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The Minimum Variance Algorithm was compared to several standard optimization methods and algorithms in a recent set of tests done by Michael Kapler of Systematic Investor. Michael created a webpage for MVA to review some details of these tests and also to summarize some of the background information. We plan to release a whitepaper on MVA with some additional material in the coming weeks.
Below is a summary of testing done across multiple data sets contained in the MCA paper. We used a standardized score (the normsdist of the z-score) of the performance of each method versus other methods using three metrics: 1) sharpe ratio (higher is better) 2) volatility (lower is better) 3) portfolio turnover (lower is better). These factors were weighted equally to create a composite score. We tested across a wide range of data sets– stocks, ETFs and Futures.
The Minimum Variance Algorithm (MVE in the chart below) scored the highest of all methods across datasets- outperforming standard minimum variance and also the minimum correlation algorithm.
The following acronyms are defined below.
MVE: Minimum Variance Algorithm (MVA) in Excel
MCE: Minimum Correlation Algorithm (MCA) in Excel
MC: Minimum Correlation Algorithm (MCA)– Whitepaper/R Version
MC2: Minimum Correlation Algorithm 2 (MCA)
MV: Minimum Variance – standard minimum variance using a quadratic optimizer long only
MD: Maximum Diversification-standard maximum diversification using a quadratic optimizer long only
EW: Equal Weight
RP: Risk Parity- basic version inverse volatility weighting