Machine Learning-Enhanced Dynamic Asset Allocation in Target-Date Investment Strategies for Pension Funds
Bibliographic record
Abstract
Target-date funds constitute the dominant default investment vehicle in defined contribution pension systems, managing approximately $3.4 trillion globally. Traditional glide path designs employ static allocation rules failing to adapt to evolving market regimes. This research develops a machine learning framework integrating temporal feature engineering with ensemble prediction models to construct adaptive asset allocation strategies. Our probabilistic optimization transforms static age-based allocation into a dynamic system responsive to macroeconomic indicators, volatility patterns, and correlation structures. Empirical analysis across 15-year backtesting demonstrates ML-enhanced strategies achieve 1.8% annual excess returns while reducing maximum drawdown by 34% compared to conventional glide paths. The framework incorporates gradient boosting machines for regime classification and LSTM networks for return forecasting, establishing differentiable optimization objectives balancing growth with capital preservation. Implementation protocols address overfitting through walk-forward validation and transaction cost constraints.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".