arXiv q-fin· Bolin Song (Cornell University), Surui Xiang (United Nations)·· 5 hr agoAI Score38
When News Is Stale: Portfolio Allocation with Reinforcement Learning and Large Language Models
When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models
AI summary
A quantitative finance study compares two portfolio allocation approaches for handling stale news signals: retaining signals and providing timeliness information to the portfolio strategy, versus filtering out stale news before portfolio selection. The study conducts backtests based on historical data from 2023 to 2025, using a fixed large language model to extract signals from SEC public disclosure filings, and compares the after-fee returns of reinforcement learning strategies and direct stock-picking LLM strategies.
Source:arXiv q-fin · arxiv.org
Academic papers#Papers / Research#Machine Learning#Large Language Model Applications#Portfolio Construction#arXiv#SEC