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

• URL: http://arxiv.org/abs/2501.01763v1
• Date: Fri, 03 Jan 2025 11:27:49 GMT
• Title: Quantifying A Firm's AI Engagement: Constructing Objective, Data-Driven, AI Stock Indices Using 10-K Filings
• Authors: Lennart Ante, Aman Saggu,
• Abstract summary: This paper proposes a new, objective, data-driven approach using natural language processing (NLP) techniques to classify AI stocks. We analyze annual 10-K filings from 3,395 NASDAQ-listed firms between 2011 and 2023. Using these metrics, we construct four AI stock indices-the Equally Weighted AI Index (AII), the Size-Weighted AI Index (SAII), and two Time-Discounted AI Indices (TAII05 and TAII5X)
• Score: 0.0
• License:
• Abstract: Following an analysis of existing AI-related exchange-traded funds (ETFs), we reveal the selection criteria for determining which stocks qualify as AI-related are often opaque and rely on vague phrases and subjective judgments. This paper proposes a new, objective, data-driven approach using natural language processing (NLP) techniques to classify AI stocks by analyzing annual 10-K filings from 3,395 NASDAQ-listed firms between 2011 and 2023. This analysis quantifies each company's engagement with AI through binary indicators and weighted AI scores based on the frequency and context of AI-related terms. Using these metrics, we construct four AI stock indices-the Equally Weighted AI Index (AII), the Size-Weighted AI Index (SAII), and two Time-Discounted AI Indices (TAII05 and TAII5X)-offering different perspectives on AI investment. We validate our methodology through an event study on the launch of OpenAI's ChatGPT, demonstrating that companies with higher AI engagement saw significantly greater positive abnormal returns, with analyses supporting the predictive power of our AI measures. Our indices perform on par with or surpass 14 existing AI-themed ETFs and the Nasdaq Composite Index in risk-return profiles, market responsiveness, and overall performance, achieving higher average daily returns and risk-adjusted metrics without increased volatility. These results suggest our NLP-based approach offers a reliable, market-responsive, and cost-effective alternative to existing AI-related ETF products. Our innovative methodology can also guide investors, asset managers, and policymakers in using corporate data to construct other thematic portfolios, contributing to a more transparent, data-driven, and competitive approach.

Related papers

• Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation [2.2241228857601727]
  This paper presents an interdisciplinary meta-review of about 100 studies that discuss shortcomings in quantitative benchmarking practices. It brings together many fine-grained issues in the design and application of benchmarks with broader sociotechnical issues. Our review also highlights a series of systemic flaws in current practices, such as misaligned incentives, construct validity issues, unknown unknowns, and problems with the gaming of benchmark results.
  arXiv  Detail & Related papers  (2025-02-10T15:25:06Z)
• Explainable Artificial Intelligence for identifying profitability predictors in Financial Statements [0.7067443325368975]
  We apply Machine Learning techniques to raw financial statements data taken from AIDA, a Database comprising Italian listed companies' data from 2013 to 2022. We present a comparative study of different models and following the European AI regulations, we complement our analysis by applying explainability techniques to the proposed models.
  arXiv  Detail & Related papers  (2025-01-29T14:33:23Z)
• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• GAIA: Rethinking Action Quality Assessment for AI-Generated Videos [56.047773400426486]
  Action quality assessment (AQA) algorithms predominantly focus on actions from real specific scenarios and are pre-trained with normative action features. We construct GAIA, a Generic AI-generated Action dataset, by conducting a large-scale subjective evaluation from a novel causal reasoning-based perspective. Results show that traditional AQA methods, action-related metrics in recent T2V benchmarks, and mainstream video quality methods perform poorly with an average SRCC of 0.454, 0.191, and 0.519, respectively.
  arXiv  Detail & Related papers  (2024-06-10T08:18:07Z)
• AI in ESG for Financial Institutions: An Industrial Survey [4.893954917947095]
  The paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes.
  arXiv  Detail & Related papers  (2024-02-03T02:14:47Z)
• Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selection [0.07499722271664146]
  This paper introduces MarketSenseAI, an innovative framework leveraging GPT-4's advanced reasoning for selecting stocks in financial markets. MarketSenseAI analyzes diverse data sources, including market trends, news, fundamentals, and macroeconomic factors, to emulate expert investment decision-making. Our findings highlight the transformative potential of Large Language Models in financial decision-making.
  arXiv  Detail & Related papers  (2024-01-08T08:58:46Z)
• Guideline for Trustworthy Artificial Intelligence -- AI Assessment Catalog [0.0]
  It is clear that AI and business models based on it can only reach their full potential if AI applications are developed according to high quality standards. The issue of the trustworthiness of AI applications is crucial and is the subject of numerous major publications. This AI assessment catalog addresses exactly this point and is intended for two target groups.
  arXiv  Detail & Related papers  (2023-06-20T08:07:18Z)
• Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment [66.91538273487379]
  There is a certain consensus about the need to develop AI applications with a Human-Centric approach. Human-Centric Machine Learning needs to be developed based on four main requirements: (i) utility and social good; (ii) privacy and data ownership; (iii) transparency and accountability; and (iv) fairness in AI-driven decision-making processes. We study how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data.
  arXiv  Detail & Related papers  (2023-02-13T16:44:44Z)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
  Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
  arXiv  Detail & Related papers  (2022-04-11T14:36:39Z)
• Qlib: An AI-oriented Quantitative Investment Platform [86.8580406876954]
  AI technologies have raised new challenges to the quantitative investment system. Qlib aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
  arXiv  Detail & Related papers  (2020-09-22T12:57:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.