Setting up a basic HALO optimization requires a list of asset tickers, their min and max constraints, and expected returns. Also at least one user specified category designation is required. Below is a short example:
SPY 7% 40% 9.11% Equities
PBP 0% 15% 8.53% Equities
USMV 5% 15% 9.05% Equities
VTI 5% 25% 9.35% Equities
IJH 5% 20% 9.55% Equities
VB 3% 15% 9.81% Equities
VEA 5% 12% 8.69% International
VEU 5% 20% 9.21% International
EEM 1% 11% 10.07% International
JNK 3% 15% 6.03% Bonds
BKLN 0% 8% 3.61% Bonds
AGG 1% 9% 2.32% Bonds
Generally, it is advisable to keep the sum of the individual asset minimums below 50%, and the sum of maximums above 200%. This provides the HALO optimizer the freedom to create a wide range of optimized portfolios with different risk/reward trade offs.
The above example is a very basic configuration. In order for asset managers to specify asset-class constraints, it is necessary to tell the optimizer that the “string” is a user-defined category. Currently this is done with a leading gastritis (*):
*Equities 25% 85%
*International 10% 30%
*Bonds 15% 45%
The above config specifies that Equities must comprise a minimum of 25% of the investment portfolio and a maximum of 85%. As with the individual asset constraints, it is advised to provide reasonably wide latitude to the optimization algorithms to produce a diverse set of optimized portfolios.
By default, the HALO Optimizer will produce a set of portfolios optimized to:
1) minimize:
a) semi-variance, σd (the default)
b) –OR– annualized standard deviation of total return, σ
2) maximize expected return, E(R)
The default time series used for computing σ and σd is end-of-month total-return deltas for the previous 36 months. (This requires 37 months of total-return data for each security.) The time period can be customized to use, say 60 months worth of data in the analysis. HALO also supports using weekly closing data or even daily closing data — however I generally recommend using monthly data for a variety of reasons. First, it speeds the computation. Second, monthly data captures multi-day and multi-week trends, correlations, and specifically low-correlation asset optimization. Third, monthly data is closer to the sampling period of a “typical” high-net-worth retail investor. [That said, a case could be made for using quarterly data — which is also supported.]
Frequently HALO clients want to model newer securities that do not have 37 months of historical data. For example, min-volatility ETFs such as SPLV, USMV, and EEMV are popular ETFs that are less than 3 years old. The HALO software suite has utilities that can statistically back fill the missing data. The configuration of the statistical back-fill process is beyond the scope of this blog post, however it is an important and popular HALO Optimization Suite capability that so far has been used by all of Sigma1’s clients and beta testers.
Occasionally, Sigma1 clients and beta testers have had in-house funds that do not externally report their price or total return data. For in-house funds, HALO can read client-supplied total-return data. Naturally, HALO can include stocks, bonds, commodities, futures, and other assets with historical data into the portfolio optimization mix.
Balhiser Financial 