Words + Returns: Teaching Embeddings to Invest in Themes
How do you turn a fuzzy idea like “AI + chips” into a living, breathing portfolio that adapts as markets move? A new framework called THEME proposes a crisp answer: train stock embeddings that understand both the meaning of a theme and the momentum around it, then retrieve candidates that are simultaneously on‑theme and investment‑suitable. Unlike static ETF lists or naive keyword screens, THEME learns a domain‑tuned embedding space in two steps: first, align companies to the language of themes; second, nudge those semantics with a lightweight temporal adapter that “listens” to recent returns. The result is a retrieval engine that feeds a dynamic portfolio constructor—and in backtests, it beats strong LLM/embedding baselines and even average thematic ETFs on risk‑adjusted returns. ...