Quantamental investing was just starting to catch on when coronavirus came and broke all the models. The approach, which seeks to combine fundamental analysis of individual stocks with quantitative factor investing assessments, is capable of generating new ones.
Low-cost passive tracking contributed to the advance of quantamental as active managers seek to reduce portfolio risk and justify their fees. The growth of Big Data and machine learning has given them new tools with which to do so. Morningstar has estimated that factors such as valuation, growth, quality and momentum, drive about 65% of a global equity manager’s relative returns.
In a world that can be taken over by germs from a bat, the need to reduce portfolio risk has never been greater. Some industries have simply ceased functioning overnight for unknown durations. “A pandemic like this is going to affect the models, which place a low probability on such an occurence,” says Jason Ader, CEO of SpringOwl Asset Management in New York.
Big data and machine learning, he adds, are good ways to analyse patterns – until the patterns collapse. “The tools are only as good as you program them to be. Moments like this break every pattern.” SpringOwl’s response to coronavirus so far has been to make sure that portfolio companies are managing their liquidity, remaining within bank covenants, and avoiding unnecessary drains such as share buybacks. “Extreme value” in the energy, hotels and airline sectors is now available, Ader said. He confirmed he had seen some similarities with the 9/11 terrorist attack in the US, after which travel and tourism soon recovered. Ader spoke immediately before going into a SpringOwl investment committee meeting. The due diligence required by the committee means that any new investments will take months to approve, he said. The firm keeps its investments private, and will try to avoid going above 5% in a listed stock to avoid disclosure requirements.
SpringOwl’s value-driven approach seeks to understand what the market is saying about the cheapest stocks, Ader says. The firm avoids investing in highly leveraged companies. “We look for nuggets in the rubble. We’re not chasing momentum.”
“Quantamental investing is a relatively recent labelling of a decision-making process that has always existed among discretionary fund managers,” says Max Galland, former special situations equities trader at Deutsche Bank and managing director of Succinct Information in London.
The approach allows for the “effective screening of a large universe of stocks to remove noise” and will “certainly add value at a pre-trade stage,” he says. “Defining the leverage and concentration levels of a fund, placing hard stop losses and following them with discipline, applies to a discretionary strategy or the algorithms designed for a systematic fund.”
According to Morgan Stanley, between 2007 and 2016, equity funds outperformed the average investor in the same funds by 6%-12%. That is attributed to emotional overreactions on the part of individual investors, who put money in or pull it out on the basis of short-term recent performance. “Individual investors should focus on the basics of risk management while ignoring the latest investment fad,” Galland says.
Melissa Brown is managing director of applied research at Qontigo in New York. She has worked as both a buy-side and a sell-side quantitative analyst, or “quant”. Now she runs a supplying indices and portfolio stress tests.
Ten years ago, she says, many fundamental managers did not see the need for quants, yet they are now increasingly embracing quant techniques. Increasingly common masters’ programmes in financial engineering have contributed to the quant advance, she says. She expects this process to accelerate as old school fund managers retire.
“Most fundamental managers are not good at understanding relationships between stocks, or their volatility,” she says. Even “slight tweaks” to a portfolio, which leave its basic nature unchanged, can dramatically improve performance by reducing risk. Such an exercise, she argues, is also helpful in enabling fund managers to answer client questions about where in the portfolio the risk lies.
The increased popularity of low-cost passive index tracking creates an opportunity for quantamental investing to add value, she says. A few stocks in a portfolio may drive all the volatility, which is a situation many managers will want to avoid, she argues. Quantification, therefore, can provide a framework for diversification as well as improving portfolio attribution, or demonstrating where portfolio returns came from, and what risks were taken to get them. “You need to prove that you did what you said you were going to,” Brown says.
A study by Chris Martin, director of equity solutions Axioma in San Francisco, argues that a fundamental manager’s convictions often drive the weighting of each security they purchase. This can lead to emotional bias towards certain stocks. Even a simple factor risk model gives a predicted standard deviation of returns and divides risks into systematic and security-specific components. Axioma’s factor risk model includes several different factor blocks, including style, industry, country and currency factors, to help managers understand the risks inherent in a portfolio.
The aim of such an optimisation tool is to analyse every possible combination of stock weights from a given buy list and identify the one that minimises risks while keeping higher weights in high conviction stocks. The user does not need to understand all the maths behind it, but just the act that it can evaluate millions of possible asset combinations.
Pension fund trustees, Brown argues, are much more likely to retain a fund manager who may not have knocked the ball out of the park in a given year, but has “shown self-awareness” and can demonstrate that portfolio risks were also limited. Such managers are “much more likely to hold onto the assets,” she says.
Many investment styles, by definition, would cease to work if everyone used them. Quantamental tools, however, can be used by managers with very different overall approaches, suggesting that it has considerable potential for wider adoption. Whether an investment strategy is predominantly based on qualitative, quantitative analysis or a mix, is independent of the key risk management decisions, Galland argues.
George Mussalli, chief investment officer at PanAgora Asset Management in Boston, Massachusetts, has been finding ways to combine fundamental and quantitative investing for 20 years. On the organisational level, he says this means breaking down barriers between those who focus on fundamentals and quants.
Many investment houses, he says, remain as either fundamental or quant shops with some input from the other discipline. But genuinely bridging the gap between the two, he says, means going further. PanAgora has sought to build a team with both fundamental and quantitative skills, some from non-traditional backgrounds. “Anyone on the team can come and talk to a fundamental analyst,” he says. “A lot of quant specialists couldn’t do that.” Blending the two approaches, Mussalli says, “reduces the breadth of the investable universe” as stocks that score poorly on either set of criteria are eliminated.
Passive investing has the power to move markets, and fundamental managers need to understand what it is doing to their portfolios, says Mark Aldoroty, head of prime services and collateral funding & trading at BNY Mellon’s Pershing in New York. Market forces may have prevented fundamental managers’ stock-picking skills from showing through: “the idea might have been right, but the trade moved against you.”
The development of quantamental investing in response has been a process of “evolution not revolution,” he says. The result is a “much more collaborative effort” between fund managers and quant specialists.
Widening the radar
The use of quants, Mussalli says, enables a “systematic risk-control process” which involves “capturing every piece of data for every stock.” Yet fundamental research remains key. Mussalli gives the example of the run-up to the financial crisis of 2008. A purely quantitative approach in the preceding years, he says, would have thrown up a range of banks with low PE ratios combined with high returns on equity.
At PanAgora, he says, “we saw that a huge piece was missing – the risks that the banks were taking to get the ROE.” He used a data vendor to obtain the information on the contracts that banks had filed with their insurers to get a fuller picture of the risks. The exercise, he says, “helped us to avoid the banks that went under in 2008.”
Today, Mussalli says, data vendors are “blasting every quant in the world” to try to sell their information. “Buy raw data and you will get the same signals as everyone else,” he says. Proactively digging deeper remains the key. “We try to put ourselves in the shoes of the industry” in which an investment is being considered. Using quantitative methods, he says, makes it possible to replicate fundamental analysis more frequently and across a wider breadth of companies.
The quantamental approach is “like a radar” for SpringOwl’s investment committee, Ader says. “It opens up the conversation but doesn’t make the decision for us.” Quantamental will be part of the committee’s toolkit in the new world: “We are value investors who use quantamental as a tool.”