Semantic State Abstraction Interfaces for LLM-Augmented Portfolio Decisions: Multi-Axis News Decomposition and RL Diagnostics
Authors: Likhita Yerra (AIVANCITY School of AI and Data), Remi Uttejitha Allam (AIVANCITY School of AI and Data)
Summary
The authors introduce SSAI, a framework for mapping sparse unstructured text (news) into K named, auditable coordinates (e.g., sentiment, risk, confidence, volatility) with neutral defaults on no-news days. They use it to build trading portfolios on US equities, testing direct factor portfolios, supervised ridge forecasters, and RL agents that all share the same four-axis representation. The four-factor portfolio reaches 307% cumulative return and Sharpe 1.067, but the apparent edge over buy-and-hold (243%) fails when you control for coverage stratification, apply realistic transaction costs (≥0.2%), or compare to simpler baselines like sentiment-only or PCA.
Main takeaways:
- Proposes a framework for converting sparse text into K auditable dimensions with neutral defaults, separating representation design from optimizer variance
- Tests on 30 NASDAQ-100 stocks (2019–2023) with four axes: sentiment, risk, confidence, volatility forecast
- Four-factor portfolio shows 307% return and Sharpe 1.067, but edge over buy-and-hold is fragile under controls and transaction costs
- Sentiment-only baseline, PC1 composite, and FinBERT portfolio are stronger ranking signals in this setting
Relevance
No connection to my persona-installation or language-model work—this is a finance paper on portfolio optimization using LLM-extracted features from news text. Included for completeness but entirely outside my research domain.
Abstract
arXiv:2605.06730v1 Announce Type: new Abstract: We introduce Semantic State Abstraction Interfaces (SSAI): a methodological template for mapping sparse unstructured text into $K$ auditable, named coordinates with neutral defaults on no-news days, designed to separate representation hypotheses from optimisation variance in sequential decision systems. Our contribution is the framework and its evaluation protocol, not a claim that SSAI outperforms denser alternatives. We instantiate SSAI with $K=4$ axes (sentiment, risk, confidence, volatility forecast) on a US-equity panel (30 NASDAQ-100 names, FNSPID news, 2019--2023 test), and evaluate it across direct factor portfolios, supervised ridge forecasters, and RL agents (DP-PPO, SAC) that share the same fixed $\phi$. The four-factor factor portfolio reaches 307.2% cumulative return and Sharpe 1.067, but apparent gains versus buy-and-hold (243.6%) fail coverage-stratified controls, reverse at $\geq 0.2$% costs, and are statistically fragile versus a sentiment-only baseline; a PC1 composite and a FinBERT portfolio baseline are stronger ranking signals in this setting. Ridge and RL blocks diagnose representation versus optimiser effects. We position SSAI as an interpretability-performance diagnostic and reusable protocol for sparse-text decision systems.