Author(s) :

Khwaish Khandelwal .

Volume/Issue :

Abstract :

Predicting financial markets has long been a challenging risk due to their inherent volatility, complexity and high frequency nature. Traditional method such as statical models have limited capacity to handle the vast amounts of structured and unstructured data produced in real-time. This paper explores the application of data science and deep learning techniques for real-time financial market prediction, focusing on stock price forecasting, volatility prediction, focusing, and high-frequency trading. We highlight the role of time series, analysis, sentiment analysis and the integration of alternative data sources in enhancing predictive accuracy.

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