Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202020,
        author = {G Aravindh and Gunothaman KR and Kamalakannan A and Ms.R.Iyswarya},
        title = {AI-Based Stock Price Manipulation Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6966-6970},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202020},
        abstract = {Stock price manipulation, particularly pump-and-dump schemes, has become more sophisticated with the rise of social media platforms that rapidly influence investor sentiment and trading behavior. Traditional market surveillance systems primarily rely on numerical trading data and rule-based techniques, which are often ineffective in detecting manipulation driven by coordinated online hype, misinformation, and automated bot activity. This paper proposes an AI-based multi-modal stock price manipulation detection framework that integrates social media sentiment data with high-frequency financial market data. The system analyzes temporal lead–lag relationships to identify patterns where sudden spikes in online sentiment precede abnormal movements in stock prices and trading volumes. By leveraging machine learning (ML) and deep learning (DL) techniques, the framework distinguishes between genuine market trends and artificially induced price fluctuations. The proposed model provides early warning signals of suspicious trading activity, improves the accuracy of market surveillance, and supports regulators and financial analysts through scalable, data-driven, real-time detection of potential stock price manipulation.},
        keywords = {stock market manipulation; pump-and-dump detection; deep learning; LSTM; sentiment analysis; anomaly detection; multi-modal learning.},
        month = {May},
        }