Financial Risk Tolerance: The Case of Older Chinese in Klang Valley, Malaysia 2020
Bibliographic record
Abstract
Ageing demographic profiles pose challenges to the nation as policymakers are concerned about the health, public pension and the financial management of the society. In Malaysia, retirees are mostly dependent on savings from the Employees Provident Fund (EPF), a government agency that manages savings and retirement plans for private-sector employees and non-pensionable public servants. Many Malaysians aged past the targeted retirement age of 60 years old chose to remain in the workforce, mainly due to insufficient retirement funds or has depleted their retirement funds in a short period. To ensure sufficient funds to attain an ideal retirement life, Malaysians resort to invest or seek business opportunities. Thus, this paper studies the impact of demographic characteristics (sex, age, and educational level) and finance characteristics (financial knowledge and financial satisfaction) on the risk tolerance among older Malaysian Chinese in the year 2020. The results of this study showed that the older Malaysian Chinese risk tolerance is highly influenced by sex, age, education level, financial knowledge and financial satisfaction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".