Trading Volatility for Returns
Obsidian’s proprietary machine learning technology systematically allocates capital between assets within a portfolio. This facilitates a unique volatility suppression methodology which in turn improves returns leading to much better Sharpe and Sortino Ratios respectively when compared to a benchmark. In addition Obsidian have developed a methodology whereby, for a given portfolio, volatility can be traded off with returns. This means that for roughly the same Sharpe and Sortino Ratio clients can express a express a preference for the volatility the portfolio might experience with a commensurate return. This is particularly relevant given the current state of the market.
The graph above shows the Average Annual Return against Annual Volatility for a set of actively managed portfolios based on the HLAL ETF using Obsidian’s Portfolio Trading System. The intersection of the two measures are the Sharpe and Sortino Ratios respectively. As can be seen when moving from the Aggressive through the Passive into the Conservative Portfolios the Annual Volatility falls as does the Average Annual Returns. However, the gradient of a line joining the points has approximately a constant gradient, which means effectively that the Sharpe and Sortino Ratios are fairly consistent. This means that clients can specify a Volatility tolerance for a given Ratio. The Average Sharpe and Sortino Ratios are 2.70 and 4.10 respectively across the sample. The figures for the HLAL are 0.90 and 1.0 respectively.
The portfolios are unleveraged and the results are gross of fees, profit share and commission.