Stock Price Prediction and Risk Analysis

Project Overview

This project explores stock market data for leading technology companies – Apple (AAPL), Google (GOOGL), Amazon (AMZN), and Microsoft (MSFT). Through a combination of statistical analysis, technical indicators, portfolio risk assessment, and predictive modeling, the project aims to:

• Understand stock behavior and volatility
• Assess risk vs. return for individual stocks and portfolios
• Identify market patterns and correlations
• Predict future stock prices using ARIMA and LSTM models

Project Highlights

Objective

• Perform exploratory and predictive analysis of stock prices
• Compare risk-return trade-offs across assets
• Forecast future stock trends

Concepts & Techniques Used

• Exploratory Data Analysis (EDA): summary statistics, price trends, correlation heatmaps
• Technical Indicators: moving averages, Bollinger Bands, rolling statistics
• Portfolio Risk Assessment: portfolio returns, volatility, covariance/correlation matrices, Value at Risk (VaR), efficient frontier visualization
• Predictive Modeling:
  • ARIMA for classical time-series forecasting
  • LSTM networks for deep learning-based sequence modeling

Tools & Libraries

• Python (Pandas, NumPy, yfinance (Yahoo Finance API))
• Matplotlib & Seaborn (data visualization)
• Modeling statsmodel (ARIMA), tensorflow/keras (LSTM)

Key Results

1. Trends & Volatility: Tech stocks generally move together (high positive correlations). Short-term decoupling observed during specific market events (e.g., Microsoft 2024 outage).
2. Portfolio Performance:
   • Equally weighted portfolios smooth volatility compared to single stocks
   • Diversification benefits limited due to strong correlations among tech giants
3. Predictive Modeling:
   • ARIMA provides a simple, interpretable baseline for short-term predictions
   • LSTM captures overall trends better but lags in sudden price movements
   • Errors remain within realistic ranges, showing potential for practical forecasting

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