πͺ The new math of new news
AI Summary
A new study titled 'The Inefficient Pricing of News' finds that markets are surprisingly slow to incorporate news into stock prices, with an LLM-based trading strategy using 'news shocks' (residual embeddings) generating ~30% annual returns by trading only once a month. The research builds on Alfred Cowles' 1932 finding that professional forecasters cannot beat the market, but reframes the inefficiency: instead of human skill being the problem, it's the market's failure to process publicly available information using modern NLP tools. The author predicts quantitative hedge funds will exploit this anomaly within nine months, closing the gap.
Key Facts
Author Takes
Market efficiency and LLM-based news trading
The persistence of news-pricing inefficiency feels as shocking as Cowles' 1932 result, and the author predicts quant funds will close the anomaly within nine months of adopting LLM embedding strategies.
Future informational efficiency of markets
Markets will eventually become informationally efficient through LLM adoption, but this won't prove the wisdom of crowds β only that technology has advanced beyond pencil and paper.
Contrarian Angle
Trading Stocks Monthly Using LLM News Embeddings
Researchers built a long-short portfolio trading exclusively on 'news shocks' β residual embeddings from LLM-processed news articles β that returns ~30% annually while only rebalancing once per month.
In an era of high-frequency trading and real-time data, simply reading the news once a month with an LLM outperforms nearly all professional strategies β contradicting the assumption that modern markets are informationally efficient.
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