As a report by analysts at Morgan Stanley puts it, “ for a long time, hedge funds had an anti-mega-cap bias, underweighting these names in favour of stocks less than $100 billion in market capitalization”. Lurking beneath this analysis is a recognition that the market for very large company stocks is probably fairly efficient, and certainly very liquid – how can a hedge fund or mutual fund manager possibly hope to outperform?
This consensus has though come under greater scrutiny in recent years – as we’re about to discover analysts at Morgan Stanley argue that a sizeable amount of alpha can in fact be generated by selectively picking mega large cap names using well known ‘factors’. These quantitatively derived “factors” could include everything from value considerations through to measures of volatility.
This factor driven approach is also powering the ‘smart beta’ revolution in the world of exchange traded funds or ETFs. In this growing universe of funds, quantitative driven measures are used to work out why certain types of stock have outperformed the benchmark in the past – these could be risk based factors such as low beta stocks, size, value considerations or even the ‘quality’ of a stock.
But which factors matter the most when it comes to working which large/mega caps to buy for a fund or portfolio? The most systematic attempt to answer this challenge has come from the French business school EDHEC. Their Scientific Beta project (at www.scientificbeta.com) has identified a wide range of factors using a 40 year slug of data from the US equity markets. This analysis focuses on the largest 500 stocks in terms of market cap – EDHECH have then chosen to focus their analysis on four main factor strategies:
- Low volatility based on weekly returns over past two years
- Value – book to market ratio
- Momentum – cumulative return over the past year relative to wider market
- The size effect – smaller companies based on market cap tend to outperform over the longer term. In the EDHEC series of factors this involves a mid cap rather than small cap bias
But EDHEC also adds in another crucially important layer of ‘stock selection’ – rather than looking to eliminate certain stocks using a factor bias, they look to achieve maximum ‘diversification’ between different stock “risks”. Five main diversification strategies feature in the EDHEC Scientific Beta approach, all based around an alternative approach to simply market cap weighting an index:
(1) Maximum Deconcentration strategy corresponds to an adjusted equal weighting strategy which takes into account implementation constraints.
(2) Diversified Risk Weighted strategy aims to achieve equal risk contribution from all stocks under the assumption of identical pair-wise correlation across stocks, and it is the same as inverse volatility weighting. The strategy is implemented with upper and lower bounds on individual weights to avoid a highly concentrated portfolio.
(3) Maximum Decorrelation aims at minimising portfolio volatility under the assumption that stock volatilities are identical and only correlations are taken into account. The strategy is implemented with upper and lower bounds on individual weights to avoid a highly concentrated portfolio.
(4) Efficient Minimum Volatility attempts to minimise the portfolio volatility by using an endogenous concentration adjustment within the optimisation procedure.
(5) Efficient Maximum Sharpe Ratio aims to maximise the Sharpe ratio (the risk adjusted performance) while imposing concentration constraints in the form of lower and upper bounds on stock weights.
These two strategies – one built on individual factors, the other on diversifying the mixture of stocks – are then combined to produce a number of indices. Returns for these indices have also been back tested using US data for the 500 largest stocks by market cap over the period between the end 1972 and end 2012.
