Markowitz Portfolio Theory and Random Portfolio Simulation

Summary

This introduction explains how Markowitz portfolio theory uses expected returns, volatility, and asset covariances to evaluate diversified allocations. Because portfolio risk depends on how assets move together, imperfect or negative correlations can reduce total risk. The efficient frontier describes portfolios offering the highest expected return at each risk level.

The article demonstrates a simulation approach using historical daily crypto prices: estimate annualized returns and volatility, generate random asset weights, calculate portfolio measures, and select the simulated allocation with the highest Sharpe ratio. It reports an example allocation and performance figures for a basket of six cryptocurrencies. Those figures are outputs from the stated example, not evidence of future performance. The method is limited by its historical estimates and random search; the sample does not establish that the selected portfolio is truly optimal or robust to changing market conditions. The code’s treatment of covariance and risk merits careful review before relying on its results.

Key ideas

• Portfolio risk depends on asset covariances as well as each asset’s individual volatility.
• Diversification can reduce risk when assets do not move in lockstep.
• The efficient frontier maps allocations with the highest expected return for each risk level.
• The example searches random crypto allocations and selects the one with the highest Sharpe ratio.
• Historical estimates and a random search do not guarantee a robust or truly optimal allocation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.