Remove human subjectivity and replace it with the objectivity of a computer programmed to identify factors determining share price performance.
It is an approach to active management that has come to be known by the awe-inspiring name of quantitative (quant) analysis.
"Quant analysis gives you a disciplined and structured approach to analysing a market," says Grant Irvine-Smith, manager of the Investec Active Quants Fund.
In the US in particular, a growing number of management firms are using quant strategies to revive the flagging popularity of actively managed funds.
Only about 20% of US active funds beat their index benchmarks.
For the world’s biggest asset manager, BlackRock, which boasts assets under management of US$4.3trillion, it is vital to get and understand big data.
"The data explosion is a great opportunity for asset managers," says Ronald Kahn, BlackRock’s global head of scientific investment research.
BlackRock is throwing big money at big data.
"We hire a lot of maths, stats and data science graduates," says Kahn. "We compete with the likes of Google and Facebook for staff."
This has given BlackRock the ability to analyse vast quantities of data. It scours 6,000 broker reports daily, for example, to identify changes in analysts’ sentiment.
Taking data mining even further, the asset manager’s sources include satellite images of big retailer parking lots and online search terms as lead indicators of consumer spending trends.
But is this giving BlackRock the edge it seeks?
That’s not clear. In the 2015 ranking of US fund families published by Barron’s, BlackRock came in 18th overall over one year and 28th and 34th in the US and world equity categories, respectively. Over five years it ranked 22nd overall and over 10 years 17th.
SA quant managers are sticking to a more conventional approach, with variations.
"Our model looks at the factors driving the market leaders," says Warren McLeod, co-manager of Old Mutual Managed Alpha Equity Fund. "If it is profitability, for example, we will tend to be overweight in high profitability shares."
But the human factor in decision making is not ignored.
"We do not go overweight a share just because it is a potential winner," says McLeod. "We also manage portfolio risk."
In similar vein, Irvine-Smith says he "tilts" his portfolio towards shares with characteristics currently assessed by his model as driving market leaders. Characteristics could include value or quality. When analysts revise earnings for the better, that’s also taken into account.
"We use price momentum as confirmation," says Irvine-Smith.
Ricco Friedrich of Denker Capital remains to be convinced about the value of quant strategies. "Active management is about understanding businesses and the business environment," he says. "No quant method can do that."
Quant models, argues Friedrich, are also "backward looking" and insensitive to potential "exogenous factors".
McLeod acknowledges that drawback. "Models rely on historical data, which makes it hard to get rotation timing right," he says, referring to the recent swing from industrials towards resource shares.
The proof of the pudding is in the eating. For any fund, that starts with how it performs relative to its benchmark.
For McLeod’s fund the benchmark is the JSE shareholders weighted index (Swix). The fund has matched the Swix closely over the past 10 years — so closely that it could be a tracker fund. But a rather expensive one, with a total expense ratio of 1.56%, compared with the Satrix Swix exchange traded fund’s 0.45%.
Irvine-Smith’s fund, also Swix benchmarked, has disappointed, falling short of the Swix over 10 years, five years, three years and one year.
There is arguably another indication of quant strategy shortcomings.
A quant fund has yet to feature among the winners of the Raging Bull awards, which "honour the managers of funds that consistently earn good returns for SA retail investors".