Analysing consensus – An academic approach to QIS

Quantitative investing is an approach to asset allocation grounded in financial research and mathematical modelling. A successful strategy is one that is rooted in academic research of the fundamental drivers of investment returns and governed by immutable rules dictated by reason, not whimsy. 

It’s little surprise, therefore, that Edhec-Risk Institute (ERI) has become a premier resource for institutions looking to create or invest in quantitative investment strategies. As a leading academic think-tank in investment solutions, ERI provides research, outreach, education and practitioner partnerships oriented towards the broader application of innovative asset management models – and especially factor-based strategies. 

ERI only promotes factors for which broad academic consensus exists: value, momentum, size, low volatility, high profitability and low investment. The institute champions simple definitions of these factors and is wary of those that rely on data mining as opposed to the well-documented empirical research in academic studies on factor investing. 

Eric Shirbini, global research and investment solutions director at ERI, explains: “Our academics spend a long time analysing these factors, looking at data typically spanning some 40 to 80 years to see if they are priced or not. This analysis spans the period after a given risk factor has been discovered, so that we can see if it persists once practitioners start to invest.”  

ERI has constructed indexes that access all six factors as well as a multi-factor product for investors seeking diversification between sources of alternative risk premia. This allows asset managers to smooth out the peaks and troughs associated with individual factor exposures.

 

Index construction 

ERI has been painstaking in its approach to index design. First of all, simple and transparent criteria for identifying those securities rich in a given factor premium were established and then applied consistently within and between equity markets – meaning the same analysis is used on an equity regardless of whether it’s listed in Lima or London.

The selected equities are chosen within economically integrated regions rather than on a worldwide basis, however. This is to prevent exposing an index to geographical risks. Disparate interest rate regimes, currencies and economic cycles exist between regions, meaning if stock selection were based globally, certain countries would be over- or under-represented.    

“If you want to create a value factor index, you shouldn’t look at value stocks across the entire developed or developing world, because if you do that you may find your index overweighted Japan, as that is where a lot of cheap stocks can be found, whereas there are not many cheap stocks in the US,” says Shirbini.

“Our methodology prevents us taking big macroeconomic bets, which would be wrong as factors are microeconomic exposures,” he adds.

“Our academics spend a long time analysing these factors, looking at data typically spanning some 40 to 80 years to see if they are priced or not”

Eric Shirbini, ERI

Once factor-rich stocks have been identified, ERI’s methodology applies a stock diversification weighting to prevent the index tilting too much towards a small handful of securities. 

“Instead of relying on individual stock characteristics, we capture a wide group of stocks exhibiting a factor bias and diversify between them. The objective is to capture the factor performance of the group,” Shirbini explains. 

It is this two-layer approach that generates
the superior risk-adjusted returns investors
seek from factor strategies. Shirbini says an ERI index could deliver a Sharpe ratio between
60% and 80% higher than a corresponding
cap-weighted index over a period of 10 years or more. 

Although the lion’s share of this outperformance is provided by the factor exposure, around 25% to 40% can be traced to the high degree of diversification achieved between the chosen stocks, and between the factor index as a whole and its cap-weighted competitors. 

“Cap-weighted strategies are extremely concentrated. Our factor strategies stay away from taking too much risk on certain individual names. That is why we diversify,” says Shirbini.

 

Benchmarking

Not knowing how to gauge the performance of a factor strategy can frustrate a naive investor. Factor models promise much, but a manager that uses a deficient means of benchmarking their chosen strategy will receive a flawed assessment of their true performance.  

Shirbini says the appropriate method of comparison will depend on the role a factor strategy plays within a manager’s broader portfolio.

“If you are replacing a cap-weighted index with a factor index, it doesn’t make sense to measure the performance of the latter relative to the former, because the purpose of this allocation shift is to create a portfolio with lower risk than a market exposure but one that obtains higher returns. The more appropriate measure is absolute risk-adjusted return,” he says.

“But if you are looking to use a factor portfolio on a relative return basis, versus the market, it’s important to adjust the benchmark to reflect the different market beta of that factor exposure. There’s no point benchmarking to the market without an adjustment, as with these strategies your exposure to the market is changing all the time,” he adds.

 

Isolating biases

Market beta bias is one of three ERI identifies as having ‘implicit risks’ hidden in factor strategies. Another is geographic bias, as previously described, and the third is sector bias.

Market beta bias concerns the extent to which a factor strategy’s performance is contingent on the equity market risk factor. A zero-correlated alternative risk premia strategy would exhibit no market beta bias, for example. But all long-only factor indexes will see their returns governed in part by broader market movements.

Shirbini says it is important investors understand the role of market beta bias and adjust their allocations, or benchmark, as appropriate.

“Research suggests that, when you invest in a factor, your market beta changes. If you have a value exposure, your market beta would be completely different than if you had a momentum exposure. It’s important to recognise this, as that market beta will still be an important driver of a strategy’s performance,” he explains.

ERI offers an index overlay that controls for market beta, allowing investors to maintain a constant exposure to the market risk factor within their chosen factor strategy.

Sector bias refers to a factor strategy’s propensity to certain over- or underweight industry segments in its allocation. This represents an unrewarded risk, as a concentrated sector investment can lead performance to be influenced by that sector, rather than the chosen factor.

A value index, for example, may contain a large number of insurance or bank company stocks – far more than in the corresponding cap-weighted index. This would make the strategy overweight financials.

An investor may want to adjust the portfolio to pare back this sector risk. However, applying such a filter could significantly reduce the very exposures that generate the factor-based returns, undermining the efficacy of the strategy.

“We wouldn’t recommend adjusting for sectors, because there is a price to pay in terms of long-term performance, unless the investor is concerned about tracking error,” says Shirbini.  

However, this decision – as with all those regarding implicit risks – is in the hands of the investor alone, and depends on their desired outcomes, says Shirbini.

 

The myth of crowding

ERI does not need to put faith in factor investing. It has the empirical data and economic rationale to prove these strategies’ utility, and the institute is keen to debunk certain fallacies that have arisen around them.

One argument peddled by factor sceptics is that the returns associated with value, momentum, low-volatility premia and the like will evaporate as investors pile into the equities that exhibit them. 

Take, for example, value stocks: these are securities considered cheap relative to their price-to-book ratios. However, as managers increasingly buy up such stocks, as directed by value strategies, their prices will rise, reducing or even eliminating the initial value premia.

Shirbini has little time for this supposed flaw in factor investing. “Crowding applies to alpha, as this is a transient phenomena, which, once uncovered, will disappear. Factors are different. They are specific, rewarded risks. You can have a factor that performs well for long periods of time but then the risk they hold materialises, resulting in drawdowns. Then all the people who jumped on the bandwagon when it was doing well will jump off again, but the factor premia will still be there for the long-term investor who rides out the cycle,” he says. 

Another misapprehension is that factors can be ‘timed’ – that is, an investor can efficiently rotate between different factor exposures over a given time horizon to maximise their returns and minimise their risks. 

Shirbini says this is wishful thinking. “You cannot time factors. They have their own cycles and they cannot be forecast. This is why the best solution is to hold a diversified portfolio across and between factors. Such a portfolio is very difficult to beat.”

 

Quantitative Investment: Uncovered – Special report 2019
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