The chart above is a representation of risk and return in US securities markets over calendar year 2017.
The coloured squares show benchmark results for well known indexes. The green and red circles show two different hypothetical examples of portfolio results. The black line is a best fit line to the 4 US domestic benchmark squares on the risk and return chart.
The green circle is above the line, but the red circle has a higher return. Which was better? Which manager is more likely to achieve durably good returns for the long term?
Chasing returns is very hazardous without effective tactical systems. High returns could just reflect high risk.
Finding return relative to risk above the line is very rare, and could be a clearer signal of superior risk management.
Does your investment manager show and measure risk as a key factor in allocation and reported returns?
Search for the best risk managers and then adjust their models to a suitable level of risk for you.
Here is a broad selection of directly managed low cost investment models with reportable risk factors.
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In the chart above, the red circle portfolio achieved a higher return than the green circle portfolio, 10% rather than 8.5% . However, looking at the the best fit line from the domestic benchmarks, the green circle portfolio achieved something very rare. It beat the domestic benchmark proxy, by a clear 3%, while the red circle portfolio had over 80% more volatility and fell far below the domestic benchmark proxy, by around 6%! That’s a great deal to give up relative to a benchmark portfolio with similar volatility. Could it be high expenses, or inefficient risk taking? Shouldn’t you be able to find out?
It would be well worth spending some time finding out why there is a difference between your portfolio return and the return to risk benchmark line. Here are a few things worth understanding.
Benchmarks are just a first guide, they only go so far
While benchmarks provide a quick first take, they don’t go far in providing a full assessment. Take an extreme example. No one is likely to thank an investment advisor for a great job in losing only 40% of their capital, because a benchmark was down 50%! So any direct return comparison between benchmarks and portfolio returns fails to take account of risk management and portfolio diversification, amongst other factors over the long term.
Returns Below Benchmarks is the usual result for 3 reasons.
First of all, costs clearly play a key role, particularly for mutual funds. It is important to point out that returns are typically lower than benchmark proxies as shown by a Dalbar table below. Investment costs play a significant part but poor timing usually also has a part to play.
Secondly, without a repeatable and systemic approach it is very hard to perform well consistently. There are multiple factors which influence us all, including the most seasoned investment managers.
It really takes a very long track record to get an understanding of the effectiveness of an asset manager, and even then, market behaviour and investment techniques are ever evolving, and a good manager will be consistantly trying to improve his abilities and techniques. How has management evolved and how will this continue into the future?
How much of the returns were skill? How much of the returns were based on a single idea? Being high exposed in a bull market, without risk management produces high returns, but what happens if a bull market ends? Very few investors do durably well over an entire market cycle.
Thirdly, forecasts are not reliably correct. This means that successful long term investing usually involves systemic approaches based as far as possible on current known data and market behaviour, but also on loss mitigation. Is there a clear explanation of what risk management is in place if forecasts turn out wrong or market conditions suddenly change?
The best assessment of investment management is not just returns
For all these reasons, it is especially worth repeating, after an extended equity bull market, that chasing returns can be highly dangerous. High returns may show superior asset management, or reckless risk taking in fragile and dangerous conditions. Further assessment is crucial to avoid the latter.
http://chrisbelchamber.com/investmentmanagementcompleterethink1/
What is very rare is high return to risk portfolio returns above benchmarks
In order to generate high return to risk outcomes above benchmarks, capital has to be managed highly efficiently and effectively. No passive allocation to low cost benchmarks could have replicated those returns without taking more risk, so it is clear sign of superior risk management. Furthermore, an excess return over benchmarks has in addition exceeded any costs. As shown above this is an additional very high hurdle, which reflects either/both low costs or very effective risk management.
This is why the note above advocates using RVAR (Repeatable Volatility Adjusted Returns) as the best assessment for finding asset managers.
Does your investment manager show and measure risk as a key factor in allocation and reported returns?
Investors should consider finding asset managers that demonstrate high return to risk results and then work with them to find the most suitable risk profile. Then the returns are most likely to be optimal for the investor.
Portfolio performance in 2017
I fully embraced tactical methodologies for 2017. On the Belpointe platform the ability to develop and execute tactical models has completely changed what I able to do. Many advisors simply do not have a platform that enables this type of management to this degree. The extremely low fixed commision structure, combined with the Orion trading platform is a near complete transformation in terms of capability.
Over the year I have developed 10 tactical models (2 models are combined in TSP.Gov) to provide a universal set of tactical trend aggregation strategies (combinations of tactical models) for any investor. While it has involved a certain amount of transition into the new models, it has already had a very significant effect, I believe, and provides a new level of stability in accounts, as well as measurable efficiency to fit suitability requirements for any investor.
It is possible to measure risk and return on each model to check how I measure up to conventional benchmarks. Once a constant measure is in place I can constantly work on this metric, to improve results, and it becomes possible to select portfolio allocation for any account or household with far greater precision and variety.
My constant goal is to produce the maximum possible Repeatable Volatilty Adjusted Returns, as described in my August 2017 notes, for every model I manage.
Investment Management: Why You Need A Complete Rethink. (short version with links)
Then it is solely a matter of selecting the appropriate allocation for each client from any combination of the 9 strategies.
Here is a broad selection of directly managed low cost investment models with reportable risk factors.
Clients will receive a grid of all the information I currently have on the models. Non clients may receive further information on application.
Model descriptions
LOW RISK Initiated Upside volatility Allocation Guide
Cycle Dynamics Passive 1/1/17 4 12.5%
Cycle Dynamics Bond 1/1/17 5 12.5%
MODERATE RISK
Tactical Diversified High Income 2/1/17 7 15%
Cycle Dynamics Asset 1/1/17 7 15%
TSP.GOV Models 1/1/17 7
Quant Agora 6/1/17 9 20%
HIGH RISK
Equity Aristocrats 1/1/17 12 15%
Venture Tech 3/9/17 15 5%
Precious metals 12/6/17 20 5%
For tactical models volatility refers mainly to upside volatility. Downside volatility is lower as risk management kicks in, and is usually better expressed through a drawdown calculation. As a guide to volatility, the S&P 500 index currently has a volatility of 10 on the measure used above.
Model options and construction
Cycle Dynamics Passive
This model is a variation on the All Weather system designed by Bridgewater in the 1970s. Over 40 years of data shows that this static allocation has lower long term volatility and higher return to risk than almost any other static asset allocation model. The performance of PRPFX shows similar results since 1981.
CD Passive takes this a step further by adjusting the allocation to only holding the All Weather assets when they are in long term uptrends. This is a similar but different version of the model shown in this link.
Cycle Dynamics Bond
This model rotates through different ETFs through the whole economic cycle. No matter which phase of the economic cycle there are good choices for different types of bonds. This approach could solve the asset allocation problem of investing in bonds at the lowest interest rate levels in history, with little upside potential. Investors can have greater peace of mind about what could happen if a bond bear market could materialize.
A full fact sheet is available on application, showing results back to the beginning of 2011.
Tactical Diversified High Income
This model looks at the whole universe of high income assets, from corporate bonds, high income stocks, and fixed income products. Here is an outline.
Income Portfolio. Higher Yield AND Lower Volatility Than S&P 500
