VOLATIAI: REAL-TIME FINANCIAL MARKET VOLATILITY FORECASTING USING DEEP LEARNING

Abstract

Financial markets are highly volatile and influenced by numerous dynamic factors such as economic 
indicators, geopolitical events, and investor sentiment. Accurately predicting this volatility is critical for 
investors and financial institutions to mitigate risk and make informed decisions. Traditional statistical 
models like GARCH and ARCH often fall short due to their linear assumptions, inability to adapt to real
time data, and reliance on historical trends. To overcome these limitations, this project proposes an AI
Driven Financial Market Volatility Predictor that leverages real-time data and advanced machine learning 
(ML) techniques. The system incorporates data preprocessing, SMOTE for handling class imbalance, and 
efficient feature extraction methods. It employs K-Nearest Neighbors (KNN) and Convolutional Neural 
Networks (CNN) for volatility classification, supported by a user-friendly Tkinter-based GUI for 
interaction and visualization. Experimental results demonstrate that the CNN classifier achieves superior 
performance, with an accuracy of 95.65%, outperforming the KNN classifier at 87.37%. The CNN model 
also excels in precision, recall, and F1-score, highlighting its ability to capture complex, non-linear 
patterns in financial data.This system addresses the key limitations of traditional approaches by providing 
a scalable, adaptive, and accurate solution for market volatility prediction. Its real-time processing 
capabilities and high accuracy make it a valuable tool for financial analysts, traders, and institutions 
seeking to enhance decision-making and risk management. By integrating AI and real-time analytics, this 
project contributes to building a more data-driven and resilient financial environment.