PORTFOLIO OPTIMIZATION: TAA / FACTOR REBALANCING
1. TACTICAL ASSET ALLOCATION (TAA)
PIQ provides a classic TAA model that is best suited to optimizing between major market level asset classes and asset themes. A typical allocation procedure might look to optimize across multiple markets by region, by major characteristic such as big cap versus small cap and/or various fixed income classifications. This model is also useful for executing sector allocations across various major economic sectors such Energy, Technology, Financials and others. The model allows for the user to input their own forecast for returns and risks and the correlation matrix is fixed by the user’s selection of the conditioning period.
PIQ highly recommends using custom forecasts. Using forward looking forecasts solves one of the main drawbacks of many mean variance models. Relying solely on historical return data creates the risk of projecting unsustainable returns resulting in sub-optimal allocations and returns. As with most models in the PIQ tool set users are encouraged to use the system’s custom data capabilities.
The TAA model is a valuable tool to aid in the risk versus reward trade off associated with choosing suitable weights for specific risk levels. As with the other PIQ models a default forecast for returns is embedded in the system.
The TAA program requires users to provide a risk tolerance level. PIQ models use a risk scale between 1 and 100 and this number, once selected, is embedded into the optimizer’s utility maximizing equation. 1 is low tolerance, 100 the highest. PIQ’s ETF optimization tool suite provides a Risk Tolerance Questionnaire that results in a risk tolerance number.
2. FACTOR BALANCING OPTIMIZATION
Equity portfolios can be optimized to enhance or adjust specific fundamental investment characteristics. The optimizer can be used to tilt a portfolio in a controlled way to satisfy multiple constraints. An example of typical optimization objectives might be to rebalance a portfolio toward higher Business Cycle sensitivity while minimizing the portfolio’s forecasted volatility, all within certain minimum and maximum holding weights.