In summary:

I transform economic language into embedding vectors, and show these vectors are informative of perceptions and beliefs

I train LLMs that address credibility issues with ML in empirical research

I study economic mechanisms that drive valuation and misvaluation

Valuations can fluctuate when perceptions change—is a customer service firm that adopts a language model really an “AI” firm, or just a “service” firm?

I find that these perception changes relate to selective attention, firm communication, and technology transformations

Main results:

1️⃣ Embeddings explain valuations + outperform traditional characteristics
2️⃣ Returns reflect changes in how businesses are valued + changes in the perceived business model itself
3️⃣ Some changes in embeddings reflect misperceptions, which generate misvaluation

Embeddings also encode similarity between firms—geometric distance relates to established measures of perceived similarity

Taken together, these results demonstrate that a firm’s embedding is informative of its perceived business model

First, I train new language models on historical data to avoid lookahead bias. I’ve released these LLMs to the research community

Second, I use contrastive representation learning to construct embeddings of firms. The geometry of these vectors relates to economic features of firms

I transform financial news language into embedding vectors

Embeddings put quantitative structure on unstructured data, and have contributed to the success of machine learning over the past decade

Economic valuations fluctuate in ways empirical research cannot fully explain

What information are we missing? Economic theories emphasize the role of hard-to-quantify beliefs and perceptions

My job market paper develops algorithms + measurement to quantify perceptions of firms