Figure 2.2 displays the distribution of how assets are approximately invested in the different sectors (as of October 2001). Long/Short Equity is the dominant sector with more than 40% of all AIS assets invested in this strategy sector (Equity Hedged 29% plus Equity Non-Hedged 16%), followed in roughly equal size (11–13%) by Event Driven, Relative Value and Global Macro strategies. Futures strategies and Equity Market Neutral each take about 5%, Convertible Arbitrage, Distressed Securities and Emerging Markets about 3% each. Convertible Arbitrage and Equity Market Neutral are Relative Value strategies, but they are counted sep- arately here. Other strategies like Short Selling, Regulation D and Equity Market Timing fall below the 1% range. Note that these numbers depend on the classifi- cation scheme chosen (in this case by Hedge Fund Research).
market hypothesis’ (EMH). The EMH comes in various forms related to different types of information available to investors: a weak form, a semi-strong form, and strong form.13 The EMH (in its strongest form) states that there is no price relevant information available to any investor that is not yet reflected in market prices. This implies that investment managers will not consistently generate alpha. Most investment and academic professionals do not hold the EMH in its strong form for true, but its weak and semi-strong form has more numerous supporters. The market efficiency battle between proponents of standard finance and their counter-parties (e.g. advocates of behavioural finance) is waged over the interpretation of price anomalies and consis- tent alpha generation in the main equity, foreign exchange and fixed income markets.14 But it is rather undisputed that the overall global spectrum of financial markets presents varying degrees of efficiency. The major foreign exchange markets, G7 Government Bond markets as well as the large capitalization segment of the major international equity markets are generally considered to be quite efficient, while real estate and private equity markets generally display a much lower degree of efficiency, i.e. superior information or skill in these markets pays off in above average returns.
Hedge fund managers operate in security markets with various efficiencies and are thus in some middle position between public equity and bond portfolio managers on the one side and private equity or real estate experts on the other side.
For a further understanding of the ‘battle of alpha’, it is important to assess the basic assumptions of common asset pricing models. The CAPM is based on the following (and some other) important assumptions:15
■ Investors choose investments according to a mean variance framework (i.e.
they measure reward by the mean return and risk by the variance – or
standard deviation – of returns). Investors are generally risk averse and have a quadratic utility function with respect to risk.
■ All investors have the same forecast of expected return variances and correlations. This leads all investors to hold the same risky market portfolio (with varying weights relative to the risk-free part of their portfolios, depending on their risk profiles). Consequently, there is only one source of risk for which investors are rewarded, which is the ‘broad market risk’ (as measured, for example, by an equity index).
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■ Trading is frictionless, i.e. there are not transaction costs, taxes, etc. Investors can (and will) sell short securities without any restrictions.
The first assumption becomes questionable when the probability distribution of investment outcomes is skewed (i.e. non-symmetric) or leptohurtic (i.e. exhibits ‘fat tails’). In this case, the conventional measure of risk, namely the standard deviation, provides an imprecise basis for risk measurement. Some AIS – comparable to option strategies – have non-symmetric return distributions. The fact that investors have a preference for positively skewed outcomes and an aversion against negatively skewed outcomes is not captured by a risk measure that weighs each part of the distribution identically. For many AI strategies, the right amount of return for the given exposure to risk cannot be unambiguously determined in the framework of CAPM.
The second point is plainly false, as investments, particularly those in AIS, are subject to numerous other risks beyond broad market risk. The ‘Arbitrage Pricing Theory’ (APT) provides a more general framework,16which allows the inclusion of more factors to explain asset returns. The crucial question of which particular factors to include remains a matter of dispute.17The numerous extensions of the one-factor CAPM usually include additional fundamental factors such as firm size or value factors (price-earnings ratio, price-book value, etc.) or macroeconomic factors (such as unexpected inflation or credit spreads).
Considering only market risk leads to the incorrect conclusion that superior returns are always the result of unique manager skills or superior access to infor- mation. But the reality is that investors who are willing to assume risks beyond market risk (e.g. liquidity risk, credit risk, event risk) can earn additional returns unconditional on superior information or skill. The investor might incur losses from bearing one of these risks during some periods, but over time the returns should be sufficient to make the investment profitable. These returns are called
‘risk premiums’. Multi-factor models (like APT) allow capturing these other dimensions of risk besides overall market risk and they include the corresponding
‘factor risk premiums’ in the modelling of asset prices.
The third restriction is at best only partly true. Transaction costs are indeed an important factor for investors to consider. Further, many investors are constrained in selling short securities. This can create inefficiencies in financial markets that
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Hedge funds try to take advantage of (see the discussion of ‘Long/Short Equity’
and ‘Short Selling’ in the next chapter).
Effective AIS risk management requires a sound understanding of how returns are generated and what risks come with these return sources. The limitations of quantita- tive measures of risk-adjusted returns have to be considered (e.g. as in the case of skewed return distributions). As already mentioned, it is generally useful to distinguish the following two (real) sources of returns (besides ‘luck’): risk premiums and man- ager skill. These two sources are by no means mutually exclusive. It takes skill to capture a premium effectively and to manage the related risk. By the same token, some strategies are based largely on a manager’s skill in forecasting price moves or detecting relative mispricings, which is often performed with the help of quantitative valuation models. One can argue that these strategies earn a ‘complexity’ (risk) pre- mium. Further, price anomalies and apparent Arbitrage opportunities are often related to different risk premiums.18Despite certain overlaps and ambiguities that come with this classification scheme, distinguishing ‘risk premium strategies’ from ‘pure skill strategies’ nevertheless provides a good framework for an analysis of the AIS Universe.