Financial Risk Tolerance During Post-Pandemic: A Case Study Among Urban Older Malaysians
Bibliographic record
Abstract
The COVID-19 pandemic has negatively impacted the older population's financial satisfaction and well-being as health-related expenses deplete their retirement funds and family members lose their jobs or receive pay cuts.Older peoples may invest in riskier financial products or businesses to compensate for the loss of savings and financial aid.Hence, this paper intends to investigate the direct and indirect effects between financial satisfaction and financial risk tolerance among the Malaysian older population aged 60 and above.This primary data was collected in the third quarter of 2021 and employed purposive sampling through face-to-face interviews with a structured questionnaire.The results from Process Macro for SPSS and Model 4 (mediation analysis) show that older Malaysians' financial risk tolerance is not directly influenced by their financial satisfaction, which indicates that older populations share similar risk tolerance during the pandemic regardless of their financial satisfaction.However, financial satisfaction indirectly affects financial risk tolerance, mediated by attitude and subjective norms.These results indicate that the older population, who are more family-orientated and have better financial situations, are more likely to take the lesser risk.In contrast, those with sound financial knowledge and skills, able to identify and invest in profitable financial products, and strongly influenced by their friends are more likely to take higher risks.This study is essential for financial institutions in developing investment products targeting the older population and for the financial planner to educate the elderly to take a reasonable level of financial risk to preserve and accumulate retirement funds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".