This project consists of analyzing two daily financial time series of stock returns from Amazon and Meta (Facebook).
The main objectives of this analysis are:
✅ Understanding the economic dynamics driving these two market leaders.
✅ Identifying potential correlations between Amazon and Meta.
✅ Predicting stock trends based on historical data (from 01/06/2012 to 01/12/2021).
These companies belong to GAFAM (Google, Apple, Facebook, Amazon, Microsoft), the dominant tech firms shaping global markets. Understanding their stock performance is essential for:
- Evaluating market positioning.
- Assessing long-term investment strategies.
- Identifying economic or financial trends linking these two giants.
1️⃣ Data Collection & Cleaning
- Stock data retrieved from Yahoo Finance.
- Data preprocessed using Pandas (Python) and Tidyverse (R).
2️⃣ Descriptive Statistics & Visualization
- Analyzing stock trends using R Shiny Dashboards.
- Plotting historical price movements and volatility.
3️⃣ Predictive Modeling
- ARIMA models for time series forecasting.
- Machine Learning regression models (Random Forest, LSTM).
4️⃣ Correlation & Market Trends
- Identifying cross-influences between Amazon and Meta.
- Evaluating market conditions impacting their stock prices.
🔹 Amazon & Meta stock price correlations over time
🔹 Forecasts for stock trends (next 12 months)
🔹 Economic insights on how tech giants move together in financial markets
- 🐍 Python: Pandas, NumPy, Scikit-learn, Matplotlib.
- 📊 R & Shiny: Data visualization, dashboards.
- 📈 Yahoo Finance API: Stock market data.
- 📡 Machine Learning: ARIMA, LSTM, Random Forest.
✅ Incorporating macroeconomic indicators (inflation, interest rates).
✅ Extending the dataset to include Google, Apple, and Microsoft.
✅ Developing a real-time stock analysis dashboard using R Shiny.
📜 Stock Data Source: Yahoo Finance
📚 Time Series Forecasting: ARIMA Guide
📘 Machine Learning in Finance: Scikit-Learn
📢 For questions or collaboration, contact : @smdlabtech
By smdlabtech : Stock Market Analysis