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Diverging paths

Keith PopplewellIam now well advanced with this series of articles discussing various attributes of the main asset classes and projections of their future behaviour.

In my last article, I delayed a closer examination of the equity market in favour of an overview of the ways in which these asset classes can profitably be combined in a portfolio to maximise potential investment returns while minimising overall risk to the investor.

Specifically, I noted three concepts in this respect:

  • Diversification and correlation.
  • Projecting investment returns.
  • The measurement and use of volatility.
  • I discussed methods by which correlation data can be used to help restrict or reduce the level of portfolio risk. I also summarised a key feature of this series of articles, which is the ways in which realistic investment growth rate projections can be arrived at to ensure that this reduction of risk is not matched by a reduction in portfolio returns.

    I would like to conclude these issues by noting that there will be many instances where it will be appropriate to enhance or reduce the overall risk/reward profile to either aim for increased investment returns (even at the cost of higher risk) or reduce risk further (even at the expense of potentially lower returns). Important changes can be made to a core portfolio by considering the relative volatilities of asset classes, sectors and funds.

    This is most conveniently achieved by noting the measurement of volatility of each proposed investment in the form of standard deviations. This is the subject of a separate, more detailed briefing note (The measurement and uses of volatility in portfolio planning) at

    Standard deviations are a measurement of the past volatility of an investment. Basically, the higher the quoted standard deviation, the higher the past volatility of that fund or asset class. These measurements should by no means be taken to indicate a better or worse investment opportunity as differing circumstances and requirements require different risk profiles within a recommended portfolio.

    Put simply, if an adviser has determined a broad asset allocation model for a particular portfolio which, as noted above, could and should be guided by the principles of correlation and the appropriate targeting of investment returns, how might standard deviations be used to assist the selection of appropriate funds and sectors within each asset class?

    First, a comparison of the standard deviations of asset classes and sectors will reveal which of these potential constituent parts of a portfolio have had the highest and lowest volatility and by how much. Over the past five years, for example, equities have demonstrated the highest volatility – some four times higher than investment property, the asset class with the lowest volatility, apart from cash.

    Perhaps more surprisingly, the average managed fund has had a volatility only 20 per cent less than equities and even defensive managed funds have only managed to halve the average equity volatility and are therefore still twice as volatile as investment property.

    For those who are not familiar with the concept of standard deviations, higher numbers indicate higher volatility. The statistics for the main asset classes are informative, to say the least (see Table A).

    Beyond this comparison of asset class volatility, further important portfolio planning messages can be gleaned from a comparison of the respective volatilities of individual funds within each sector. Certain funds have demonstrated markedly lower volatility than their sector average, due for the most part to their management style, and could therefore be particularly considered in situations where it is desired to reduce the risk profile of the portfolio further, perhaps without reducing the likely investment returns.

    Conversely, other funds have demonstrated a much higher volatility than the sector average and might be particularly considered where an investor might aim for above-average investment returns and is willing and able to wait to crystallise profits when his holdings are doing particularly well. Note that funds with higher standard deviations have typically enjoyed higher peaks and lower troughs than their sector colleagues with lower volatility ratings. A few examples can be used to illustrate this (see Table B).

    If an adviser is seeking to reduce volatility in their use of this sector, they might be tempted to favour Fund A, subject to an assessment of its past and potential performance. If they wish to increase volatility, perhaps in a commitment to pound-cost averaging for regular investments, they might favour Fund C. Obviously, Fund B has displayed volatility at exactly the average for this sector.

    It should be understood that standard deviations are an historical measure and do not give a direct and precise indication as to whether certain assets or funds will demonstrate the same volatility in the future. But volatility which has been significantly higher or lower than average generally has not happened by accident. There will usually be some underlying reason, typically fund management style or the precise nature of assets held in the fund, which can help to give a clear indication as to its future behaviour as regards volatility, though not investment returns.

    Before summarising the stages in this suggested portfolio planning strategy, it must again be stressed that there are many techniques which may be used in portfolio planning and I am not suggesting that, by omission, any of them are less useful than the strategies and concepts on which I have concentrated in these articles.

    The three techniques discussed above may be used independently of each other but a logical sequence of their use follows the sequence of this article. First, the use of correlation factors to ascertain combinations of assets which might be expected to produce the highest reduction in portfolio risk. Second, the identification of appropriate expected investment returns from different asset classes and sectors to help ensure that a suggested portfolio not only reduces risk but also maintains or increases overall returns. Third, the use of standard deviation statistics to fine tune the portfolio to the exact needs and circumstances of a particular portfolio.

    By the end of this process, the adviser knows they will have constructed a portfolio in which the suggested overall risk profile and projected investment returns can be more confidently assessed, predicted and justified.

    The ultimate aim is to arrive at an asset allocation structure which approaches the most efficient combination between an acceptable level of risk and desired level of return. These combinations are increasingly becoming known as the efficient frontier between risk and reward, this being a topic for a future article, following a closer examination of the recent behaviour and imminent prospects for the two remaining asset classes I have not already discussed – cash and equities.


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