Risk-sensitive Policies for Portfolio Management
Why this work is in the frame
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Bibliographic record
Abstract
The decision-making on portfolio investment is fundamental in the financial market, but getting the optimal strategy is challenging due to high uncertainty and massive noise in the market. Deep Deterministic Policy Gradient (DDPG), proposed by Lillicrap et al. (2015), is a deep Reinforcement Learning (RL) algorithm that made remarkable achievements in the financial perspective. Although the applications of RL in financial trading are well-developed, it is surprising that most of the literature ignores the possible risk of rare occurrences of catastrophic events and the effect of the worst-case scenarios on trading decisions. In this paper, we first develop a novel deep RL algorithm, called Hierarchical DDPG, that combines the classical DDPG algorithm and the Hierarchical RL structure to control the risk of portfolio investment. Second, we adapt the distributional DDPG method for portfolio management problems, which aims to maximize the alpha-�percentile expectation based on the distribution of future returns. A real world data set is used to validate the performance of our proposed models. The experimental results show that our proposed models outperform the market and classical DDPG, and moreover, both approaches provide effective methods of constructing a risk-sensitive policy to protect investors from suffering a huge loss.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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