International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Artificial Intelligence Vs Efficient Market Hypothesis: Can Technology Beat The Stock Market?

Author(s) Dr Bharathi N.S Yadav, Ms. Swati H S, Ms. Tanisha R Iytha, Ms. Vaishnavi S M, Ms. Thanmayi Gowda P, Ms Trisha L ., Ms Varuni T D
Country India
Abstract The stock market has undergone significant transformations over the years. In the past, trading was a lively affair on physical trading floors, where investors communicated through shouts and hand signals. Today, however, the landscape is dominated by digital platforms that operate at breathtaking speed and rely heavily on cutting-edge technology. A predominant theory within this realm is the Efficient Market Hypothesis (EMH), established by Eugene Fama. EMH states that stock prices reflect all known information, making consistent market outperformance a significant challenge for investors. However, the rise of artificial intelligence is starting to shift this dynamic. AI can analyze vast datasets in seconds, far beyond human capability, by examining historical trends, corporate reports, news articles, and social media sentiment to predict stock price movements. The objective of this research paper is to explore the interplay between AI and EMH, examining whether modern technology can indeed surpass traditional market performance. EMH, long-standing in the field of finance, holds that stock prices inherently reflect all pertinent information, thus making it difficult for anyone to achieve consistent excess returns. However, with the rise of AI and algorithmic trading, the solidity of this theory is being re-evaluated. This investigation utilizes secondary data gathered from 11 research articles and 3 relevant books within the realms of finance, behavioral economics, and financial technology. It delves into how AI systems leverage extensive data, including historical price movements, current news, and social media activity, to forecast stock trends. Additionally, it contrasts the theoretical frameworks of EMH with actual market behaviors observed today. The findings reveal that while markets are generally efficient over the long haul, short-term inefficiencies still exist. These inefficiencies arise from factors like human emotions, delays in information processing, and unequal access to certain data. AI systems excel at pinpointing and capitalizing on these fleeting opportunities, particularly in short-term trading contexts. Overall, this paper posits that while AI does not entirely invalidate EMH, it highlights the theory's limitations. Markets are not perfectly efficient; rather, AI serves to illuminate these gaps. Nonetheless, as AI adoption becomes widespread among traders, the opportunities available may diminish over time, leading to a future where markets may edge closer to the efficiency EMH suggests.
Keywords Behavioural Finance, Market Efficiency, Stock Market Prediction, Financial Technology, High-Frequency Trading.
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-02
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E-ISSN 2582-2160
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