The increasing use of pooled funds has brought about a greater focus on analytical tools to assess risk and historic returns. As a result, statistical and technical analysis techniques used elsewhere in the investment industry are now being applied to funds.
Statistical analysis is based on as-sumptions of historical data, which can be useful in assessing the variance of markets, but are not in any way predictive.
The most common measure of total risk used in the analysis of securities and portfolios is the ex post standard deviation. When used to measure the risk involved in a portfolio over a given period, standard deviation is a measure of the dispersion of returns around the average return over that period. This measure has an intuitive appeal; if the returns had a high standard deviation over, it means there were large swings in the returns, indicating uncertainty about the underlying price of the securities in the portfolio. Alternatively, if another portfolio had a low standard deviation over the same period, it would be possible to say that the risk associated with the assets was lower.
As an indicator of risk, the standard deviation fails the test for the same reason as most other statistical tools, in that it is backward looking. Nonetheless, using this method, it is possible to generalise that returns on equities have a higher standard deviation and real-ised return than government bonds.
The capital asset pricing model (CAPM) was developed in the early 1960s from Modern Portfolio Theory and although much maligned, remains perhaps the most popular tool for quantifying and measuring risk in the US investment industry. Its main at-traction is the simplicity of its predictions, though it is has a simplistic view of how financial markets work.
Analysing individual funds’ risk factors involves measuring how sensitive the fund is to market movements. This sensitivity is called beta. The beta coefficient is the key parameter of the CAPM. The measure of the covariance between the return on the portfolio and the return on the market indicates the extent to which the two move together. If the return on portfolio covaries ex-actly with the market, it will have a beta equal to one, or put another way, the fund will provide a return comparable with the market index.
A beta of less than one indicates the portfolio is less risky than the market, while a portfolio with a beta greater than one implies that its return varies proportionately more than the return on the market. For example, if the market is up 10% over the risk-free rate, then- other things held equal -a stock with a beta of 1.5 will be up 50% over the risk-free rate. Higher than average beta is either an indication that the manager is adding value or losing it, relative to a benchmark.
In practical terms, a pension fund with relatively mature liabilities may wish to form a portfolio of equities with a CAPM beta of less than one, while the trustees of a less mature fund may be inclined to hold a portfolio with greater inherent risk and a higher beta.
By measuring risk and return, it is possible to calculate risk-adjusted returns, for which there are a number of recognised measures. The most commonly used is the Sharpe Measure (or Ratio), which uses an estimate of the total risk of a portfolio to calculate excess return to volatility, using the standard deviation as its measure. The Treynor Measure uses beta as its measure of volatility. It takes into account only systematic risk, whereas the Sharpe Ratio considers total risk.
Correlation is another statistical measure used in fund management. The correlation coefficient determines the relationship between two variables. Its values always lie between -1 and +1. A value of +1 means that two variables will always move in perfect unison while a value of -1 means that their movement are exactly the opposite of each other.
Drawdown refers to the maximum negative month-on-month performance over the performance period specified. That is to say, maximum loss defines the greatest percentage loss in any one month. Where performance is always increasing, the maximum loss is set to zero.
One proprietary risk/return system commonly used by fund managers to assess their own portfolios is Barra. Barra programs use predicted rather than historical beta and are based on a firm’s financial statements and business fundamentals, adapting to changes in the market. Fund managers admit though that the measure is more useful for large caps than small, and fairly ineffectual for emerging markets investments. We have not seen any Barra system that covers funds.
Technical analysis does not, of itself, provide an effective view of a fund. The risk measures are not fool-proof. For example, beta only works where the source data is naturally comparable. And style categorisation of funds is not at the level of sophistication that would make it worthwhile relying on statistical analysis alone. Nor are the benchmarks that focused. In the US, you have, for example, the S&P 500 ‘value’ index and S&P 500 ‘growth’ index, whereas on an international scale, we haven’t developed the categorisations to that level. If one fund is ‘mid-cap value’ in style (though this will not be apparent from its name), it will often be compared with an outright growth fund.
Statistical analysis will only take you a short way down the road to assessing individual fund risk and likely future re-turns. If you are comparing individual stocks within a market sector, there is some basis for the comparison. But if you take funds such as Invesco Continental European Fund and Baring European, they are very different animals. And it is only by doing the qualitative work that you would discover this.
Richard Newell is with Forsythe Partners in Croydon