Invisible Frontiers: Robust and Risk-Sensitive Financial Decision-Making within Hidden Regimes
Bibliographic record
Abstract
In this dissertation, we delve into the exploration of robust and risk-sensitive strategies for financial decision-making within hidden regimes, focusing on the effective portfolio management of financial market risks under uncertain market conditions. The study is structured around three pivotal topics, that is, Risk-sensitive Policies for Portfolio Management, Robust Optimal Life Insurance Purchase and Investment-consumption with Regime-switching Alpha-ambiguity Maxmin Utility, and Robust and Risk-sensitive Markov Decision Process with Hidden Regime Rules. In Risk-sensitive policies for Portfolio Management, we propose two novel Reinforcement Learning (RL) models. Tailored specifically for portfolio management, these models align with investors’ risk preference, ensuring the strategies balance between risk and return. In Robust Optimal Life Insurance Purchase and Investment-consumption with Regime-switching Alpha-ambiguity Maxmin Utility, we introduce a pre-commitment strategy that robustly navigates insurance purchasing and investment-consumption decisions. This strategy adeptly accounts for model ambiguity and individual ambiguity aversion within a regime-switching market context. In Robust and Risk-sensitive Markov Decision Process with Hidden Regime Rules, we integrate hidden regimes into Markov Decision Process (MDP) framework, enhancing its capacity to address both market regime shifts and market fluctuations. In addition, we adopt a risk-sensitive objective and construct a risk envelope to portray the worst-case scenario from RL perspective. Overall, this research strives to provide investors with the tools and insights for optimal balance between reward and risk, effective risk management and informed investment choices. The strategies are designed to guide investors in the face of market uncertainties and risk, further underscoring the criticality of robust and risk-sensitive financial decision-making.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".