Robust Portfolio Optimization with Deep Risk Factor Models Under Regime Shifts
Bibliographic record
Abstract
Modern portfolio management faces significant challenges arising from the non-stationary nature of financial markets, particularly during periods of structural breaks or regime shifts. Traditional factor models, while mathematically elegant, often fail to capture non-linear dependencies and struggle to adapt rapidly to changing market conditions. This paper proposes a novel framework for robust portfolio optimization that integrates Deep Learning for non-linear factor extraction with regime-switching logic to handle market uncertainty. We introduce a temporal Deep Latent Factor Model based on Temporal Convolutional Networks (TCNs) to disentangle idiosyncratic risk from systematic risk factors in a high-dimensional feature space. These latent factors serve as inputs to a Hidden Markov Model (HMM) that dynamically identifies market regimes. Subsequently, we formulate a distributional robust optimization problem where the ambiguity set is constrained by the Wasserstein distance, centered around the regime-dependent empirical distribution. This approach mitigates the estimation error inherent in mean-variance optimization and provides resilience against worst-case scenarios. Our empirical analysis, conducted on S&P 500 constituents over a twenty-year horizon, demonstrates that the proposed method significantly outperforms traditional benchmarks and state-of-the-art deep learning baselines in terms of Sharpe ratio and maximum drawdown control.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".