Multidimensional Views Behind Target-Price Revisions: An LLM-Based Representation of Analyst Outlooks

Abstract

A target price compresses an analyst's multidimensional views into a single number. We propose an LLM-based text representation that extracts these views from research reports as question-based outlook scores with supporting evidence. The questions organize reported views on earnings, valuation, industry conditions, and other investment dimensions into a compact representation of analyst sentiment. In walk-forward evaluation, the scores improve prediction of subsequent target-price revisions beyond current-revision momentum and sector differences. They outperform financial dictionary and FinBERT sentiment measures. These findings suggest that structured questions can capture information about subsequent analyst actions that is not fully reflected in the current numerical revision. By linking each dimension to an explicit investment question and supporting report evidence, the approach makes the extracted outlooks available for inspection and interpretation. It offers a way to complement target-price signals with a multidimensional view of the judgments expressed in analyst narratives.