An AI-Based Portfolio Value-at-Risk Prediction

The stock market is subject to fluctuations and uncertainties that can lead to financial losses for investors. Predicting potential losses is essential for effective portfolio management and risk assessment. This project aims to develop an Artificial Intelligence (AI)-based model to predict the Value at Risk (VaR) of a stock portfolio. Value at Risk is a financial risk measurement technique used to estimate the maximum potential loss of a portfolio over a specified period at a given confidence level under normal market conditions. The proposed system uses historical stock market data, including stock prices and returns, to analyze market trends and estimate potential portfolio losses. Machine learning techniques can be applied to identify patterns in historical data and improve risk prediction. The model's performance can be evaluated using appropriate statistical measures and by comparing its predictions with actual portfolio returns. The main objective of this project is to provide a reliable and data-driven approach to financial risk estimation, helping investors and portfolio managers make informed investment decisions and manage market risks more effectively.