---
title: Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using 10-K Filings
url: https://www.ml-quant.com/papers/doi/10-1016-j-techfore-2024-123965/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: doi:10.1016/j.techfore.2024.123965
source_url: http://dx.doi.org/10.1016/j.techfore.2024.123965
featured: 2025-01-08
citations: 17
topic: ML & AI Methods
---


# Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using 10-K Filings

A new method using natural language processing (NLP) has been suggested for classifying AI stocks, providing a cost-effective alternative that outperforms existing AI-themed ETFs.

- Source: http://dx.doi.org/10.1016/j.techfore.2024.123965
- Identifier: doi:10.1016/j.techfore.2024.123965
- Released: 2025-01-03
- First featured: Quant Letter No. 81 (2025-01-08): https://www.ml-quant.com/issues/2025-01-08/
- Citations (Semantic Scholar): 17
- Published in: Technological forecasting & social change
- Topic: ML & AI Methods

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