Alternative Data and NLP in Quant Strategies › Alternative Data and NLP in Quant Strategies

For decades, quantitative strategies relied on a relatively narrow universe of information: prices, volumes, financial statements, and economic indicators. You had access to the same Bloomberg terminal, the same SEC filings, the same earnings announcements. The informational playing field was level. Alpha came primarily from superior modeling or faster execution.

The available information set has changed. Today's quantitative funds ingest satellite imagery of retail parking lots to estimate sales before earnings releases, analyze the sentiment of millions of social media posts to gauge market mood, and parse credit card transaction data to track consumer spending in real time. Funds process linguistic patterns in CEO speech to detect deception, confidence, or uncertainty. These non-traditional sources, collectively called alternative data, can produce signals unavailable in traditional datasets. They also raise questions about data ethics, regulatory compliance, and the sustainability of information advantages.

This chapter explores the alternative data revolution and the natural language processing (NLP) techniques that make much of it actionable. Building on the machine learning foundations from the previous chapters, we'll examine how to extract tradable signals from unstructured text, work through practical implementations of sentiment analysis, and confront the unique challenges these approaches present.