Dynamic Objective Selection with Safeguards and LLM Oversight for Financial Decision-Making

2026-06-02

AI Summary

Traditional financial decision-making relies on fixed optimization objectives, rendering it ill-suited for dynamic markets. Existing approaches that switch among latent regimes are often sensitive to noise, exhibit latency, and incur high turnover. To address these limitations, this work proposes the DOSS framework, which eschews latent state modeling and instead formulates objective selection as a sequential classification task. DOSS dynamically selects the optimal objective—among candidates such as return maximization, loss aversion, and risk-adjusted performance—based on interpretable statistical summaries of recent returns. By integrating confidence-aware gating with a rule-driven large language model (LLM) supervision mechanism, DOSS enables forward-looking, time-leakage-free objective switching. This approach significantly reduces portfolio turnover and operational instability while enhancing overall decision-making performance.

Abstract

Financial decision-making tasks such as stock recommendation and portfolio allocation typically estimate future return and risk and then select trades or allocations for an investor, and the chosen optimization objective often determines realized performance. However, because market conditions evolve over time, a fixed objective can be suboptimal across regimes, while regime-switching pipelines that rely on latent regime estimates can be noisy or delayed and frequent switching can increase turnover and operational instability. In this paper, we propose DOSS (Dynamic Objective Selection with Safeguards), a learning-based selector that directly chooses the decision-relevant objective function at each time point from interpretable statistical summaries of recent returns, selecting among a small set of candidates (e.g., return-seeking, loss-averse, and risk-adjusted) without introducing intermediate regime variables. DOSS formulates objective selection as a classification problem over objectives and performs sequential updates with a rolling window to make forward-looking selections without temporal leakage, while also outputting a confidence score for each proposal. To mitigate misselection and excessive switching in deployment, DOSS applies confidence-aware gating with a fail-safe that overrides low-confidence proposals to a conservative default and enforces explicit controls tied to switching frequency. We further integrate governance by positioning a Large Language Model (LLM) as an oversight component rather than a generator of new objectives: the LLM is restricted to accept a proposed objective or override it to a predefined safe default, with deterministic rule-based constraints triggering overrides when needed.

Problem

• dynamic objective selection
• financial decision-making
• regime switching
• optimization objective
• market conditions

Innovation

• Dynamic Objective Selection
• Confidence-aware Gating
• LLM Oversight
• Regime-agnostic Decision Making
• Financial Portfolio Optimization