Emerging markets remain intriguing for institutional investors who want to increase portfolio diversification – particularly at a time of extreme market volatility and against a background of geopolitical issues. Quantitative investment strategies can help unlock specific opportunities, Arup Datta, a quant veteran at Mackenzie Investments, says.
Quantitative or systematic investing has blossomed in the digital age and is now far more familiar to investors than in the past. Less common, though, is the application of quantitative investing to emerging markets. But, according to Arup Datta, Head of the Global Quantitative Equity Team at Mackenzie Investments, emerging markets represent something of a “sweet spot” for quants.
In a recent webinar hosted by Funds Europe, Datta talks about how quant investing can be applied to emerging markets and how certain strengths of quant strategies compare with the more traditional approach of fundamental investing.
Mackenzie’s models show that alpha efficacy has been higher in emerging markets than developed markets for the last 20 years and is likely to be so for the next 20. Datta ascribes this to several factors.
“Emerging markets are less efficient than developed markets so a good active manager should be able to find alpha in such an environment. There are also fewer active managers in emerging markets so there is less competition to deliver alpha,” he says.
But why would active quantitative investors fare better than their active fundamental counterparts in emerging markets?
“There is a greater breadth and diversity of names in emerging markets and that is a key advantage for quants over fundamental investors because they have a broader, rather than deeper, knowledge of the stock universe,” says Datta. “Quants may also have proprietary risk and transaction-cost models, daily rebalancing and may be able to react quicker to the latest information.”
Datta says that he has always taken a core and balanced approach to the investment factors that are prevalent in the emerging markets. He adopts four ‘super factors’ - value, revision, quality and informed investor.
“How does a company look versus its peers? Are sell-side analysts advising a buy? We also look at the quality of management through quantitative means – how they allocate capital, for example.”
A sign of trust
Dividend payments also form an important investment factor, one that has greater importance in emerging markets than in developed markets because dividends foster trust in companies.
Historically, corporate management is generally more trusted in developed markets than in the emerging economies where transparency and regulation may be weaker in comparison. So Datta says that “once an EM company starts paying dividends, the trust in that company goes up because the cash flow is shared and for example, is not being used to finance a director’s new house”.
Dividends, therefore, have a vital role for Datta and his process uses three dividend factors. The dividend factors fall under his ‘value’ factor (which centres on the size of dividends – “the higher the better”, says Datta); in ‘quality’ (which looks at the past growth of dividend); and in ‘revision’ (which concentrates on sell-side analyst forecasts and whether dividends will go up or down).
Also, and crucially important, Datta highlights the market-impact cost and transaction costs of trading in emerging markets.
As there is less liquidity in most emerging market stock markets compared to developed markets, any large trade in a single stock could increase its price. This is something that should be factored into any investment decision. Using historical data, the Mackenzie quant system is able to predict the impact on trading and tactically trade to minimise any erosion of value due to moving prices.
Similarly, transaction costs are factored into any trade as availability of stocks can make transactions expensive.
Dealing with sceptics
Despite the contrast between quantitative and fundamental strategies, Datta believes the divide between growth versus value is more important. This is especially important in light of the current market volatility and consequent central bank action which some say favours fundamental rather than quantitative investors.
“In the current low interest rate environment, growth stocks get valued even higher at the expense of value stocks,” says Datta.
While quantitative trading has become more popular, there are still sceptics that Datta and other quants have to contend with. For example, in the wake of the 2008 financial crisis, quantitative hedge funds were criticised for ‘herding’ and with the coronavirus now wreaking havoc across global markets, is there a fear the same could happen again?
“I would argue there is more diversity now among fundamental quants, we are not as similar so there should be less concern about herding,” says Datta.
Another criticism is that trading algorithms are limited by what programmers can imagine and few would have factored in an event like coronavirus. Datta says that Mackenzie has a human overlay to all of its trades meaning that all trades are vetted by a portfolio manager. “In the last two to three weeks, as this virus has spread around the world, we have been very careful about adding airlines, cruise liners or hotels. This is where the human overlay helps in using our judgement to guide the models,” says Datta.
Quant investing can be seen as a tech and data ‘arms race’ and not all investors are able to fully understand the processes and methodologies. While Datta does not dispute the importance of technology and data, he contends that any good quant manager should be able to explain their uniqueness in an understandable way to an investor.
“I don’t come from a school of thought where there is a black box that investors have to trust which will deliver alpha. Each well-known quant manager has to explain in layman’s terms where their strengths lie.”
Datta’s team does utilize new technology and alternative data sources to enhance their quant trading models. For example, they’re applying natural language processing to analysts’ briefings and earnings forecasts to detect the extent of negative or positive sentiment.
There is also research around the use of machine learning and neural networks, although Datta is more hesitant in this area due to concerns about transparency. “As you move to more non-linear areas, the model becomes more opaque. If it is not somewhat transparent, I would not be willing to adopt it.”
A couple of polls conducted during the webinar showed that 60% of respondents already use quantitative strategies in their active equity allocations while 100% of respondents said that emerging market equities will become a bigger part of their portfolios.
This suggests there is still some work to be done in order to convince investors of the value of quantitative strategies, but Datta is undaunted by the challenge.
“You learn by experience and I have spent many years talking to institutions and pension plans that know all about investing but not about quantitative strategies.
“You should always be able to bring it down to a level that an investor can understand. I am very happy to meet any sceptic and try and convert them because I have done it many times. Ultimately it comes down to transparency.”
*The webinar - Opportunities in Emerging Markets Today, a Sweet Spot for Quants - can be listened to here.
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