, social and meta) have a significant impact on the level of interest towards investment, so financial product sellers must use these dimensions and sources of cognition to bring up interest from the domestic investor to invest in the domestic capital market. It has also been found that the risk-absorption characteristics play a mediating and vital role in the relation between investors’ cognition and level of interest in investment. Therefore, it is imperative to uplift the risk-absorption capacity through different dimensions of cognition and sources of information, which can reflect in a better understanding of the market and investment scenarios.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".