US large-cap equities constitute the most liquid and competitive segment among developed equity markets. I often meet investors who believe that it is arbitraged to such an extent that active management is bound to fail in such a stock universe and therefore opt for market cap indexation. However, greater liquidity does not mean that there are no structural biases in this stock universe. Psychologists have demonstrated over and over again that group thinking often leads to sub-optimal solutions in a very persistent manner. Factor investing is a simple way to profit from such persistent biases on the S&P 500. Here is the result.
US large caps constitute half the market capitalisation of developed equity markets and one third of all equities, including small caps and emerging markets. The subject of out-performing US large caps is thus difficult to avoid in any discussion about global asset allocation. Asset managers have noticed – Morningstar has registered more than 1500 funds investing in this specific stock universe – that there are actually three times more funds than underlying stocks! Yet, there is no market for which the debate between active and passive investing is more acute. Despite the numerous actively managed funds available, some ETFs passively replicating the market capitalisation S&P 500 index have reached incredible sizes. In Europe, in particular, I meet many investors who have given up using active managers for US large caps, on the premise that stock picking is too difficult in that stock universe, where abundant liquidity is believed to allow for easy arbitrage of any good investment idea.
Yet, psychological studies on the way groups take decisions tell us that the so-called ‘group think’ is often inefficient. Most often, groups actually make worse decisions than the average of their individual members. Social interactions, trends, endowment effect or availability biases, among others, trick groups into exaggerated decisions, around the position of some opinion leaders. The notable exception is when the decision being taken is ‘eureka like’, i.e. when there is an obvious better answer that, when discovered, is easily accepted by anyone as being better. But equity prices are rarely of that type. Most observers will notice that markets tend to focus successively on specific events, seen as drivers for some time, although their true impact on equity prices can be questioned. In the US, presently, discussions about the effect of Trumponomics are all that matters, for instance, but are they really so important?
Smart beta, and more specifically multi-factor investing, takes another view. By using algorithms to select stocks, their aim is to focus on information about stocks that have been proving over decades to be capable of generating out-performance in a persistent way. One example is our Diversified Factor Investing (DEFI) strategy which relies of four factors to generate out-performance: Value, Momentum, Quality and Low Volatility. The advantage of this approach is that instead of falling victim to the same emotions or trends, it actually capitalises on taking advantage of the systematic behavioural biases of investors to out-perform the S&P 500 index. And because it relies on the diversification of the sources of information behind these four factors, it manages to generate ‘all-weather’ out-performance in the medium to long-term. As the performance attribution below for our DEFI strategy applied to the S&P 500 index illustrates, a balanced approach allocating a quarter of the active risk budget (tracking error risk) to each of those four factors not only crossed unharmed the surprises of 2016, but actually took advantage of the important mood shifts in the US market during that period.
Investments in the aforementioned fund are subject to market fluctuation and risks inherent in investing in securities. The value of investments and the revenue they generate can increase or decrease and it is possible that investors will not recover their initial investment. Source: BNP Paribas Asset Management Holding.