Uncertainty indices built by language models

Economic, climate, trade and monetary policy uncertainty indices appear on 423 recent papers in the finance and economics journals, almost all built from counts of newspaper words. Only 5 of them use language models, embeddings or text mining beyond word counts. Would an index that reads the articles agree with the word counts, and would it predict better?

What prompted this

Since October 2025, 423 papers carry a policy-uncertainty, uncertainty-index or text-based keyword (243 in finance journals, 144 in economics): "economic policy uncertainty" on 161, "climate policy uncertainty" on 106, "policy uncertainty" on 68, "trade policy uncertainty" on 48, "monetary policy uncertainty" on 32. Only 5 of the 423 also carry a language-model, BERT, transformer, machine-learning, deep-learning, embedding, text-mining or natural-language-processing keyword.

Questions

Do indices built by a language model reading the full text (asking whether the article describes policy uncertainty and of what kind) track the word-count indices, and where do they diverge?
Do the model-built indices predict investment, hiring and asset prices better, or does the simpler index already capture what matters?
Can the same approach build indices for countries and languages where the newspaper word lists were never validated?

Data that could help

Newspaper archives (Factiva, LexisNexis), the published uncertainty indices, and macro and market data.

Next step

Score a year of articles from the newspapers used by one uncertainty index with a language model and compare the resulting monthly index with the published one.

Related research activity

The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26
Keyword	Papers, last 30 days	Rank	Papers, last 36 months
economic policy uncertainty	5	#393	488
text mining	1	#2881	59
natural language processing	4	#525	131

@misc{scholar4ai-ideas-llm-built-uncertainty-indices, author = {{Scholar4AI}}, title = {Uncertainty indices built by language models}, year = {2026}, howpublished = {\url{https://scholar4ai.com/ideas/llm-built-uncertainty-indices}}, urldate = {2026-10-23}, note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0} }