Markowitz Portfolio Management

Learnt about the Markowitz Portfolio Theory which diversifies portfolio to earn a particular return while minimising the risk taken. Implemented the same in python by analysing the NIFTY 50 stocks dataset and evaluated the obtained portfolio against the Nifty 50 benchmark.

Assignment 1

• Processed the NIFTY 50 stocks dataset (Time period : 01/01/2016 - 31/12/2020) using Numpy and Pandas libraries.
• Calculated the volatility, CAGR(Compounded Annual Growth Rate) and Sharpe Ratio for the stock price series.
• Identified the top 15 stocks having the highest Sharpe Ratio. The aim was to look for stocks having high returns with low risk.

Assignment 2

• Initialised 600000 random weights(to have a large representative sample) for the 15 stocks and calculated the Expected Return(taken equal to the CAGR), Volatility and Sharpe Ratio corresponding to the weights.
• Identified the Max Sharpe Ratio of the portfolio from the random weights and plotted the Return and Volatility of the portfolios on a Scatter Plot using Matplotlib.
• Plotted the Efficient Frontier using SLSQP method.
• Taking the total amount as 10000000, obtained the amount of money to be invested in each stock and the number of shares held for them.

Assignment 3

• Tested the portfolio on time period 01/01/2021 - 30/06/2021.
• Scraped the required data from Yahoo using Pandas data reader.
• Evaluated the portfolio on the following metrics :
  1. Total Return on Portfolio
  2. Volatility
  3. Beta
  4. Sharpe Ratio
  5. Jenson's Alpha
  6. Sortino/Treynor Ratio

These metrics were compared with the benchmark, i.e. Nifty 50.