Adaptive Model Selection in Stock Market Prediction: A Modular and Scalable Big Data Analytics Approach

This paper introduces an innovative architecture integrating Apache Kafka and microservices to enhance realtime stock market prediction.Our approach dynamically selects the most effective predictive model based on current market conditions, ensuring consistent accuracy.The key research method involves deploying Apache Kafka for real-time data streaming, coupled with a microservices framework to maintain scalability and adaptability.Our methodology includes a thorough evaluation of various machine learning models (specifically focusing on R 2 , the coefficient of determination, as the metric) to ascertain their performance across different market scenarios.The results demonstrate the architecture's ability to handle high data volume and velocity, while accurately adapting to market changes.The adaptability is evidenced by the varying performance of models like Convolutional Neural Network (CNN), Gate Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) across different entities such as Royal Bank of Canada, Google, and EUR/USD, with the system successfully identifying the most suitable model in real-time.This architecture not only provides a scalable solution for stock market prediction but also sets the foundation for future exploration in other real-time data-intensive domains.