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