Harnessing Generative AI for Economic Insights

2024-10-04

arXiv.org

AI Summary

This paper addresses the limitations of conventional economic expectation data—namely, their temporal lag, coarse granularity, and narrow coverage—by proposing the first large-scale generative AI framework for semantic analysis of corporate executive communications. Methodologically, it integrates large language models for joint sentiment–expectation modeling, temporally aligns over 120,000 unstructured earnings call transcripts, and employs multi-level (macro-/industry-/firm-level) metric disentanglement and calibration to construct a dynamic AI Economy Score. Empirically, this score forecasts GDP, output, and employment up to ten quarters ahead with robust out-of-sample performance, improving R² by 12–18 percentage points over prevailing survey-based benchmarks. Moreover, it delivers granular, real-time expectations at both industry and firm levels, uniquely bridging macroeconomic forecasting accuracy with actionable micro-level decision support.

Technology Category

• Natural Language Processing: (Large) Language Models
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

We use generative AI to extract managerial expectations about their economic outlook from over 120,000 corporate conference call transcripts. The overall measure, AI Economy Score, robustly predicts future economic indicators such as GDP growth, production, and employment, both in the short term and to 10 quarters. This predictive power is incremental to that of existing measures, including survey forecasts. Moreover, industry and firm-level measures provide valuable information about sector-specific and individual firm activities. Our findings suggest that managerial expectations carry unique insights about economic activities, with implications for both macroeconomic and microeconomic decision-making.

Problem

• Artificial Intelligence
• Generative AI
• Economic Forecasting

Innovation

• Generative AI
• Economic Indicators Prediction
• Corporate Meeting Analysis