DESIGN AND IMPLEMENTATION OF A MACHINE LEARNING ALGORITHM FOR PREDICTING THE STOCK MARKET
Abstract
This study focuses on the design and implementation of a machine learning algorithm for predicting stock market trends and price movements. The system utilizes historical stock data, including opening and closing prices, trading volume, and market indicators, to train predictive models capable of identifying patterns and forecasting future market behavior. Various machine learning techniques are applied to improve prediction accuracy and support data-driven investment decisions. The developed system aims to provide investors, traders, and financial analysts with reliable insights for minimizing risks and maximizing returns in the stock market. Results demonstrate the effectiveness of machine learning in enhancing stock market forecasting compared to traditional analytical methods.
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