A Stochastic Control Approach to Defined Contribution Plan Decumulation:\n "The Nastiest, Hardest Problem in Finance"
Bibliographic record
Abstract
We pose the decumulation strategy for a Defined Contribution (DC) pension\nplan as a problem in optimal stochastic control. The controls are the\nwithdrawal amounts and the asset allocation strategy. We impose maximum and\nminimum constraints on the withdrawal amounts, and impose no-shorting\nno-leverage constraints on the asset allocation strategy. Our objective\nfunction measures reward as the expected total withdrawals over the\ndecumulation horizon, and risk is measured by Expected Shortfall (ES) at the\nend of the decumulation period. We solve the stochastic control problem\nnumerically, based on a parametric model of market stochastic processes. We\nfind that, compared to a fixed constant withdrawal strategy, with minimum\nwithdrawal set to the constant withdrawal amount, the optimal strategy has a\nsignificantly higher expected average withdrawal, at the cost of a very small\nincrease in ES risk. Tests on bootstrapped resampled historical market data\nindicate that this strategy is robust to parametric model misspecification.\n
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".