Abstract:

In today's dynamic financial landscape, accurate prediction of stock market trends is crucial for informed investment decisions and financial stability. This research investigates novel methodologies to enhance stock market prediction by integrating advanced machine learning techniques with comprehensive data analysis. Focusing on both fundamental and technical aspects, the study utilizes Big Data sources to develop and validate robust prediction models. The research begins with an in-depth exploration of fundamental indicators such as earnings per share (EPS), price-to-earnings (P/E) ratios, and return on equity (ROE), alongside technical metrics including trading volumes, relative strength index (RSI), and moving averages. Data from reputable financial databases and market platforms form the basis for constructing predictive models. Machine learning algorithms, particularly Random Forests and Neural Networks, are employed to analyse and forecast stock price movements. These models are rigorously evaluated using performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared to assess their accuracy and reliability. Comparative analyses with traditional regression models highlight the superior predictive capabilities of the advanced machine learning approaches. Case studies on selected equities further illustrate the practical application of these models in real-world scenarios. Companies like Alpha Tech Inc., Beta Industries, and Gamma Enterprises serve as examples where the predictive models successfully forecasted stock prices with minimal error, demonstrating their potential in portfolio management and risk assessment. In conclusion, this research underscores the transformative impact of integrating Big Data and advanced machine learning in stock market predictions. The findings not only enhance investment strategies but also pave the way for more informed financial decision-making. Moving forward, these methodologies offer promising avenues for improving market efficiency and optimizing investment outcomes in the ever-evolving financial landscape.

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