arXiv:2606.08450v1 Announce Type: new Abstract: Financial portfolio trading is naturally formulated as a reinforcement learning problem, where an agent sequentially rebalances assets under changing market conditions to balance return, risk, and transaction costs. Yet in non-stationary markets, raw OHLCV states and short-horizon return rewards often provide an under-specified learning interface, motivating large language models as a way to inject financial knowledge into state and reward design while constraining open-ended generation. To this end, we propose GIFT, an LLM-guided framework for s
The increasing sophistication of LLMs and the recognition of limitations in traditional RL for finance are converging to create new methodologies for financial decision making.
This development signals a significant advancement in applying AI to complex, non-stationary financial markets, potentially leading to more adaptive and intelligent trading systems.
The financial sector's approach to reinforcement learning will shift towards integrating LLM-guided knowledge for superior state and reward design, moving beyond purely data-driven models.
- · Quantitative hedge funds
- · AI/ML platform providers
- · Financial data providers
- · Traditional algorithmic trading firms
- · Investment firms reliant on static models
Financial trading models will become more dynamic and integrated with qualitative market insights processed by LLMs.
Increased efficiency and potentially higher returns for institutions adopting these advanced AI-driven strategies could lead to greater market concentration.
The complexity and 'black box' nature of LLM-guided financial systems may introduce new regulatory challenges and systemic risks in financial stability.
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Read at arXiv cs.AI