The seven key considerations to bear in mind for AA investments
After two major equity bear markets in the last decade, the benefits of diversification through strategic asset allocation have rarely been clearer.
Consensus suggests equity returns in coming years are poised for considerable volatility, so clients should consider a blend of assets to meet their objectives and help them smooth the performance of their investments.
The theory behind diversification is very simple – asset classes have different drivers of return so will perform differently at various stages of the market cycle.
By actively allocating across the different areas, it is possible to capture the best performers during peak periods and produce a portfolio with higher risk-adjusted returns than a single-asset equivalent.
Tip one: Diversification
Asset allocation and diversification can produce better risk-adjusted performance than single asset class portfolios.
No asset class can be the best all the time, and by using an allocation process based on sound economic theory and data, it is possible to capture this performance cyclicality.
In the past, investors were limited to a few asset classes but can now access areas such as commodities, hedge funds and other alternatives – on top of equities, bonds and cash.
Looking at Lipper data from 1999-2008, five different asset classes have been the best performer in the ten discrete years.
In GBP terms, commodities and funds of hedge funds have been the most successful, both top for three of the ten years, followed by two for global equities, and one each for UK non-government bonds and global government bonds.
This is because all these areas are driven by different factors, for example commodities have benefited from strong growth in emerging markets, China in particular.
Many commentators claimed diversification broke down in 2008 and everything fell together but that is actually inaccurate.
Correlations did spike in general but global government bonds performed well so diversification benefits still shone through although most assets were in freefall.
To use a cooking analogy, strategic asset allocation is about combining asset ingredients in the right proportions to produce a portfolio that delivers the expected client return recipe.
In order to do this, you must have accurate forecasts of how your ingredients are likely to behave.
Tips two & three: Future plans / history lessons
Use forward-looking returns in your process as relying on historic data can create huge distortions. It is also vital to base views on good economic and financial theory and ensure the data informing this is top quality
It remains standard industry practice to rely on historic data when deciding asset allocation despite all the warnings of how misleading past performance can be.
To give one example, Japanese equities produced annualised performance of over 23% in the 1980s and asset allocation done at the start of the 1990s would have been heavy in the region, if relying on these returns alone.
As most investors know, Japan has subsequently gone on to register two so-called lost decades, with annual performance in negative territory.
History is fine as a starting point but asset allocators must endeavour to understand why things happened as they did, and use this to inform forward-looking assumptions.
Tip four: Adjust for bias in your data wherever possible
Another issue with data lies in the impact of liquidity (or the lack of it) on estimates of risk – which is much more of an issue in less frequently traded assets.
Looking at UK direct property for example, volatility looks extremely flat at first glance despite a slight spike in the recent issues.
But this profile is effectively smoothed by the length of time it takes for market changes to be reflected in property prices. Unsmoothed, the volatility is as much as five times higher - so it is important to consider this in risk/return assumptions.
Tip five: Minimise impact of noise in your figures
In order to build portfolios, the standard is to use a method known as mean variance optimisation to produce return forecasts but the problem with this is extreme sensitivity to the data inputs.
Even a small variation in assumptions can have a massive impact on the end results and the chances of getting every input exactly right are slim.
A good process should involve resampling techniques to smooth out the impact of small variations on final outcomes and basically cut out the noise from the data used to determine allocations.
Tip six: Look to add further value through tactical asset allocation
While strategic asset allocation can add value, return forecasts for most asset classes are low for the next decade, so investors will need to be more active to boost performance.
One important area is picking the right managers as there is a huge differential between the performance of best and worst.
In US equities for example, the dispersion between the top and bottom 5% of managers got as high as 53% in 1999 while in hedge funds, the gap was a massive 122% in the same year.
This means there is substantial potential to add to low forecast returns by picking better managers.
Another way of boosting performance lies in tactical asset allocation to capture market trends. If an investor had run a basic 50%/50% split between equities and bonds, their average return in the 1993-2009 period would have been 7% per annum.
With perfect foresight – switching between the two areas at exactly the right times – this annual return shoots up to 32% a year.
The latter is obviously all but impossible but there is plenty of scope to add value between these extremes through tactical allocation, particularly as the choice of assets is now much wider.
Tip seven: Asset allocation is as much an art as a science
Much of the asset allocation world is data driven but manager judgement and skill is also vital. A successful process will involve looking at the wealth of information with an awareness of the real world and many of the more tactical decisions are about investment context.
Taking all the steps together, it is important to see how they have influenced asset allocation and the turbulent 2008 provides an interesting context.
Heading into last year, economic growth predictions remained fairly strong but the underlying data was signalling problems, with unemployment rising and many leading indicators weak.
This clear mismatch between optimistic expectations for growth and what the data was telling us threw up a major warning signal.
Another key area was the earnings side and we analyse sell side forecasts against history to see where expectations are.
As late as summer of last year, the analyst community was still expecting positive growth across the board and like with many economists, this optimism flew in the face of deteriorating fundamentals.
On the valuation side, these were already looking soft but not at the bargain levels required to justify taking on the additional risk given the backdrop.
All these factors, based on forward-looking data, supported a lighter equity weighting and that was before considering in the massive systemic risks from the banking sector.
Simona Paravani, global investment strategist, HSBC Global Asset Management
