📈 Stock Price Forecasting and Investment Strategy Analysis

This project explores the use of statistical modeling to forecast stock prices and guide investment decisions. Using real-world financial data, the analysis compares multiple models to evaluate predictive accuracy and simulate portfolio performance.

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🎯 Project Highlights

• Techniques Used:

  • Time series decomposition
  • Linear and exponential smoothing
  • ARIMA model forecasting
  • Simulated trading strategy comparison

• Key Insights:

  • ARIMA model offered more stable long-term predictions
  • Momentum-based strategy outperformed naive buy-and-hold under certain volatility conditions
  • Accurate forecasting can significantly reduce investment risk

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🧰 Technologies Used

• Language: R (R Markdown)
• Libraries: forecast, ggplot2, TTR, readxl, dplyr
• Visualization: Time-series plots, residual analysis, strategy performance comparison

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📁 Files

• /code/ – Source .Rmd file with all analysis and modeling
• /data/ – Raw Excel data file used for modeling
• /assets/ – Visualizations from the analysis (img1.png to img8.png)
• /report/ – Final analysis report PDF
• README.md – You are here

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🧠 Sample Visuals

Forecast Comparison	Strategy Returns

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🙋‍♂️ About Me

I'm a graduate student in Analytics with a strong interest in time-series modeling, financial forecasting, and simulation-based evaluation of investment strategies.

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📬 Contact

Feel free to connect via LinkedIn or email me at: [email protected]