Smart beta, on the other hand, is an approach to investing whereby the weighting of securities in traditional indices is adjusted to improve their risk-return profile.

Factor Investing is an advanced version of smart beta, where the primary goal is to revise the weighting of securities in traditional indices to achieve exposure to specific factors that can improve the portfolio’s risk-return profile even more.

These two subjects may seem different, in this post I am going to pinpoint the investment philosophy that is common to both of them (provided that smart beta is not just being employed as a marketing ploy).

Psychologists have conducted considerable research on the importance of stereotypes in influencing how we judge people: we tend to have an instinctive preferance for well-dressed, handsome people who look like ourselves. The same goes for words, which bear their own reputations.

In asset management, the word ‘quantitative’ arguably has a rather poor reputation, probably on account of the complexity of the mathematics employed in quantitative investment strategies. Some investors, with mathematical backgrounds love the term, most dislike it.

Indexing, on the other hand, has rather a good ring to it being associated with transparency and thrift, while fundamental portfolio management, based on a judgmental assessment of the fundamentals of a business or an economy perhaps satisifies our need for a ‘rational’ explanation of how investment decisions are made.

It is a fact that most investors find it easy to deal with the cost-conscious, common-sense investments of fundamental managers rather than with what is often thought of as the highly complex mathematical modelling behind quantitative funds.

Given these differences in perception, it is logical that marketing teams opted for the term ‘smart beta’ – which is a mix of ‘beta’ for indexing and ‘smart’ for judgmental-fundamental – over the term ‘quant’ for the development of quantitative strategies post-2007.

There is however really not much difference between smart beta and quant: they both rely on academic research defining systematic ways of investing designed to outperform, on average, the market cap. benchmark, or at least to generate higher risk-adjusted returns. The use of portfolio optimisers or risk models, as opposed to simple stock screening, is no longer a distinction as a growing number of smart beta strategies use those modelling techniques.

The aversion to quantitative strategies among some investors sometimes leads to incoherent thinking. For instance, the saying goes that ‘back-testing is bad’ (because the choice of back-test can be contrived to produce apparently good results) while ‘live performance is good’ (because it is real).

However, if you choose the strongest of 20 live performances over the last five years, you are still doing a form of back-test optimisation on a relatively short and recent data sample. You might end up with simply the luckiest of the 20 fund managers, even if his or her investment process was flawed.

What’s worse is that at present the last five years have been essentially bullish: the resulting analysis will very likely favour the most bullish of those 20 managers. A beta-neutral back-test over the last 20 years is probably much less biased, at least because it includes bear market years like 2008.

Exhibit 1: Factor investing – where it sits relative to the different approaches to managing portfolios

Source: BNP Paribas Asset Management, as of 01/09/2017

What really draws a line between ‘good’ and ‘bad’ quant, smart beta or factor investing is the ‘quantamental’ approach to the investment philosophy: Smart beta and, even more explicitly, factor investing, are not looking for returns per se, instead they are looking for systematic risks in the right kind of factors – in other words, those that are expected to be useful representations of the characteristics of those stocks with high rather than low expected returns, i.e. value, quality, momentum and low-risk.

Such factors are remunerated over the long term. But that is also the concept behind fundamental portfolio management: seeking out those companies that have good fundamentals, companies that are trading at cheap valuations relative to the level of cash flow or earnings they can generate, or to their book value (value); companies with the most competitive business economics and with the most competent company management teams (quality), companies that are bought at the best time, typically when price dynamics turn in favour of the company (momentum); or companies that do not generate too much uncertainty in the NAV of the portfolio (low risk).

Using quantitatively identifiable risks based on a distinct selection of fundamental factors, or indicators, is what makes factor investing ‘quantamental’. If this intention is diligently adhered to throughout the process, there is no reason for factor investing to go horribly wrong because there are no mysterious hidden risks and the choice of securities can be explained just as well as when securities are selected by a human fund manager.

‘Quantamental’ can also be seen as the combination of quant techniques with judgmental management, meaning that it is a human fund manager who makes the final decision. Actually, it does not mean that a human fund manager should have the last say when he disagrees with the model, because this would be bringing back emotional bias into the investment process. It just means that some investment decisions are better made by human fund managers and others are better handled by systematic strategies. A simple way to decide between the two is to look at the number of occurrences (see ‘Sample Size Matters‘): when a given decision is taken often or applies to many securities, then the law of large numbers is respected and statistics do a better job than humans.

However, when the event is rare or applies to too few securities, humans may do a better job. For instance, when it comes to the question of timing factors such as value, quality, momentum or low risk, i.e. trying to identify the few occasions when these factors fail to be useful representations of which stocks have high versus low expected returns. Something, which in the past, has occurred only on a very few occasions.

It is certainly not something that an algorithm could handle efficiently. Indeed humans may do it better. On the other hand, choosing the value stocks among a portfolio of 1 600 global equities is better done by an algorithm.

The main advantage of this ‘quantamental’ approach is to propose a combination of the strengths of quantitative and judgmental/fundamental analysis – it’s a way of overcoming the opposition between man and machine in order to improve both.