AI Impact on Asset Pricing in US Stock Market

Authors

  • Muhammad Faheem Ullah Research Scholar, Institute of Business Management Sciences, University of Agriculture Faisalabad, Pakistan.
  • Aysha Sami Latif Assistant Professor, Quaid-e-Azam College of Commerce, University of Peshawar, Pakistan
  • Waqar Ahmad MBA (B&F) 3.5 Years Institute of Banking and Finance (BZU) Multan.
  • Moazam Sarfraz Research Scholar, Institute of Business Management Sciences, University of Agriculture Faisalabad, Pakistan

Abstract

This study investigates the influence of artificial intelligence (AI) on asset pricing dynamics within the United States stock market. Utilizing data from 2010 to 2023, we employ a comprehensive analysis of various financial indicators and AI adoption metrics to assess the relationship between AI integration and stock price movements. Our findings suggest a significant relation between AI implementation and enhanced pricing efficiency, particularly in high-tech and financial sectors. The research employs partial least squares structural equation modeling (PLS-SEM) to evaluate the complex interplay of factors contributing to this phenomenon. Results indicate that AI-driven trading algorithms, sentiment analysis, and predictive modeling have substantially altered traditional asset pricing models, necessitating a reevaluation of existing financial theories.

Keywords: artificial intelligence, asset pricing, financial technology, machine learning, PLS-SEM, stock market

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Published

2024-10-10

How to Cite

Muhammad Faheem Ullah, Aysha Sami Latif, Waqar Ahmad, & Moazam Sarfraz. (2024). AI Impact on Asset Pricing in US Stock Market. Bulletin of Management Review, 2(1), 1–15. Retrieved from https://bulletinofmanagement.com/index.php/Journal/article/view/50

Issue

Vol. 2 No. 1 (2024): Bulletin of Management Review (BMR)

Section

Articles