Abstract: The allure of substantial returns in the stock market attracts countless investors, but the inherent volatility of stock prices—shaped by numerous dynamic factors—poses significant risks. To mitigate these uncertainties, investors often rely on analytical methods. One of the most pressing challenges in this domain is the accurate prediction of stock prices, making financial time series forecasting a key area where machine learning demonstrates immense potential. Research highlights that sophisticated forecasting techniques can effectively anticipate market trends. This study harnesses the capabilities of big data through the Apache Spark framework, enabling real-time analysis of stock trading volumes via a well-structured trading volume index. The system is designed to issue risk alerts corresponding to varying trading volume thresholds, thus empowering investors with timely and insightful data for improved decision-making. The results indicate that investors operating in volatile markets can enhance their financial outcomes by leveraging trading volume-based risk assessments. To support this, the study employs foundational machine learning algorithms such as linear regression and random forest for risk prediction related to stock performance.
Keywords: Computer Vision, Linear Regression, Machine Learning, Data Analytics, Risk Assessment, Portfolio optimization
|
DOI:
10.17148/IJARCCE.2025.14610
