Portfolio-Optimization
1) Introduction
Portfolio Optimization is the process of selecting the best portfolio (asset distribution), out of the set of all portfolios being considered, according to some objective. The objective typically maximizes factors such as expected return, and minimizes costs like financial risk.
Modern Portfolio theory states that assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of diversification in investing, the idea that owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. It uses the variance of asset prices as a proxy for risk.
In addition to this, using Monte Carlo Simulation we are able to run N different simulations for different portfolios with different security combinations. This aligned with the efficient frontier, te set of optimal portfolios that offer the highest expected return for a defined level of risk we are able to find the portfolio that maximizes the expected returns.
2) Main objetives of this project:
- Extract data from Yahoo Finance;
- Analyze data using different statistical metrics;
- Show how to simulate thousands of portfolios using the same assets;
- Show how portfolio weights can be optimized for either volatility or Sharpe Ratio;
- Build the Markowitz efficient frontier and Capital Allocation Line
3) References for this project:
[1] Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.2307/2975974
[2] Detemple, Jérôme B., et al. “A Monte Carlo Method for Optimal Portfolios.” The Journal of Finance, vol. 58, no. 1, 2003, pp. 401–46. JSTOR, http://www.jstor.org/stable/3094492
[3] For the Integration of the Monte Carlo Simulation: https://quantpy.com.au/category/monte-carlo/