Do Past Performance and Past Cash Flows Explain Current Cash Flows into Retail Superannuation Funds in Australia?
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the link between current-quarter cash flows and both past performance and past cash flows using a sample of Australian retail superannuation fund data (managed growth and managed stable) drawn from the period 1994 to 2000. This is a rapidly growing sector within the superannuation industry and it reflects investment behaviour of smaller investors rather than institutions and large corporations. Using both the Gruber (1996) approach and panel-data analysis we find a positive relationship between past performance and current-quarter cash flows as well as evidence of persistence in cash flows over time. Panel-data analysis also identifies a positive relationship between current net cash flows and past performance and cash inflows as well as a negative relationship between current net cash flows and past outflows. Market-wide growth in the retail superannuation sector over the study period does not appear to be driving these results.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 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 it