International Journal For Multidisciplinary Research

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

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Stocker: FinTech Innovation in Stock Market Prediction and Trading Automation

Author(s) Prof. Dr. Ms. JAYANTHI KANNAN M.K, Sunil Kumar, Yashwant Ramesh Mule, Ujwal Anand, Abhay Raj Raj, Gopal Patidar
Country India
Abstract The rapid evolution of financial markets, combined with the increasing reliance on technology-driven solutions, highlights the necessity of intelligent systems to assist traders in making informed decisions. The "Stocker" platform is designed as an innovative stock market prediction and trading solution that leverages advanced machine learning algorithms to analyze market trends and forecast stock prices. With features including real-time stock trading, predictive analytics, and a user-friendly interface, Stocker aims to empower investors by providing accurate insights and seamless trading experiences. By integrating secure payment processing, real-time data tracking, and intelligent recommendations, Stocker enhances user engagement and supports efficient financial decision-making in the dynamic stock market landscape. The integration of FinTech in stock market prediction and trading automation is revolutionizing the financial world. AI-driven tools like algorithmic trading, high-frequency trading (HFT), and predictive analytics are enabling faster, data-driven decision-making. These technologies analyze vast amounts of data in real-time, identify patterns, and execute trades with precision. The AI-powered systems can process market trends, financial news, and economic indicators to predict price movements and optimize trading strategies. This reduces human error and emotional biases, making trading more efficient and potentially more profitable.
Keywords Stock Market Prediction, Machine Learning, Real-time Trading, Financial Analytics, Investment Strategies, Stock growth prediction, FinTech Innovation in Stock Market, Trading Automation.
Field Computer Applications
Published In Volume 7, Issue 2, March-April 2025
Published On 2025-03-17
DOI https://doi.org/10.36948/ijfmr.2025.v07i02.39169
Short DOI https://doi.org/g882j2
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