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A large bond allocation has long been the staple diversification tool for RIAs trying to manage market risk in their equity portfolios—especially for clients nearing retirement. In high yield environments, this is an excellent tool. RIAs can either hold the bonds to maturity and benefit from the high fixed income yield, or they can sell the bonds as yields revert to the mean and decrease thereby allowing for capital appreciation on the assets. In a high yield environment, bonds are great assets by themselves, independent of the diversification benefits they provide.
In a low yield environment, the value proposition is the opposite. The low yields on the asset don’t justify the AUM fees the RIA is charging on them. Furthermore, the high ordinary income taxes owed on bond gains makes the net after-tax, after-advisory fee yields for the client abysmal. In the current low-
yield environment we’re in, after the client pays taxes and the RIAs fees on these assets, the client actually makes significantly less for owning the asset and taking all the interest, credit, and liquidity risk than the RIA earns for just managing them.
After Tax, After Advisory Fee Client Bond Yields vs RIA AUM fees:
In a low-yield environment, RIAs are making 3-5 times more money managing clients’ bond assets than the clients are actually making owning the assets and taking all the interest, credit, and liquidity risk.
On top of this if, interest rates revert to the mean and increase, clients will lose money on these bonds that will take them years or even decades to regain!
Modeling different composition of equity and bond portfolios show us that clients are better off utilizing a 100% equity portfolio than the traditional 60% equity/40% bond portfolio split.
As the table above shows, utilizing bonds as a means of portfolio diversification no longer makes mathematical sense in this low-yield environment for HNW clients.
The simulation results show a number of key findings:
Note that the above modeling assumes that the bond yields are normally distributed. If we were to account for the fact the interest rates are at historic lows and over the long-term there will be mean reversion to higher yields (meaning larger upfront bond losses), the percentage of successful simulations for the 60/40 portfolio would have been significantly worse.
The impact of this mean reversion is not currently being accounted for in most financial modeling software, and further limits advisors ability to understand the true risk posed by allocating a large percentage of client assets to bonds in a low-yield environment.
Effect of Mean Reversion in a Low-Yield Environment:
In a low-yield environment, upfront bond losses are more sensitive to an increase in interest rates. A small mean reversion of an increase in 1% can take bond portfolios years or decades to recover from.
The above table shows how long it would take of investing in bonds of various terms at the higher interest rate of 3% to make up for the initial upfront loss absorbed.
This mean reversion is not being accounted for in current financial modeling software thereby further limiting advisors ability to understand the true risks of a bond-heavy retirement strategy.
In order to replace the value that bonds have traditionally held for portfolios in higher yield environments, RIAs need to either focus on retirement plans and investment vehicles that minimize
taxes and interest rate/credit risk on those bonds or utilize other assets with low correlation to market and interest rate risk that provide better risk-adjusted returns.
If they don’t implement better strategies, clients will soon realize that they’re better off only having their financial advisor managing the equity portion of their portfolio.
Or even worse, they’ll take their entire portfolio to another financial advisor who is willing and able to implement better retirement strategies for them.
Want to learn more?
Read our article on 6 Financial Advising Strategies to Increase After-Tax, After-Advisory Fee Bond Returns for HNW Clients in a Low-Yield Environment
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