An Empirical Study on the Driving Factors of Financial Management Behavior of the Middle-Aged and Elderly Based on Logistic Model
Bibliographic record
Abstract
With the rapid development of economy and society, people's demand and participation in investment and financial management are also gradually increasing. With the advent of an aging society, the participation of the middle-aged and elderly people in investment and financial management behavior is also gradually increasing, but so far, there is still a large space for development. This paper takes the middle-aged and elderly people as the research object, analyzes the basic situation of the middle-aged and elderly people's investment and financial decision-making through the literature analysis method, collects the specific data of the factors affecting the investment and financial decision-making through the questionnaire survey, and uses the logistic model and other methods to analyze the data. Finally, the regression analysis results show that age, financial knowledge, herding, risk preference and future expectation will have a significant impact on the investment and financial decision-making of the elderly. Through this research, this paper hopes to provide relevant financial institutions with opinions and references related to products, help the middle-aged and elderly people avoid the risk of financial fraud, and make a certain contribution to the development of financial market and the improvement of the participation of the middle-aged and elderly people in investment and financial management.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".