This repository explores portfolio optimization and factor investing strategies using quantitative methods. The project focuses on constructing an optimal portfolio by selecting assets based on risk factors, utilizing statistical techniques for covariance matrix estimation, and implementing portfolio optimization models.

• Data: The data folder contains downloaded trading data used for analysis.
• Documentation: The doc folder holds the PDF report summarizing the research findings.
• Research Notebooks: The res folder contains Jupyter notebooks (ipynb) capturing the research process and analysis.
• Source Code: The src folder includes Python scripts for various components of the project.

1. Covariance Matrix Estimation:

   • Implemented the shrinkage estimator proposed by Ledoit-Wolf (2003) to address the instability of the sample covariance matrix.

2. Portfolio Optimization Models:

   • Explored Mean-Variance Optimization, Max Sharpe Ratio Optimization, and the Black-Litterman model to derive optimal portfolio weight allocations.

3. Factor Exposure Analysis:

   • Investigated factor exposures of securities to target factors, providing insights into the diversification and risk management strategies.

4. Future Enhancements:

   • Recommendations for future improvements include incorporating international markets, refining optimization methods, and exploring advanced topics like individual investor uncertainty.

|-- data/
| |-- [Downloaded Trading Data]
|
|-- doc/
| |-- [PDF Report]
|
|-- res/
| |-- [Research Notebooks]
|
|-- src/
| |-- [Python Scripts]
|
|-- README.md

Feel free to explore the contents of each folder for detailed information.

1. Download Data:

   • Obtain trading data and place it in the data folder.

2. Run Scripts:

   • Utilize Python scripts in the src folder for analysis and optimization.

3. Explore Notebooks:

   • Delve into Jupyter notebooks in the res folder for a detailed walkthrough of the research process.

4. Review Report:

   • Refer to the PDF report in the doc folder for a comprehensive summary of the findings.

Feel free to contribute, raise issues, or provide feedback to enhance the project further.